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Journal articles on the topic 'Robotics-inspired algorithms'
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Mir, Imran, Faiza Gul, Suleman Mir, Laith Abualigah, Raed Abu Zitar, Abdelazim G. Hussien, Emad Mahrous Awwad, and Mohamed Sharaf. "Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective." Biomimetics 8, no. 3 (July 7, 2023): 294. http://dx.doi.org/10.3390/biomimetics8030294.
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This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.
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Gutiérrez, Álvaro. "Recent Advances in Swarm Robotics Coordination: Communication and Memory Challenges." Applied Sciences 12, no. 21 (November 2, 2022): 11116. http://dx.doi.org/10.3390/app122111116.
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Du, Fengze. "Research of Bio-Inspired Motion Control in Robotics." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 378–84. http://dx.doi.org/10.62051/ay9zws79.
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Bio-inspired motion control in robotics draws inspiration from biological systems to enhance the movement capabilities of robots. This article explores the integration of bionics techniques in robots’ path planning, motion control, and design of moving parts, offering advantages over traditional robots control systems. In path planning, bio-inspired approaches, such as swarm intelligence algorithms and artificial neural networks, optimize trajectories and enable obstacle avoid ability in complex environments. Furthermore, bio-inspired design principles facilitate the creation of motion components tailored for specific locomotion modes, such as legged locomotion and aquatic propulsion, improving robots’ agility and adaptability. Reflex-based and vision-based control methods emulate biological responses and utilize visual sensors to enhance robots’ perception and responsiveness. Additionally, recent advancements include the exploration of Long Short-Term Memory Networks (LSTM) for predicting control inputs based on animal trajectories. Through a synthesis of biomechanical principles, materials science, and artificial intelligence integration, bio-inspired motion control revolutionizes robotic capabilities, with implications for autonomous navigation and task execution in dynamic environments. Future research directions include further investigation into biomechanical principles, advancements in materials science, and the integration of artificial intelligence algorithms for enhanced autonomy and adaptability in robotics.
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Latif, Rachid, Kaoutar Dahmane, Monir Amraoui, Amine Saddik, and Abdelouahed Elouardi. "Evaluation of Bio-inspired SLAM algorithm based on a Heterogeneous System CPU-GPU." E3S Web of Conferences 229 (2021): 01023. http://dx.doi.org/10.1051/e3sconf/202122901023.
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Localization and mapping are a real problem in robotics which has led the robotics community to propose solutions for this problem... Among the competitive axes of mobile robotics there is the autonomous navigation based on simultaneous localization and mapping (SLAM) algorithms: in order to have the capacity to track the localization and the cartography of robots, that give the machines the power to move in an autonomous environment. In this work we propose an implementation of the bio-inspired SLAM algorithm RatSLAM based on a heterogeneous system type CPU-GPU. The evaluation of the algorithm showed that with C/C++ we have an executing time of 170.611 ms with a processing of 5 frames/s and for the implementation on a heterogeneous system we used CUDA as language with an execution time of 160.43 ms.
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Verma, Jyotsna, and Nishtha Kesswani. "A Review on Bio-Inspired Migration Optimization Techniques." International Journal of Business Data Communications and Networking 11, no. 1 (January 2015): 24–35. http://dx.doi.org/10.4018/ijbdcn.2015010103.
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Nature inspired computing techniques has become a very popular topic in recent years. Number of applications in computer networks, robotics, biology, combinatorial optimization, etc. can be seen in literatures which are based on the bio-inspired techniques. Nature inspired techniques are proven to solve complex optimization problems irrespective of their problem size. This review summarizes various nature inspired migration algorithms and comparison between them, based on the automated tools, evolutionary techniques and applications.
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Kumar, Suresh, and Patricia Sha. "Human Brain inspired Artificial Intelligence & Developmental Robotics: A Review." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (June 30, 2017): 43. http://dx.doi.org/10.30537/sjcms.v1i1.6.
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Along with the developments in the field of the robotics, fascinating contributions and developments can be seen in the field of Artificial intelligence (AI). In this paper we will discuss about the developments is the field of artificial intelligence focusing learning algorithms inspired from the field of Biology, particularly large scale brain simulations, and developmental Psychology. We will focus on the emergence of the Developmental robotics and its significance in the field of AI.
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Muhsen, Dena Kadhim, Ahmed T. Sadiq, and Firas Abdulrazzaq Raheem. "A Survey on Swarm Robotics for Area Coverage Problem." Algorithms 17, no. 1 (December 20, 2023): 3. http://dx.doi.org/10.3390/a17010003.
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The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.
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Tiago Sant’Anna and Lucas Silva. "Biology-Inspired Innovations in Soft Robotics for Efficient Locomotion." JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH 7, no. 2 (July 20, 2024): 218–20. http://dx.doi.org/10.34178/jbth.v7i2.400.
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Inspired by nature, soft robotics promises to overcome traditional robots' limitations by using the flexibility and adaptability of living organisms to navigate complex environments. This field aims to replicate natural movements, such as the peristaltic motion of earthworms, applying them to robots to enhance locomotion and manipulation capabilities. Research focuses on developing prototypes inspired by biological mechanisms, with significant advances in design, actuation, and control, highlighting applications in challenging environments. Studies include the development of mobile robots with pneumatic actuation and models that mimic earthworm locomotion and exploring the use of friction for efficient movement. Soft robotics points to a future with more adaptable and efficient robots, promising innovations in inspection, exploration, and medicine, thanks to integrating new materials, actuators, and control algorithms.
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Gurko, Alexander, and Volodymyr Hurko. "Bio-inspired methods for planning the path of mobile robots." Bulletin of Kharkov National Automobile and Highway University, no. 98 (November 29, 2022): 37. http://dx.doi.org/10.30977/bul.2219-5548.2022.98.0.37.
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Problem. The issue of path planning for a mobile robot is one of the most important ones of mobile robotics. Proper path planning ensures the safety of the robot and its environment, the efficiency of the tasks carried out by a robot, saves time and energy consumption for these tasks, etc. Therefore, research is constantly conducted on the implementation of new and improving existing optimization methods for the path planning for a mobile robot. The utilization of classical optimization methods is limited by their significant drawbacks, such as computational complexity and long time for searching the optimal path. To eliminate these issues, heuristic and then metaheuristic methods have been developed. Among metaheuristic methods, bio-inspired optimization methods, which are based on evolutionary processes in nature, as well as the behaviour of living organisms, are becoming increasingly popular. Goal. This paper aims to analyse the most popular bio-inspired algorithms used for mobile robot path planning. Methodology. The paper briefly reviews the bio-inspired optimization methods that are applicable to the path planning of a mobile robot. Particular emphasis is given to swarm intelligence algorithms, in which the relatively simple behaviour of individual agents interacting with each other and with the environment allows a swarm of these agents to achieve a given goal. Results. A classification of bio-inspired optimization methods used for mobile robot path planning is proposed. Pseudocodes for swarm optimization algorithms that are most frequently applied in mobile robotics are presented. Originality. This paper is one of the first in Ukraine to offer a comprehensive overview of bio-inspired methods of optimization used for mobile robot path planning. Practical value. The implementation of the considered algorithms in mobile robot control systems will improve the efficiency of robots in performing their assigned tasks. The given pseudocodes will simplify the development of software to implement the mentioned algorithms.
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Türkler, Levent, Taner Akkan, and Lütfiye Özlem Akkan. "Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems." Sensors 22, no. 12 (June 11, 2022): 4437. http://dx.doi.org/10.3390/s22124437.
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In this study, the general structure of swarm robotics is examined. Algorithms inspired by nature, which form the basis of swarm robotics, are introduced. Communication topologies in robotic swarms, which are similar to the communication methods between living things moving in nature, are included and how these can be used in swarm communication is emphasized. With the developed algorithms, how the swarm can imitate nature and what tasks it can perform have been explained. The various problems that will be encountered in terms of the design of the optimization methods used during the control of the swarm and the solutions are simulated using the Webots software. As a result, ideas on the solutions of these problems and suggestions are proposed.
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Bhagat, Sarthak, Hritwick Banerjee, Zion Ho Tse, and Hongliang Ren. "Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges." Robotics 8, no. 1 (January 18, 2019): 4. http://dx.doi.org/10.3390/robotics8010004.
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The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.
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Chen, Junchi. "Algorithmic implementation and optimisation of path planning." Applied and Computational Engineering 33, no. 1 (January 22, 2024): 176–84. http://dx.doi.org/10.54254/2755-2721/33/20230263.
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Path planning algorithms are widely used in the fields of robotics and robotic arms, unmanned devices, automatic navigation, etc., and are an important technical basis for promoting the development of automation as well as the popularisation of artificial intelligence technology. This paper will briefly introduce various path planning algorithms implemented by mathematical models or inspired by biological features or genetics from the aspects of geometric search algorithms, intelligent search algorithms, artificial intelligence algorithms, and hybrid algorithms, including the characteristics, advantages and disadvantages, and important improvements of typical path planning algorithms as well as hybrid algorithms which are made by imitating and improving each other of several algorithms. Meanwhile, the development trend of path planning algorithms is also summarised, and the outlook of its development is made to provide reference for related fields.
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Devi, Kskn Venkata Ramana, Smitha B S, Sorabh Lakhanpal, Ravi Kalra, Vandana Arora Sethi, and Sadiq Khader Thajil. "A review: Swarm Robotics: Cooperative Control in Multi-Agent Systems." E3S Web of Conferences 505 (2024): 03013. http://dx.doi.org/10.1051/e3sconf/202450503013.
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Swarm robotics epitomizes a frontier in cooperative control within multi-agent systems, where the emulation of biological swarms offers a paradigm shift in robotics. This paper delves into the mechanisms of decentralized decision-making and the emergent behaviors that arise from local interactions among autonomous robotic agents without the need for a central controller. It explores the synthesis of simple control rules that yield complex, adaptive, and scalable group behaviors, akin to those found in natural swarms. A critical examination of communication protocols elucidates how information-sharing among agents leads to the robust execution of collective tasks. The research further investigates the dynamics of role allocation, task partitioning, and redundancy, which are crucial for the resilience of swarm robotic systems. Through simulation and empirical analysis, the efficacy of swarm algorithms in various applications, including search and rescue, environmental monitoring, and collective construction, is demonstrated. The study's findings underscore the significance of bio-inspired algorithms and the potential of swarm robotic systems to adapt and thrive in unpredictable environments. The implications for the future of autonomous systems are profound, as swarm robotics paves the way for innovations in distributed artificial intelligence and robotic.
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Valdez, Fevrier, Oscar Castillo, Amita Jain, and Dipak K. Jana. "Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real-World Control and Robotics Applications." Computational Intelligence and Neuroscience 2019 (April 15, 2019): 1–2. http://dx.doi.org/10.1155/2019/9128451.
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Chang, C. K., C. Siagian, and L. Itti. "Hardware and software computing architecture for robotics applications of neuroscience-inspired vision and navigation algorithms." Journal of Vision 10, no. 7 (August 13, 2010): 1056. http://dx.doi.org/10.1167/10.7.1056.
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Sun, Boai, Weikun Li, Zhangyuan Wang, Yunpeng Zhu, Qu He, Xinyan Guan, Guangmin Dai, et al. "Recent Progress in Modeling and Control of Bio-Inspired Fish Robots." Journal of Marine Science and Engineering 10, no. 6 (June 2, 2022): 773. http://dx.doi.org/10.3390/jmse10060773.
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Compared with traditional underwater vehicles, bio-inspired fish robots have the advantages of high efficiency, high maneuverability, low noise, and minor fluid disturbance. Therefore, they have gained an increasing research interest, which has led to a great deal of remarkable progress theoretically and practically in recent years. In this review, we first highlight our enhanced scientific understanding of bio-inspired propulsion and sensing underwater and then present the research progress and performance characteristics of different bio-inspired robot fish, classified by the propulsion method. Like the natural fish species they imitate, different types of bionic fish have different morphological structures and distinctive hydrodynamic properties. In addition, we select two pioneering directions about soft robotic control and multi-phase robotics. The hybrid dynamic control of soft robotic systems combines the accuracy of model-based control and the efficiency of model-free control, and is considered the proper way to optimize the classical control model with the intersection of multiple machine learning algorithms. Multi-phase robots provide a broader scope of application compared to ordinary bionic robot fish, with the ability of operating in air or on land outside the fluid. By introducing recent progress in related fields, we summarize the advantages and challenges of soft robotic control and multi-phase robotics, guiding the further development of bionic aquatic robots.
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Zangana, Hewa Majeed, Zina Bibo Sallow, Mohammed Hazim Alkawaz, and Marwan Omar. "Unveiling the Collective Wisdom: A Review of Swarm Intelligence in Problem Solving and Optimization." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 9, no. 2 (May 10, 2024): 101–10. http://dx.doi.org/10.25139/inform.v9i2.7934.
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Swarm intelligence, inspired by the collective behaviour of natural swarms and social insects, represents a powerful paradigm for solving complex optimization and decision-making problems. In this review paper, we provide an overview of swarm intelligence, covering its definition, principles, algorithms, applications, performance evaluation, challenges, and future directions. We discuss prominent swarm intelligence algorithms, such as ant colony optimization, particle swarm optimization, and artificial bee colony algorithm, highlighting their applications in optimization, robotics, data mining, telecommunications, and other domains. Furthermore, we examine the performance evaluation and comparative studies of swarm intelligence algorithms, emphasizing the importance of metrics, comparative analysis, and case studies in assessing algorithmic effectiveness and practical applicability. Challenges facing swarm intelligence research, such as scalability, robustness, and interpretability, are identified, and potential future directions for addressing these challenges and advancing the field are outlined. In conclusion, swarm intelligence offers a versatile and effective approach to solving a wide range of optimization and decision-making problems, with applications spanning diverse domains and industries. By addressing current challenges, exploring new research directions, and embracing interdisciplinary collaborations, swarm intelligence researchers can continue to innovate and develop cutting-edge algorithms with profound implications for science, engineering, and society.
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MALIM, MUHAMMAD ROZI, and FARIDAH ABDUL HALIM. "IMMUNOLOGY AND ARTIFICIAL IMMUNE SYSTEMS." International Journal on Artificial Intelligence Tools 21, no. 06 (December 2012): 1250031. http://dx.doi.org/10.1142/s0218213012500315.
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Artificial immune system is inspired by the natural immune system for solving computational problems. The immunological principles that are primarily used in artificial immune systems are the clonal selection principle, the immune network theory, and the negative selection mechanism. These principles have been applied in anomaly detection, pattern recognition, computer and network security, dynamic environments and learning, robotics, data analysis, optimization, scheduling, and timetabling. This paper describes how these three immunological principles were adapted by previous researchers in their artificial immune system models and algorithms. Finally, the applications of various artificial immune systems to various domains are summarized as a time-line.
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Olari, Viktoriya, Kostadin Cvejoski, and Øyvind Eide. "Introduction to Machine Learning with Robots and Playful Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 15630–39. http://dx.doi.org/10.1609/aaai.v35i17.17841.
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Inspired by explanations of machine learning concepts in children’s books, we developed an approach to introduce supervised, unsupervised, and reinforcement learning using a block-based programming language in combination with the benefits of educational robotics. Instead of using blocks as high-end APIs to access AI cloud services or to reproduce the machine learning algorithms, we use them as a means to put the student “in the algorithm’s shoes.” We adapt the training of neural networks, Q-learning, and k-means algorithms to a design and format suitable for children and equip the students with hands-on tools for playful experimentation. The children learn about direct supervision by modifying the weights in the neural networks and immediately observing the effects on the simulated robot. Following the ideas of constructionism, they experience how the algorithms and underlying machine learning concepts work in practice. We conducted and evaluated this approach with students in primary, middle, and high school. All the age groups perceived the topics to be very easy to moderately hard to grasp. Younger students experienced direct supervision as challenging, whereas they found Q-learning and k-means algorithms much more accessible. Most high-school students could cope with all the topics without particular difficulties.
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Sang, Ash Wan Yaw, Zhenyuan Yang, Lim Yi, Chee Gen Moo, Rajesh Elara Mohan, and Anh Vu Le. "Inter-Reconfigurable Robot Path Planner for Double-Pass Complete Coverage Problem." Mathematics 12, no. 6 (March 19, 2024): 902. http://dx.doi.org/10.3390/math12060902.
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Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics.
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Shigaki, Shunsuke, Mayu Yamada, Daisuke Kurabayashi, and Koh Hosoda. "Robust Moth-Inspired Algorithm for Odor Source Localization Using Multimodal Information." Sensors 23, no. 3 (January 28, 2023): 1475. http://dx.doi.org/10.3390/s23031475.
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Odor-source localization, by which one finds the source of an odor by detecting the odor itself, is an important ability to possess in order to search for leaking gases, explosives, and disaster survivors. Although many animals possess this ability, research on implementing olfaction in robotics is still developing. We developed a novel algorithm that enables a robot to localize an odor source indoors and outdoors by taking inspiration from the adult male silk moth, which we used as the target organism. We measured the female-localization behavior of the silk moth by using a virtual reality (VR) system to obtain the relationship between multiple sensory stimuli and behavior during the localization behavior. The results showed that there were two types of search active and inactive depending on the direction of odor and wind detection. In an active search, the silk moth moved faster as the odor-detection frequency increased, whereas in the inactive search, they always moved slower under all odor-detection frequencies. This phenomenon was constructed as a robust moth-inspired (RMI) algorithm and implemented on a ground-running robot. Experiments on odor-source localization in three environments with different degrees of environmental complexity showed that the RMI algorithm has the best localization performance among conventional moth-inspired algorithms. Analysis of the trajectories showed that the robot could move smoothly through the odor plume even when the environment became more complex. This indicates that switching and modulating behavior based on the direction of odor and wind detection contributes to the adaptability and robustness of odor-source localization.
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Jovanovic, Kosta, Jovana Vranic, and Nadica Miljkovic. "Hill’s and Huxley’s muscle models - tools for simulations in biomechanics." Serbian Journal of Electrical Engineering 12, no. 1 (2015): 53–67. http://dx.doi.org/10.2298/sjee1501053j.
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Numerous mathematical models of human skeletal muscles have been developed. However, none of them is adopted as a general one and each of them is suggested for some specific purpose. This topic is essential in humanoid robotics, since we firstly need to understand how human moves and acts in order to exploit human movement patterns in robotics and design human like actuators. Simulations in biomechanics are intensively used in research of locomotion, safe human-robot interaction, development of novel robotic actuators, biologically inspired control algorithms, etc. This paper presents two widely adopted muscle models (Hill?s and Huxley?s model), elaborates their features and demonstrates trade-off between their accuracy and efficiency of computer simulations. The simulation setup contains mathematical representation of passive muscle structures as well as mathematical model of an elastic tendon as a series elastic actuation element. Advanced robot control techniques point out energy consumption as one of the key issues. Therefore, energy store and release mechanism in elastic elements in both tendon and muscle, based on the simulation models, are considered.
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Luo, Yandong, Jianwen Guo, Zhenpeng Lao, Shaohui Zhang, and Xiaohui Yan. "Swarm Robot Exploration Strategy for Path Formation Tasks Inspired by Physarum polycephalum." Complexity 2021 (May 19, 2021): 1–17. http://dx.doi.org/10.1155/2021/6698421.
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Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.
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Srinivasan, Mandyam V. "Honeybees as a Model for the Study of Visually Guided Flight, Navigation, and Biologically Inspired Robotics." Physiological Reviews 91, no. 2 (April 2011): 413–60. http://dx.doi.org/10.1152/physrev.00005.2010.
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Research over the past century has revealed the impressive capacities of the honeybee, Apis mellifera , in relation to visual perception, flight guidance, navigation, and learning and memory. These observations, coupled with the relative ease with which these creatures can be trained, and the relative simplicity of their nervous systems, have made honeybees an attractive model in which to pursue general principles of sensorimotor function in a variety of contexts, many of which pertain not just to honeybees, but several other animal species, including humans. This review begins by describing the principles of visual guidance that underlie perception of the world in three dimensions, obstacle avoidance, control of flight speed, and orchestrating smooth landings. We then consider how navigation over long distances is accomplished, with particular reference to how bees use information from the celestial compass to determine their flight bearing, and information from the movement of the environment in their eyes to gauge how far they have flown. Finally, we illustrate how some of the principles gleaned from these studies are now being used to design novel, biologically inspired algorithms for the guidance of unmanned aerial vehicles.
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Diaf, Moussa, Kamal Hammouche, and Patrick Siarry. "From the Real Ant to the Artificial Ant." International Journal of Signs and Semiotic Systems 2, no. 2 (July 2012): 45–68. http://dx.doi.org/10.4018/ijsss.2012070103.
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Biological studies highlighting the collective behavior of ants in fulfilling various tasks by using their complex indirect communication process have constituted the starting point for many physical systems and various ant colony algorithms. Each ant colony is considered as a superorganism which operates as a unified entity made up of simple agents. These agents (ants) interact locally with one another and with their environment, particularly in finding the shortest path from the nest to food sources without any centralized control dictating the behavior of individual agents. It is this coordination mechanism that has inspired researchers to develop plenty of metaheuristic algorithms in order to find good solutions for NP-hard combinatorial optimization problems. In this article, the authors give a biological description of these fascinating insects and their complex indirect communication process. From this rich source of inspiration for researchers, the authors show how, through the real ant, artificial ant is modeled and applied in combinatorial optimization, data clustering, collective robotics, and image processing.
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Krontiris, Athanasios, Ryan Luna, and Kostas Bekris. "From Feasibility Tests to Path Planners for Multi-Agent Pathfinding." Proceedings of the International Symposium on Combinatorial Search 4, no. 1 (August 20, 2021): 114–22. http://dx.doi.org/10.1609/socs.v4i1.18289.
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Multi-agent pathfinding is an important challenge that relates to combinatorial search and has many applications, such as warehouse management, robotics and computer games. Finding an optimal solution is NP-hard and raises scalability issues for optimal solvers. Interestingly, however, it takes linear time to check the feasibility of an instance. These linear-time feasibility tests can be extended to provide path planners but to the best of the authors’ knowledge no such solver has been provided for general graphs. This work first describes a path planner that is inspired by a linear-time feasibility test for multi-agent pathfinding on general graphs. Initial experiments indicated reasonable scalability but worse path quality relative to existing suboptimal solutions. This led to the development of an algorithm that achieves both efficient running time and path quality relative to the alternatives and which finds a solution on available benchmarks. The paper outlines the relation of the final method to the feasibility tests and existing suboptimal planners. Experimental results evaluate the different algorithms, including an optimal solver.
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Ding, Zhiyuan. "Analysis of Mechanical Structure in Mobile Robots." Highlights in Science, Engineering and Technology 106 (July 16, 2024): 157–64. http://dx.doi.org/10.54097/rhm6we30.
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Robotics is a rapidly growing field that combines knowledge and skills from various areas, such as computer science, mechanical engineering, electrical engineering, and mathematics. These skills are used to create robots in different sectors, such as healthcare, manufacturing, and agriculture. This article underscores the interdisciplinary nature of robotics and its far-reaching impact, emphasizing the significance of structural design in addition to software algorithms by exploring the fundamental principles of designing mobile robots and how their mechanical structures could affect movement capabilities. Rather than focusing solely on specific structures, such as bio-inspired legged robots, the article aims to address this gap by examining several common principles of mobile robot motion and make comparisons between them horizontally to analyze the pros and cons of legged, wheeled, and inflatable biomimetic robots, elaborating on their unique characteristics and uses. The potential for hybridization and fusion of mechanical structures, exemplified by wheel-legged robots, has also been highlighted and discussed to propose future research directions in robot design and applications. In summary, this article will provide a comprehensive overview of the principles of mechanical structure in mobile robots, aiming to promote a deeper understanding of robot motion and inspire further innovation in the field.
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Urbanowicz, Ryan J., and Jason H. Moore. "Learning Classifier Systems: A Complete Introduction, Review, and Roadmap." Journal of Artificial Evolution and Applications 2009 (September 22, 2009): 1–25. http://dx.doi.org/10.1155/2009/736398.
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If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.
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Pham, Quang Anh, Hoong Chuin Lau, Minh Hoàng Hà, and Lam Vu. "An Efficient Hybrid Genetic Algorithm for the Quadratic Traveling Salesman Problem." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (July 1, 2023): 343–51. http://dx.doi.org/10.1609/icaps.v33i1.27212.
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The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which has been studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named the quadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics. The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leads to a quadratic objective function that is much harder to solve. To efficiently solve the problem, we propose a hybrid genetic algorithm including a local search procedure for intensification and a new mutation operator for diversification. The local search is composed of a restricted double-bridge move (a variant of 4-Opt); and we show the neighborhood can be evaluated in O(n^2), the same complexity as for the classical TSP. The mutation phase is inspired by a ruin-and-recreate scheme. Experimental results conducted on benchmark instances show that our method significantly outperforms state-of-the-art algorithms in terms of solution quality. Out of 800 considered instances, it finds 437 new best-known solutions.
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Juhászné Bíró, T., and P. Németh. "Innovative methods and research directions in the field of logistics." IOP Conference Series: Materials Science and Engineering 1237, no. 1 (May 1, 2022): 012011. http://dx.doi.org/10.1088/1757-899x/1237/1/012011.
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Abstract By the 21st century, logistics and various supply chains had become key units in the global market and corporate structures. Industry 4.0 has brought developments and implementations to life that have drastically changed and are still changing the practices used in certain areas of logistics. Many new technologies (advanced robotics, additive manufacturing, artificial intelligence (AI), blockchain, drones, Internet of Things (IoT)) have emerged in the digital world, which many companies are using to develop cyber-physical systems in order to increase efficiency, speed, accuracy and the ability to change and steer competition between companies around the world. Planning tasks at the strategic, tactical and operational levels are covered in the areas of production and logistics. The tasks presented here can be identified as extremely complex optimization problems that belong to the np-hard complexity class. These can be addressed in many cases with metaheuristics, and industry also often uses search strategies inspired by biological or physical processes. Metaheuristic algorithms simulate the behavior of a selected phenomenon in a given search area. Algorithms based on various principles can help optimize processes, such as: population-based algorithms, evolutionary methods, behavior-inspired procedures, swarm intelligence methods, etc. New technologies or metaheuristic procedures are also increasingly used in logistics due to the complexity of the tasks. This paper presents theoretical application possibilities of digital transformation, AI and IoT in the field of logistics. The paper provides a further brief overview of the problems surrounding metaheuristics, supported by examples. The article shows the impact of different Industry 4.0 technologies on logistics. There is a shortage of such comprehensive studies, so the article helps provide insight into innovative optimization opportunities in a larger area - the field of logistics. Within this one paper, the impact of new technologies on the field of logistics was collected. A brief description of these will help to identify further directions and deepen the applicability of the new methods in logistics.
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Pransky, Joanne. "The Pransky interview: Dr Maja Matarić, Professor, University of Southern California; Pioneer, field of socially assistive robotics; co-founder of Embodied." Industrial Robot: the international journal of robotics research and application 46, no. 3 (May 20, 2019): 332–36. http://dx.doi.org/10.1108/ir-04-2019-0069.
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Purpose The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD and innovator regarding her pioneering efforts and the challenges of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Maja Matarić, Chan Soon-Shiong Distinguished Professor in the Computer Science Department, Neuroscience Program, and the Department of Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (RASC), co-director of the USC Robotics Research Lab and Vice Dean for Research in the USC Viterbi School of Engineering. In this interview, Matarić shares her personal and business perspectives on socially assistive robotics. Findings Matarić received her PhD in Computer Science and Artificial Intelligence from MIT in 1994, MS in Computer Science from MIT in 1990 and BS in Computer Science from the University of Kansas in 1987. Inspired by the vast potential for affordable human-centered technologies, she went on to found and direct the Interaction Lab, initially at Brandeis University and then at the University of Southern California. Her lab works on developing human–robot non-physical interaction algorithms for supporting desirable behavior change; she has worked with a variety of beneficiary user populations, including children with autism, elderly with Alzheimer’s, stroke survivors and teens at risk for Type 2 diabetes, among others. Originality/value Matarić is a pioneer of the field of socially assistive robotics (SAR) with the goal of improving user health and wellness, communication, learning and autonomy. SAR uses interdisciplinary methods from computer science and engineering as well as cognitive science, social science and human studies evaluation, to endow robots with the ability to assist in mitigating critical societal problems that require sustained personalized support to supplement the efforts of parents, caregivers, clinicians and educators. Matarić is a Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the IEEE and AAAI, recipient of the Presidential Awards for Excellence in Science, Mathematics & Engineering Mentoring (PAESMEM), the Anita Borg Institute Women of Vision Award for Innovation, Okawa Foundation Award, NSF Career Award, the MIT TR35 Innovation Award, the IEEE Robotics and Automation Society Early Career Award and has received many other awards and honors. She was featured in the science documentary movie “Me & Isaac Newton”, in The New Yorker (“Robots that Care” by Jerome Groopman, 2009), Popular Science (“The New Face of Autism Therapy”, 2010), the IEEE Spectrum (“Caregiver Robots”, 2010), and is one of the LA Times Magazine 2010 Visionaries. Matarić is the author of a popular introductory robotics textbook, “The Robotics Primer” (MIT Press 2007), an associate editor of three major journals and has published extensively.
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Arshad, Jehangir, Adan Qaisar, Atta-Ur Rehman, Mustafa Shakir, Muhammad Kamran Nazir, Ateeq Ur Rehman, Elsayed Tag Eldin, Nivin A. Ghamry, and Habib Hamam. "Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence." Applied Sciences 12, no. 21 (October 25, 2022): 10813. http://dx.doi.org/10.3390/app122110813.
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The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics and automation industry by the deployment of intelligent systems and reducing human intervention. Reading human brain signal through electroencephalography (EEG) has provided a new direction of research that automate machines through the human brain and computer interface or Brain–Computer Interface (BCI). The study is also inspired by the same concept of intelligently controlling a robotic arm using BCI and AI to help physically disabled individuals. The proposed system is non-invasive, unlike existing technologies that provide a reliable comparison of different AI-based classification algorithms. This paper also predicts a reliable bandwidth for the BCI process and provides exact placements of EEG electrodes to verify different arm moments. We have applied different classification algorithms, i.e., Random Forest, KNN, Gradient Boosting, Logistic Regression, SVM, and Decision Tree, to four different users. The accuracy of all prescribed classifiers has been calculated by considering the first user as a reference. The presented results validate the novel deployment, and the comparison shows that the accuracy for Random Forest remained optimal at around 76%, Gradient Boosting is around 74%, while the lowest is 64% for Decision Tree. It has been observed that people have different activation bandwidths while the dominant frequency varies from person-to-person that causes fluctuations in the EEG dataset.
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Vorobyev, V. V., V. V. Karpov, and A. S. Nasedkin. "On one implementation of collective behavior in a group of underwater robots." Transactions of the Krylov State Research Centre S-I, no. 2 (December 21, 2021): 7–16. http://dx.doi.org/10.24937/2542-2324-2021-2-s-i-7-16.
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This paper discusses underwater robotic networks from the standpoint of stealthy surveillance by means of bio-inspired drones. “Bio-inspired” means that various hardware, software and technology solutions implemented in a robot have biological basis and rely on the studies in ethology and morphology of living organisms. In underwater robotics, this approach makes it possible to develop the vehicles that resemble sea life in terms of appearance and behavior and therefore are harder to detect for both animal and human observer, which facilitates the tasks of water area surveillance and fauna research observations. This work is meant to develop and refine a number of basic collective behavior patterns for this kind of robots, which is necessary to make robots as similar to the sea life in their operation area as possible to reduce their chances of being detected. Basic behavior algorithms for robots were developed as per the findings of ichthyological and ethological studies and also relying on certain points of the automata theory. A number of functions for the lower-level control systems were developed through simulation. The experiments were mostly performed in Robotic Test Tank of the Kurchatov Institute on a real shoal of underwater robots developed under this project. The results of this study made it possible to develop one of the basic patterns in shoaling behavior of robots, i.e. schooling after a non-established leader whose position is disputed. In real environment, this pattern was tested on three fish-like underwater robots with two-level control system. Another output of the study is a short-range infrared communication system for limited data exchange between drones. Experimental validation of this system and the pattern of schooling after a non-established leader implemented at the top level of robot control system have confirmed the viability of suggested solutions. This mechanisms, as well as technical and technological solutions yielded by this work will become the basis for further efforts towards development of a bio-inspired underwater robot. The algorithm of schooling after a non-established leader plays a key role in further improvement of collective behavior patterns for drones, like shoaling.
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Chen, Yujin, Ruizhi Chen, Mengyun Liu, Aoran Xiao, Dewen Wu, and Shuheng Zhao. "Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free." Sensors 18, no. 8 (August 16, 2018): 2692. http://dx.doi.org/10.3390/s18082692.
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Indoor localization is one of the fundamentals of location-based services (LBS) such as seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of robotics, etc. Visual features take up a dominant part of the information that helps human and robotics understand the environment, and many visual localization systems have been proposed. However, the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost. To better address this problem, a localization method based on image retrieval is proposed in this paper, which mainly consists of two parts. The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image. The second one is pose estimation phase that computes accurate localization result. Owing to the robust CNN feature extractor, our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments. The pose estimation scheme was inspired by monocular visual odometer, therefore, only RGB images and poses of reference images are needed for accurate image geo-localization. Furthermore, our method attempts to use lightweight datum to present the scene. To evaluate the performance, experiments are conducted, and the result demonstrates that our scheme can efficiently result in high location accuracy as well as orientation estimation. Currently the positioning accuracy and usability enhanced compared with similar solutions. Furthermore, our idea has a good application foreground, because the algorithms of data acquisition and pose estimation are compatible with the current state of data expansion.
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López-Muñoz, Raúl, Edgar A. Portilla-Flores, Leonel G. Corona-Ramírez, Eduardo Vega-Alvarado, and Mario C. Maya-Rodríguez. "Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics." Applied Sciences 13, no. 11 (May 27, 2023): 6543. http://dx.doi.org/10.3390/app13116543.
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The inverse kinematics problem (IKP) is fundamental in robotics, but it gets harder to solve as the complexity of the mechanisms increases. For that reason, several approaches have been applied to solve it, including metaheuristic algorithms. This work presents a proposal for solving the IKP of a doubly articulated kinematic chain by means of a modified differential evolution (DE) algorithm. The novelty of the proposal is both in the modeling of the problem and the modification to the DE for solving it. The modeling is inspired by a technique used in animation software to recreate movements by dividing the complete trajectory in a number of segments. Each segment represents a single optimization problem linked to the IKP as a sequence that is solved by the modified DE where the initial population for each single problem is biased by using the solution of the previous one. The approach produces solutions for positioning the end effector in a specific point within the work space while minimizing the angular displacement between the initial and final poses. The proposal was able to obtain solutions requiring a fewer total execution cycles compared to the usual approach of solving only one optimization problem related to the inverse kinematics. Different trajectories were used to test the solutions generated by the proposed approach, and the set of conditions that must be covered to apply it to solve the IKP of a particular mechanism are presented.
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Antonelli, Michele Gabrio, Pierluigi Beomonte Zobel, and Nicola Stampone. "Response Surface Methodology for Kinematic Design of Soft Pneumatic Joints: An Application to a Bio-Inspired Scorpion-Tail-Actuator." Machines 12, no. 7 (June 26, 2024): 439. http://dx.doi.org/10.3390/machines12070439.
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In soft robotics, the most used actuators are soft pneumatic actuators because of their simplicity, cost-effectiveness, and safety. However, pneumatic actuation is also disadvantageous because of the strong non-linearities associated with using a compressible fluid. The identification of analytical models is often complex, and finite element analyses are preferred to evaluate deformation and tension states, which are computationally onerous. Alternatively, artificial intelligence algorithms can be used to follow model-free and data-driven approaches to avoid modeling complexity. In this work, however, the response surface methodology was adopted to identify a predictive model of the bending angle for soft pneumatic joints through geometric and functional parameters. The factorial plan was scheduled based on the design of the experiment, minimizing the number of tests needed and saving materials and time. Finally, a bio-inspired application of the identified model is proposed by designing the soft joints and making an actuator that replicates the movements of the scorpion’s tail in the attack position. The model was validated with two external reinforcements to achieve the same final deformation at different feeding pressures. The average absolute errors between predicted and experimental bending angles for I and II reinforcement allowed the identified model to be verified.
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OLSON, BRIAN, KEVIN MOLLOY, S. FARID HENDI, and AMARDA SHEHU. "GUIDING PROBABILISTIC SEARCH OF THE PROTEIN CONFORMATIONAL SPACE WITH STRUCTURAL PROFILES." Journal of Bioinformatics and Computational Biology 10, no. 03 (June 2012): 1242005. http://dx.doi.org/10.1142/s021972001242005x.
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The roughness of the protein energy surface poses a significant challenge to search algorithms that seek to obtain a structural characterization of the native state. Recent research seeks to bias search toward near-native conformations through one-dimensional structural profiles of the protein native state. Here we investigate the effectiveness of such profiles in a structure prediction setting for proteins of various sizes and folds. We pursue two directions. We first investigate the contribution of structural profiles in comparison to or in conjunction with physics-based energy functions in providing an effective energy bias. We conduct this investigation in the context of Metropolis Monte Carlo with fragment-based assembly. Second, we explore the effectiveness of structural profiles in providing projection coordinates through which to organize the conformational space. We do so in the context of a robotics-inspired search framework proposed in our lab that employs projections of the conformational space to guide search. Our findings indicate that structural profiles are most effective in obtaining physically realistic near-native conformations when employed in conjunction with physics-based energy functions. Our findings also show that these profiles are very effective when employed instead as projection coordinates to guide probabilistic search toward undersampled regions of the conformational space.
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Sosa-Ceron, Arturo Daniel, Hugo Gustavo Gonzalez-Hernandez, and Jorge Antonio Reyes-Avendaño. "Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey." Robotics 11, no. 6 (November 15, 2022): 126. http://dx.doi.org/10.3390/robotics11060126.
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Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.
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Chen, Chao, Jiacheng Xu, Weijian Liao, Hao Ding, Zongzhang Zhang, Yang Yu, and Rui Zhao. "Focus-Then-Decide: Segmentation-Assisted Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (March 24, 2024): 11240–48. http://dx.doi.org/10.1609/aaai.v38i10.29002.
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Visual Reinforcement Learning (RL) is a promising approach to achieve human-like intelligence. However, it currently faces challenges in learning efficiently within noisy environments. In contrast, humans can quickly identify task-relevant objects in distraction-filled surroundings by applying previously acquired common knowledge. Recently, foundational models in natural language processing and computer vision have achieved remarkable successes, and the common knowledge within these models can significantly benefit downstream task training. Inspired by these achievements, we aim to incorporate common knowledge from foundational models into visual RL. We propose a novel Focus-Then-Decide (FTD) framework, allowing the agent to make decisions based solely on task-relevant objects. To achieve this, we introduce an attention mechanism to select task-relevant objects from the object set returned by a foundational segmentation model, and only use the task-relevant objects for the subsequent training of the decision module. Additionally, we specifically employed two generic self-supervised objectives to facilitate the rapid learning of this attention mechanism. Experimental results on challenging tasks based on DeepMind Control Suite and Franka Emika Robotics demonstrate that our method can quickly and accurately pinpoint objects of interest in noisy environments. Consequently, it achieves a significant performance improvement over current state-of-the-art algorithms. Project Page: https://www.lamda.nju.edu.cn/chenc/FTD.html Code: https://github.com/LAMDA-RL/FTD
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Borkar, Kailash Kumar, Turki Aljrees, Saroj Kumar Pandey, Ankit Kumar, Mukesh Kumar Singh, Anurag Sinha, Kamred Udham Singh, and Vandana Sharma. "Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review." Processes 11, no. 12 (November 26, 2023): 3302. http://dx.doi.org/10.3390/pr11123302.
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Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.
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Albani, Dario, Wolfgang Hönig, Daniele Nardi, Nora Ayanian, and Vito Trianni. "Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms." Applied Sciences 11, no. 7 (March 31, 2021): 3115. http://dx.doi.org/10.3390/app11073115.
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Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.
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Nobuhara, Hajime. "Selected Papers from SCIS & ISIS 2006 – No.2." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 7 (September 20, 2007): 727. http://dx.doi.org/10.20965/jaciii.2007.p0727.
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As mentioned in the editorial for the special issue on selected papers from SCIS & ISIS 2006 (No.1) by Dr. Naoyuki Kubota, the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) have been highly successful with 464 original papers accepted for presentation and participants numbering 526. We have selected approximately 50 quality papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics, following publication of the first volume (Vol.11, No.6) comprising 23 papers. This second volume of the SCIS & ISIS 2006 special issue includes 19 papers covering the cutting edge of computational intelligence, and the guest editors believe that readers will be inspired by the highly interesting contents containing clues to the new frontier of the computational intelligence. linebreak Related areas include image processing, control, sensor fusion, data/context/ network analysis, genetic algorithms, ontology, VHDL, game theory, and robotics among others.smallskip I would like to thank all the authors and reviewers for their valuable contributions in making this volume possible. I am also grateful to Editors-in-Chief Prof. Toshio Fukuda of Nagoya University and Prof. Kaoru Hirota of the Tokyo Institute of Technology, Dr. Toshiaki Murofushi who served as general chair of SICS & ISIS 2006, Dr. Naoyuki Kubota who was the SCIS & ISIS 2006 program chair, and the SCIS & ISIS 2006 conference staff for inviting me to serve as Guest Editor of this Journal. Last, I would like to thank the Fuji Technology Press staff, especially Dr. Kazuki Ohmori.
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O., KOLLAROV, and KARDASH D. "Application of the particle swarm method in optimization problems of energy." Journal of Electrical and power engineering 29, no. 2 (December 19, 2023): 50–54. http://dx.doi.org/10.31474/2074-2630-2023-2-50-54.
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The article considers the application of the particle swarm method in energy domain. The problem of effective load distribution of energy-generating capacities under the conditions of minimum fuel consumption is one of those that arises most often. The economic expediency of the operation of one or another power plant at the appropriate capacity determines the distribution of generating capacities in the power system. It is no secret that power units of thermal power plants, which were built in different time periods, differ in their cost characteristics. This makes it necessary to find optimal configurations of the power system, in which the relevant energy objects are involved. Particle Swarm Optimization (PSO) is a computational optimization method inspired by the social behavior of birds in a flock or fish in a shoal. This method was first proposed by Kennedy and Eberhart in 1995. In PSO, a population of possible solutions, called particles, moves through the search space according to a set of mathematical rules. The motion of each particle affects its own bestknown position and the global best-known position of the entire population. The basic idea is that each particle adjusts its position based on its own experience and that of the entire swarm. This correction takes place with the help of two main components: 1. Cognitive component (personal best result): The particle remembers the best solution it found before. 2. Social component (global best result): A particle also takes into account the best solution found by any other particle in the swarm. These components are used to update the particle's velocity and position iteratively, with the goal of converging to an optimal solution. PSO is widely used in various optimization problems, including engineering design, robotics, finance, and data analysis. It is known for its simplicity, ease of implementation, and ability to solve non-linear, non-convex optimization problems. However, like any optimization algorithm, its performance can be sensitive to the parameters and the nature of the problem to be solved. The article solves a typical problem of distributing the total load between two thermal power plants under the conditions of minimum fuel consumption. The obtained values of the solutions confirm commonly known the statements about the achievement of adequate indicators in the range from 10 to 30 particles, in our case - 20.Analyzing the obtained results, one can see that the objective function changes almost linearly from the very beginning until the 30th iteration, after which the improvement in the result is almost imperceptible. The main reason is that at this moment the result of the algorithm is as close as possible to the reference value, namely 250. That is, in fact, it can be considered that the solution comes at the 31st iteration. Carrying out a comparison with the solution of such a problem using the genetic algorithm from the previous work, it can be seen that when solving such a problem, the algorithms demonstrate similar performance with comparable accuracy of the result. From the above studies, it can be concluded that evolutionary algorithms can be used to solve similar energy problems.
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Kravchenko, Oleksandr, Anastasiia Varava, Florian T. Pokorny, Didier Devaurs, Lydia E. Kavraki, and Danica Kragic. "A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction." Journal of Chemical Information and Modeling 60, no. 3 (March 4, 2020): 1302–16. http://dx.doi.org/10.1021/acs.jcim.9b00945.
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Vijayan, Asha, Chaitanya Nutakki, Dhanush Kumar, Krishnashree Achuthan, Bipin Nair, and Shyam Diwakar. "Enabling a Freely Accessible Open Source Remotely Controlled Robotic Articulator with a Neuro-Inspired Control Algorithm." International Journal of Online Engineering (iJOE) 13, no. 01 (January 18, 2017): 61. http://dx.doi.org/10.3991/ijoe.v13i01.6288.
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Internet-enabled technologies for robotics education are gaining importance as online platforms promoting skill training. Understanding the use and design of robotics are now introduced at university undergraduate levels, but in developing economies establishing usable hardware and software platforms face several challenges like cost, equipment etc. Remote labs help providing alternatives to some of the challenges. We developed an online laboratory for bioinspired robotics using a low-cost 6 degree-of-freedom robotic articulator with a neuro-inspired controller. Cerebellum-inspired neural network algorithm approximates forward and inverse kinematics for movement coordination. With over 210000 registered users, the remote lab has been perceived as an interactive online learning tool and a practice platform. Direct feedback from 60 students and 100 university teachers indicated that the remote laboratory motivated self-organized learning and was useful as teaching material to aid robotics skill education.
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ŁOŚ, ALEKSANDRA, MAŁGORZATA BIEŃKOWSKA, and ANETA STRACHECKA. "Honey bee as an alternative model invertebrate organism." Medycyna Weterynaryjna 74, no. 10 (2025): 6140–2025. http://dx.doi.org/10.21521/mw.6140.
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Insects perfectly fit the flagship principle of animal research – 3R: to reduce (the number of animals), to replace (animals with alternative models) and to refine (methods). Bees have the most important advantages of a model organism: they cause minimal ethical controversy, they have a small and fully known genome, and they permit the use of many experimental techniques. Bees have a fully functional DNMT toolkit. Therefore, they are used as models in biomedical/genetic research, e.g. in research on the development of cancer or in the diagnostics of mental and neuroleptic diseases in humans. The reversion of aging processes in bees offers hope for progress in gerontology research. The cellular mechanisms of learning and memory coding, as well as the indicators of biochemical immunity parameters, are similar or analogous to those in humans, so bees may become useful in monitoring changes in behavior and metabolism. Bees are very well suited for studies on the dose of the substance applied to determine the lethal dose or the effect of a formula on life expectancy. Honeybees have proven to be an effective tool for studying the effects of a long-term consumption of stimulants, as well as for observing behavioral changes and developing addictions at the individual and social levels, as well as for investigating the effects of continuously delivering the same dose of a substance. The genomic and physiological flexibility of bees in dividing tasks among workers in a colony makes it possible to create a Single- Cohort Colony (SCC) in which peers compared perform different tasks. Moreover behavioral methods (e.g. Proboscis Extension Reflex – PER, Sting Extension Reflex – SER, free flying target discrimination tasks or the cap pushing response) make it possible to analyse changes occurring in honeybee brains during learning and remembering. Algorithms of actions are created based on the behavior of a colony or individual, e.g. Artificial Bee Colony Algorithm (ABCA). Honeybees are also model organisms for profiling the so-called intelligence of a swarm or collective intelligence. Additionally, they serve as models for guidance systems and aviation technologies. Bees have inspired important projects in robotics, such as B-droid, Robobee and The Green Brain Project. It has also been confirmed that the apian sense of smell can be used to detect explosive devices, such as TNT, or drugs (including heroin, cocaine, amphetamines and cannabis). This inconspicuous little insect can revolutionize the world of science and contribute to the solution of many scientific problems as a versatile model.
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Rasouli, Samira, Kerstin Dautenhahn, and Chrystopher L. Nehaniv. "Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics." Biomimetics 9, no. 11 (November 1, 2024): 668. http://dx.doi.org/10.3390/biomimetics9110668.
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This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. In this work, we developed a robot controller using Reynolds’ flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we proposed using a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots, and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds’ rules in a swarm of e-puck robots. Our algorithm outperformed this method in terms of cohesion performance and aggregation completion time.
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Martinez, Fredy, Holman Montiel, and Fernando Martinez. "A novel visual tracking scheme for unstructured indoor environments." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (December 1, 2023): 6216. http://dx.doi.org/10.11591/ijece.v13i6.pp6216-6227.
Full textAbstract:
In the ever-expanding sphere of assistive robotics, the pressing need for advanced methods capable of accurately tracking individuals within unstructured indoor settings has been magnified. This research endeavours to devise a realtime visual tracking mechanism that encapsulates high performance attributes while maintaining minimal computational requirements. Inspired by the neural processes of the human brain’s visual information handling, our innovative algorithm employs a pattern image, serving as an ephemeral memory, which facilitates the identification of motion within images. This tracking paradigm was subjected to rigorous testing on a Nao humanoid robot, demonstrating noteworthy outcomes in controlled laboratory conditions. The algorithm exhibited a remarkably low false detection rate, less than 4%, and target losses were recorded in merely 12% of instances, thus attesting to its successful operation. Moreover, the algorithm’s capacity to accurately estimate the direct distance to the target further substantiated its high efficacy. These compelling findings serve as a substantial contribution to assistive robotics. The proficient visual tracking methodology proposed herein holds the potential to markedly amplify the competencies of robots operating in dynamic, unstructured indoor settings, and set the foundation for a higher degree of complex interactive tasks.
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Li, Jialong. "A path planning generator based on the Chaos Game Optimization algorithm." Applied and Computational Engineering 55, no. 1 (July 25, 2024): 222–31. http://dx.doi.org/10.54254/2755-2721/55/20241526.
Full textAbstract:
This research paper explores a novel path planning generator that leverages the Chaos Game Optimization (CGO) algorithm, a mathematical technique inspired by the chaos game that creates fractals. The CGO algorithm is applied to analyze fractal configurations and self-similarity problems in path planning. The paper provides detailed information about the initialization of candidate solutions and the iterative process of updating their positions and fitness values. Through MATLAB simulations, the paper demonstrates the CGO algorithm's effectiveness in generating optimal paths in complex scenarios with randomly generated blocks or labyrinth environments. The approach shows great potential in enhancing the capabilities of autonomous robots in navigating dynamic and challenging environments. This paper also simulated the path planning generator using the CGO algorithm in MATLAB. By implementing chaos theory and randomness, the CGO algorithm provides a robust and efficient solution for path planning, enabling robotic systems to handle complex and nonlinear problems. The paper concludes that the application of chaos theory in robotics opens up exciting possibilities for advancing the capabilities of robotic systems and enhancing their performance in real-world scenarios.
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Li, Junfei, and Simon X. Yang. "Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models." Biomimetics 9, no. 1 (January 1, 2024): 16. http://dx.doi.org/10.3390/biomimetics9010016.
Full textAbstract:
This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group formation. The swarm robots are designed to adaptively reconfigure their movements in response to environmental changes, mimicking the flexibility and robustness of fish foraging patterns. The simulation results show that the proposed approach demonstrates improved cooperation, efficiency, and adaptability in various scenarios. The proposed approach shows significant strides in the field of swarm robotics by successfully implementing fish-inspired foraging strategies. The integration of neurodynamic models with swarm intelligence not only enhances the autonomous capabilities of individual robots, but also improves the collective efficiency of the swarm robots.