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Journal articles on the topic 'Autonomous robots; Path planning'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Elbanhawi, Mohamed, Milan Simic, and Reza Jazar. "Autonomous Robots Path Planning: An Adaptive Roadmap Approach." Applied Mechanics and Materials 373-375 (August 2013): 246–54. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.246.

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Developing algorithms that allow robots to independently navigate unknown environments is a widely researched area of robotics. The potential for autonomous mobile robots use, in industrial and military applications, is boundless. Path planning entails computing a collision free path from a robots current position to a desired target. The problem of path planning for these robots remains underdeveloped. Computational complexity, path optimization and robustness are some of the issues that arise. Current algorithms do not generate general solutions for different situations and require user experience and optimization. Classical algorithms are computationally extensive. This reduces the possibility of their use in real time applications. Additionally, classical algorithms do not allow for any control over attributes of the generated path. A new roadmap path planning algorithm is proposed in this paper. This method generates waypoints, through which the robot can avoid obstacles and reach its goal. At the heart of this algorithm is a method to control the distance of the waypoints from obstacles, without increasing its computational complexity. Several simulations were run to illustrate the robustness and adaptability of this approach, compared to the most commonly used path planning methods.
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Zhang, Hong Min. "Path Planning Methods of Mobile Robot Based on Soft Computing Technique." Advanced Materials Research 216 (March 2011): 677–80. http://dx.doi.org/10.4028/www.scientific.net/amr.216.677.

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Path planning is one of the most important and challenging problems of mobile robot. It is one of the keys that will make the mobile robots fully autonomous. In this paper, we summarized the application of soft computing approaches in path planning for mobile robot. Finally the future works of path planning for mobile robots are prospected.
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Saleem Sumbal, Muhammad. "Environment Detection and Path Planning Using the E-puck Robot." IAES International Journal of Robotics and Automation (IJRA) 5, no. 3 (August 20, 2016): 151. http://dx.doi.org/10.11591/ijra.v5i3.pp151-160.

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Automatic path planning is one of the most challenging problems confronted by autonomous robots. Generating optimal paths for autonomous robots are some of the heavily studied subjects in mobile robotics applications. This paper documents the implementation of a path planning project using a mobile robot in a structured environment. The environment is detected through a camera and then a roadmap of the environment is built using some algorithms. Finally a graph search algorithm called A* is implemented that searches through the roadmap and finds an optimal path for robot to move from start position to goal position avoiding obstacles
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Kumar, Rajeev, Laxman Singh, and Rajdev Tiwari. "Path planning for the autonomous robots using modified grey wolf optimization approach." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 9453–70. http://dx.doi.org/10.3233/jifs-201926.

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Path planning for robots plays a vital role to seek the most feasible path due to power requirement, environmental factors and other limitations. The path planning for the autonomous robots is tedious task as the robot needs to locate a suitable path to move between the source and destination points with multifaceted nature. In this paper, we introduced a new technique named modified grey wolf optimization (MGWO) algorithm to solve the path planning problem for multi-robots. MGWO is modified version of conventional grey wolf optimization (GWO) that belongs to the category of metaheuristic algorithms. This has gained wide popularity for an optimization of different parameters in the discrete search space to solve various problems. The prime goal of the proposed methodology is to determine the optimal path while maintaining a sufficient distance from other objects and moving robots. In MGWO method, omega wolves are treated equally as those of delta wolves in exploration process that helps in escalating the convergence speed and minimizing the execution time. The simulation results show that MGWO gives satisfactory performance than other state of art methods for path planning of multiple mobile robots. The performance of the proposed method is compared with the standard evolutionary algorithms viz., Particle Swarm Optimization (PSO), Intelligent BAT Algorithm (IBA), Grey Wolf Optimization (GWO), and Variable Weight Grey Wolf Optimization (VW-GWO) and yielded better results than all of these.
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Valbahs, Edvards, and Peter Grabusts. "Path Planning Usage for Autonomous Agents." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (August 8, 2015): 40. http://dx.doi.org/10.17770/etr2013vol2.867.

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In order to achieve the wide range of the robotic application it is necessary to provide iterative motions among points of the goals. For instance, in the industry mobile robots can replace any components between a storehouse and an assembly department. Ammunition replacement is widely used in military services. Working place is possible in ports, airports, waste site and etc. Mobile agents can be used for monitoring if it is necessary to observe control points in the secret place. The paper deals with path planning programme for mobile robots. The aim of the research paper is to analyse motion-planning algorithms that contain the design of modelling programme. The programme is needed as environment modelling to obtain the simulation data. The simulation data give the possibility to conduct the wide analyses for selected algorithm. Analysis means the simulation data interpretation and comparison with other data obtained using the motion-planning. The results of the careful analysis were considered for optimal path planning algorithms. The experimental evidence was proposed to demonstrate the effectiveness of the algorithm for steady covered space. The results described in this work can be extended in a number of directions, and applied to other algorithms.
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Zanlongo, Sebastián A., Peter Dirksmeier, Philip Long, Taskin Padir, and Leonardo Bobadilla. "Scheduling and Path-Planning for Operator Oversight of Multiple Robots." Robotics 10, no. 2 (April 6, 2021): 57. http://dx.doi.org/10.3390/robotics10020057.

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There is a need for semi-autonomous systems capable of performing both automated tasks and supervised maneuvers. When dealing with multiple robots or robots with high complexity (such as humanoids), we face the issue of effectively coordinating operators across robots. We build on our previous work to present a methodology for designing trajectories and policies for robots such that a few operators can supervise multiple robots. Specifically, we: (1) Analyze the complexity of the problem, (2) Design a procedure for generating policies allowing operators to oversee many robots, (3) Present a method for designing policies and robot trajectories to allow operators to oversee multiple robots, and (4) Include both simulation and hardware experiments demonstrating our methodologies.
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Liu, Yong-tao, Rui-zhi Sun, Tian-yi Zhang, Xiang-nan Zhang, Li Li, and Guo-qing Shi. "Warehouse-Oriented Optimal Path Planning for Autonomous Mobile Fire-Fighting Robots." Security and Communication Networks 2020 (June 20, 2020): 1–13. http://dx.doi.org/10.1155/2020/6371814.

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In order to achieve the fastest fire-fighting purpose, warehouse autonomous mobile fire-fighting robots need to make an overall optimal planning based on the principle of the shortest time for their traveling path. A∗ algorithm is considered as a very ideal shortest path planning algorithm, but the shortest path is not necessarily the optimal path for robots. Furthermore, the conventional A∗ algorithm is affected by the search neighborhood restriction and the theoretical characteristics, so there are many problems, which are closing to obstacles, more inflection points, more redundant points, larger total turning angle, etc. Therefore, A∗ algorithm is improved in eight ways, and the inflection point prior strategy is adopted to compromise Floyd algorithm and A∗ algorithm in this paper. According to the criterion of the inflection point in this paper, the path inflection point arrays are constructed and traveling all path nodes are replaced by traveling path inflection points for the conventional Floyd algorithm backtracking, so it greatly reduces the backtracking time of the smooth path. In addition, this paper adopts the method of the extended grid map obstacle space in path planning safety distance. According to the relationship between the actual scale of the warehouse grid map and the size of the robot body, the different safe distance between the planning path and the obstacles is obtained, so that the algorithm can be applied to the safe path planning of the different size robots in any map environments. Finally, compared with the conventional A∗ algorithm, the improved algorithm reduces by 7.846% for the path length, reduces by 71.429% for the number of the cumulative turns, and reduces by 75% for the cumulative turning angle through the experiment. The proposed method can ensure robots to move fast on the planning path and ultimately achieve the goal of reducing the number of inflection points, reducing the cumulative turning angle, and reducing the path planning time.
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8

Urdiales, C., A. Bandera, F. Arrebola, and F. Sandoval. "Multi-level path planning algorithm for autonomous robots." Electronics Letters 34, no. 2 (1998): 223. http://dx.doi.org/10.1049/el:19980204.

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9

Arokiasami, Willson Amalraj, Prahlad Vadakkepat, Kay Chen Tan, and Dipti Srinivasan. "Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework." Unmanned Systems 06, no. 04 (October 2018): 231–50. http://dx.doi.org/10.1142/s2301385018500061.

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Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.
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Masone, Carlo, Mostafa Mohammadi, Paolo Robuffo Giordano, and Antonio Franchi. "Shared planning and control for mobile robots with integral haptic feedback." International Journal of Robotics Research 37, no. 11 (September 2018): 1395–420. http://dx.doi.org/10.1177/0278364918802006.

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This paper presents a novel bilateral shared framework for online trajectory generation for mobile robots. The robot navigates along a dynamic path, represented as a B-spline, whose parameters are jointly controlled by a human supervisor and an autonomous algorithm. The human steers the reference (ideal) path by acting on the path parameters that are also affected, at the same time, by the autonomous algorithm to ensure: (i) collision avoidance, (ii) path regularity, and (iii) proximity to some points of interest. These goals are achieved by combining a gradient descent-like control action with an automatic algorithm that re-initializes the traveled path (replanning) in cluttered environments to mitigate the effects of local minima. The control actions of both the human and the autonomous algorithm are fused via a filter that preserves a set of local geometrical properties of the path to ease the tracking task of the mobile robot. The bilateral component of the interaction is implemented via a force feedback that accounts for both human and autonomous control actions along the whole path, thus providing information about the mismatch between the reference and traveled path in an integral sense. The proposed framework is validated by means of realistic simulations and actual experiments deploying a quadrotor unmanned aerial vehicle (UAV) supervised by a human operator acting via a force-feedback haptic interface. Finally, a user study is presented to validate the effectiveness of the proposed framework and the usefulness of the provided force cues.
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11

Vasu, V., and K. Jyothi Kumar. "Optimal Path Planning of an Autonomous Mobile Robot Using Genetic Algorithm." Advanced Materials Research 488-489 (March 2012): 1747–51. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1747.

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An autonomous Mobile Robot (AMR) is a machine able to extract information from its environment and move in a meaningful and purposeful manner. Robot Navigation and Obstacle avoidance are the most important problems in mobile robots. In the past, a number of soft computing algorithms have been designed by many researchers for robot navigation problems but very few are actually implementable because they haven’t considered robot size as parameter. This paper presents software simulation and hardware implementation of navigation of a mobile robot avoiding obstacles and selecting optimal path in a static environment using evolution based Genetic algorithms with robot size as a parameter in fitness function.
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12

Ma, Xi Pei, Bing Feng Qian, Song Jie Zhang, and Ye Wang. "Research on Technology and Application of Multi-Sensor Data Fusion for Indoor Service Robots." Applied Mechanics and Materials 651-653 (September 2014): 831–34. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.831.

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The autonomous navigation process of a mobile service robot is usually in uncertain environment. The information only given by sensors has been unable to meet the demand of the modern mobile robots, so multi-sensor data fusion has been widely used in the field of robots. The platform of this project is the achievement of the important 863 Program national research project-a prototype nursing robot. The aim is to study a mobile service robot’s multi-sensor information fusion, path planning and movement control method. It can provide a basis and practical use’s reference for the study of an indoor robot’s localization.
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13

Fazlollahtabar, Hamed. "An Effective Mathematical Programming Model for Production Automatic Robot Path Planning." Open Transportation Journal 13, no. 1 (March 26, 2019): 11–16. http://dx.doi.org/10.2174/1874447801913010011.

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Objective: Path planning for production robots has been investigated. The sequence of the orders to be processed in a certain planning horizon has been planned for the production system. Methods: Production automatic robots are employed to carry parts and products among all production stations and machining centers. The combination of machines in stations and autonomous robot evolves a production network. Results: The problem is to assign orders to robots so that paths are obtained to minimize total waiting times of production system and meanwhile provide collision-free paths. Conclusion: The proposed mathematical formulation is implemented to show the efficiency and effectiveness.
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14

Pruski, A. "Multivalue coding: application to autonomous robots." Robotica 10, no. 2 (March 1992): 125–33. http://dx.doi.org/10.1017/s0263574700007542.

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SUMMARYThe paper describes a free space modeling method by multivalue coding. Each code defines some numerical values representing a set of cells from a grid. The idea consists in using the grid as a Karnaugh board whose rows and columns are binary coded rather than Gray coded. This operating method allows to define, for each code, its grid location and allows numerical comparison in order to locate a code relatively to another. This aspect is helpful for path planning. The free space model is represented by a switching function or a tree to which boolean algebra rules and mathematic operations are applied. We describe an application to mobile robot path planning.
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15

Ma, Yingchong, Gang Zheng, Wilfrid Perruquetti, and Zhaopeng Qiu. "Local path planning for mobile robots based on intermediate objectives." Robotica 33, no. 4 (April 1, 2014): 1017–31. http://dx.doi.org/10.1017/s0263574714000186.

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SUMMARYThis paper presents a path planning algorithm for autonomous navigation of non-holonomic mobile robots in complex environments. The irregular contour of obstacles is represented by segments. The goal of the robot is to move towards a known target while avoiding obstacles. The velocity constraints, robot kinematic model and non-holonomic constraint are considered in the problem. The optimal path planning problem is formulated as a constrained receding horizon planning problem and the trajectory is obtained by solving an optimal control problem with constraints. Local minima are avoided by choosing intermediate objectives based on the real-time environment.
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Zhi, Yonghong, and Yan Jiang. "Design of basketball robot based on behavior-based fuzzy control." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142090996. http://dx.doi.org/10.1177/1729881420909965.

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Aiming at the strong dependence on environmental information in traditional algorithms, the path planning of basketball robots in an unknown environment, and improving the safety of autonomous navigation, this article proposes a path planning algorithm based on behavior-based module control. In this article, fuzzy control theory is applied to the behavior control structure, and these two path planning algorithms are combined to solve the path planning problem of basketball robots in an unknown environment. First, the data of each sensor of the basketball robot configuration are simply fused. Then, the obstacle distance parameters in the three directions of front, left, and right are simplified and fuzzified. Then combined with the target direction parameters, the speed, and steering of the basketball robot are controlled by fuzzy rule reasoning to realize path planning. The simulation results show that the basketball robot can overcome the uncertainty in the environment, effectively achieve good path planning, verify the feasibility of the fuzzy control algorithm, and demonstrate the validity and correctness of the path planning strategy.
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Hosseininejad, Seyedhadi, and Chitra Dadkhah. "Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141983957. http://dx.doi.org/10.1177/1729881419839575.

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Nowadays, the usage of autonomous mobile robots that fulfill various activities in enormous number of applications without human’s interference in a dynamic environment are thriving. A dynamic environment is the robot’s environment which is comprised of some static obstacles as well as several movable obstacles that their quantity and location change randomly through the time. Efficient path planning is one the significant necessities of these kind of robots to do their tasks effectively. Mobile robot path planning in a dynamic environment is finding a shortest possible path from an arbitrary starting point toward a desired goal point which needs to be safe (obstacle avoidance) and smooth as well as possible. To achieve this target, simultaneously satisfying a collection of certain constraints including the shortest, smooth, and collision free path is required. Therefore, this issue can be considered as an optimization problem, consequently solved via optimization algorithms. In this article, a new method based on cuckoo optimization algorithm is proposed for solving the mobile robot path planning problem in a dynamic environment. Furthermore, to diminish the computational complexity, the feature vector is also optimized (i.e. reduced in dimension) via a new proposed technique. The simulation results show the performance of proposed algorithm in finding a short, safe, smooth, and collision free path in different environment conditions.
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Song, Weibo, Wei Wang, Xianjiu Guo, and Fengjiao Jiang. "Autonomous Return Path Planning Method for Small Underwater Robots." Journal of Coastal Research 83, sp1 (May 4, 2019): 184. http://dx.doi.org/10.2112/si83-028.1.

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19

Volos, Ch K., I. M. Kyprianidis, and I. N. Stouboulos. "A chaotic path planning generator for autonomous mobile robots." Robotics and Autonomous Systems 60, no. 4 (April 2012): 651–56. http://dx.doi.org/10.1016/j.robot.2012.01.001.

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20

Azpúrua, Héctor, Gustavo M. Freitas, Douglas G. Macharet, and Mario F. M. Campos. "Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys." Robotica 36, no. 8 (April 15, 2018): 1144–66. http://dx.doi.org/10.1017/s0263574718000292.

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SUMMARYThe field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.
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21

Muñoz, Pablo, María D. R-Moreno, and David F. Barrero. "Unified framework for path-planning and task-planning for autonomous robots." Robotics and Autonomous Systems 82 (August 2016): 1–14. http://dx.doi.org/10.1016/j.robot.2016.04.010.

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22

Alvarez, Gabriela, and Omar Flor. "Desempeño en métodos de navegación autónoma para robots móviles." Minerva 1, no. 2 (August 8, 2020): 19–29. http://dx.doi.org/10.47460/minerva.v1i2.8.

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En este trabajo se presenta una comparación de los tiempos de respuesta, optimización de la ruta y complejidad del grafo en métodos de planificación de trayectoria para robots móviles autónomos. Se contrastan los desarrollos de Voronoi, Campos potenciales, Roadmap probabilístico y Descomposición en celdas para la navegación en un mismo entorno y validándolos para un número variable de obstáculos. Las evaluaciones demuestran que el método de generación de trayectoria por Campos Potenciales, mejora la navegación respecto de la menor ruta obtenida, el método Rapidly Random Tree genera los grafos de menor complejidad y el método Descomposición en celdas, se desempeña con menor tiempo de respuesta y menor coste computacional. Palabras Clave: optimización, trayectoria, métodos de planificación, robots móviles. Referencias [1]H. Ajeil, K. Ibraheem, A. Sahib y J. Humaidi, “Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm, ” Applied Soft Computing, vol. 89, April 2020. [2]K.Patle, G. Babu, A. Pandey, D.R.K. Parhi y A. Jagadeesh, “A review: On path planning strategies for navigation of mobile robot,” Defence Technology, vol. 15, pp. 582-606, August 2019. [3]T. Mack, C. Copot, D. Trung y R. De Keyser, “Heuristic approaches in robot path planning: A survey,” Robotics and Autonomous Systems, vol. 86, pp. 13-28, December 2016. [4]L. Zhang, Z. Lin, J. Wang y B. He, “Rapidly-exploring Random Trees multi-robot map exploration under optimization framework,” Robotics and Autonomous Systems, vol. 131, 2020. [5]S. Khan y M. K. Ahmmed, "Where am I? Autonomous navigation system of a mobile robot in an unknown environment," 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 56-61, December 2016. [6]V. Castro, J. P. Neira, C. L. Rueda, J. C. Villamizar y L. Angel, "Autonomous Navigation Strategies for Mobile Robots using a Probabilistic Neural Network (PNN)," IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 2795-2800, Taipei, 2007. [7]Y. Li, W. Wei, Y. Gao, D. Wang y C. Fan, “PQ-RRT*: An improved path planning algorithm for mobile robots,” Expert Systems with Applications, vol. 152, August 2020. [8]A. Muñoz, “Generación global de trayectorias para robots móviles, basada en curvas betaspline,” Dep. Ingeniería de Sistemas y Automática Escuela Técnica Superior de Ingeniería Universidad de Sevilla, 2014. [9]H. Montiel, E. Jacinto y H. Martínez, “Generación de Ruta Óptima para Robots Móviles a Partir de Segmentación de Imágenes,” Información Tecnológica, vol. 26, 2015. [10] C. Expósito, “Los diagramas de Vornooi, la forma matemática de dividir el mundo,” Dialnet, Diciembre 2016.
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Paull, Liam, Mae Seto, John J. Leonard, and Howard Li. "Probabilistic cooperative mobile robot area coverage and its application to autonomous seabed mapping." International Journal of Robotics Research 37, no. 1 (November 30, 2017): 21–45. http://dx.doi.org/10.1177/0278364917741969.

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There are many applications that require mobile robots to autonomously cover an entire area with a sensor or end effector. The vast majority of the literature on this subject is focused on addressing path planning for area coverage under the assumption that the robot’s pose is known or that error is bounded. In this work, we remove this assumption and develop a completely probabilistic representation of coverage. We show that coverage is guaranteed as long as the robot pose estimates are consistent, a much milder assumption than zero or bounded error. After formally connecting robot sensor uncertainty with area coverage, we propose an adaptive sliding window filter pose estimator that provides a close approximation to the full maximum a posteriori estimate with a computation cost that is bounded over time. Subsequently, an adaptive planning strategy is presented that automatically exploits conditions of low vehicle uncertainty to more efficiently cover an area. We further extend this approach to the multi-robot case where robots can communicate through a (possibly faulty and low-bandwidth) channel and make relative measurements of one another. In this case, area coverage is achieved more quickly since the uncertainty over the robots’ trajectories is reduced. We apply the framework to the scenario of mapping an area of seabed with an autonomous underwater vehicle. Experimental results support the claim that our method achieves guaranteed complete coverage notwithstanding poor navigational sensors and that resulting path lengths required to cover the entire area are shortest using the proposed cooperative and adaptive approach.
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Lavrenov, Lavrenov, Evgeni Magid, Matsuno Fumitoshi, Mikhail Svinin, and Jackrit Suthakorn. "Development and Implementation of Spline-based Path Planning Algorithm in ROS/Gazebo Environment." SPIIRAS Proceedings 18, no. 1 (February 21, 2019): 57–84. http://dx.doi.org/10.15622/sp.18.1.57-84.

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Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment. In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria. This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point. To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms. The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.
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Das, Subir Kumar, Ajoy Kumar Dutta, and Subir Kumar Debnath. "OperativeCriticalPointBug algorithm-local path planning of mobile robot avoiding obstacles." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (June 1, 2020): 1646. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1646-1656.

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<span>For Autonomous Mobile Robot one of the biggest and interesting issues is path planning. An autonomous mobile robot should be able determine its own path to reach destination. This paper offers a new algorithm for mobile robot to plan a path in local environments with stationary as well as moving obstacles. For movable robots’ path planning OperativeCriticalPointBug (OCPB) algorithm, is a new Bug algorithm. This algorithm is carried out by the robot throughout the movement from source to goal, hence allowing the robot to rectify its way if a new obstacle comes into the route or any existing obstacle changes its route. According as, not only the robot tries to avoid clash with other obstacle but also tries a series of run time adjustment in its way to produce roughly a best possible path. During journey the robot is believed to be capable to act in an unknown location by acquiring information perceived locally. Using this algorithm the robot can avoid obstacle by considering its own as well as the obstacle’s dimension. The obstacle may be static or dynamic. The algorithm belongs to bug family.</span>
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Ravankar, Abhijeet, Ankit Ravankar, Yukinori Kobayashi, Yohei Hoshino, and Chao-Chung Peng. "Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges." Sensors 18, no. 9 (September 19, 2018): 3170. http://dx.doi.org/10.3390/s18093170.

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Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.
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Ji, Xue Song, Ping Xu, and Guo Chen Niu. "Visual-Based Motion Planning for Autonomous Humanoid Service Robot." Advanced Materials Research 143-144 (October 2010): 1031–35. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.1031.

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A visual-based autonomous motion planning algorithm is designed to guide the robot to take the elevator autonomously. The floor chosen operation as well as the path planning and motion control for the robot’s in-out elevator are the two critical issues. A visual-based motion regulation method is proposed in this paper, so is a manipulator motion planning algorithm based on image Jacobian Matrix. Fuzzy logic is used to mobile path planning. Experimental results on humanoid service robot proves the validity of this control system.
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Ćurković, Petar, and Lovro Čehulić. "Diversity Maintenance for Efficient Robot Path Planning." Applied Sciences 10, no. 5 (March 3, 2020): 1721. http://dx.doi.org/10.3390/app10051721.

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Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.
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Cui, Peng, Weisheng Yan, and Yintao Wang. "Reactive Path Planning Approach for Docking Robots in Unknown Environment." Journal of Advanced Transportation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6716820.

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Autonomous robots need to be recharged and exchange information with the host through docking in the long-distance tasks. Therefore, feasible path is required in the docking process to guide the robot and adjust its pose. However, when there are unknown obstacles in the work area, it becomes difficult to determine the feasible path for docking. This paper presents a reactive path planning approach named Dubins-APF (DAPF) to solve the path planning problem for docking in unknown environment with obstacles. In this proposed approach the Dubins curves are combined with the designed obstacle avoidance potential field to plan the feasible path. Firstly, an initial path is planned and followed according to the configurations of the robot and the docking station. Then when the followed path is evaluated to be infeasible, the intermediate configuration is calculated as well as the replanned path based on the obstacle avoidance potential field. The robot will be navigated to the docking station with proper pose eventually via the DAPF approach. The proposed DAPF approach is efficient and does not require the prior knowledge about the environment. Simulation results are given to validate the effectiveness and feasibility of the proposed approach.
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Minh, Vu Trieu. "Nonlinear Model Predictive Controller and Feasible Path Planning for Autonomous Robots." Open Computer Science 6, no. 1 (November 15, 2016): 178–86. http://dx.doi.org/10.1515/comp-2016-0015.

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AbstractThis paper develops the nonlinear model predictive control (NMPC) algorithm to control autonomous robots tracking feasible paths generated directly from the nonlinear dynamic equations.NMPC algorithm can secure the stability of this dynamic system by imposing additional conditions on the open loop NMPC regulator. The NMPC algorithm maintains a terminal constrained region to the origin and thus, guarantees the stability of the nonlinear system. Simulations show that the NMPC algorithm can minimize the path tracking errors and control the autonomous robots tracking exactly on the feasible paths subject to the system’s physical constraints.
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Yoon, Hee-Sang, and Tae-Hyoung Park. "Path Planning for Autonomous Mobile Robots by Modified Global DWA." Transactions of The Korean Institute of Electrical Engineers 60, no. 2 (February 1, 2011): 389–97. http://dx.doi.org/10.5370/kiee.2011.60.2.389.

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Santos, Luís Carlos, André Silva Aguiar, Filipe Neves Santos, António Valente, and Marcelo Petry. "Occupancy Grid and Topological Maps Extraction from Satellite Images for Path Planning in Agricultural Robots." Robotics 9, no. 4 (September 24, 2020): 77. http://dx.doi.org/10.3390/robotics9040077.

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Robotics will significantly impact large sectors of the economy with relatively low productivity, such as Agri-Food production. Deploying agricultural robots on the farm is still a challenging task. When it comes to localising the robot, there is a need for a preliminary map, which is obtained from a first robot visit to the farm. Mapping is a semi-autonomous task that requires a human operator to drive the robot throughout the environment using a control pad. Visual and geometric features are used by Simultaneous Localisation and Mapping (SLAM) Algorithms to model and recognise places, and track the robot’s motion. In agricultural fields, this represents a time-consuming operation. This work proposes a novel solution—called AgRoBPP-bridge—to autonomously extract Occupancy Grid and Topological maps from satellites images. These preliminary maps are used by the robot in its first visit, reducing the need of human intervention and making the path planning algorithms more efficient. AgRoBPP-bridge consists of two stages: vineyards row detection and topological map extraction. For vineyards row detection, we explored two approaches, one that is based on conventional machine learning technique, by considering Support Vector Machine with Local Binary Pattern-based features, and another one found in deep learning techniques (ResNET and DenseNET). From the vineyards row detection, we extracted an occupation grid map and, by considering advanced image processing techniques and Voronoi diagrams concept, we obtained a topological map. Our results demonstrated an overall accuracy higher than 85% for detecting vineyards and free paths for robot navigation. The Support Vector Machine (SVM)-based approach demonstrated the best performance in terms of precision and computational resources consumption. AgRoBPP-bridge shows to be a relevant contribution to simplify the deployment of robots in agriculture.
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Nishitani, Ippei, Tetsuya Matsumura, Mayumi Ozawa, Ayanori Yorozu, and Masaki Takahashi. "3D X-Y-T Space Path Planning for Autonomous Mobile Robots Considering Dynamic Constraints." Applied Mechanics and Materials 490-491 (January 2014): 1163–67. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1163.

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An autonomous mobile robot in a human living space should be able to not only realize collision-free motion but also give way to humans depending on the situation. Although various reactive obstacle avoidance methods have been proposed, it is difficult to achieve such motion. On the other hand, 3D X-Y-T space path planning, which takes into account the motion of both the robot and the human in a look-ahead time horizon, is effective. This paper proposes a real-time obstacle avoidance method for an autonomous mobile robot that considers the robots dynamic constraints, the personal space, and human directional area based on grid-based 3D X-Y-T space path planning. The proposed method generates collision-free motion in which the robot can yield to humans. To verify the effectiveness of the proposed method, various experiments in which the humans position and velocity were estimated using laser range finders were carried out.
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Torres-Sospedra, Joaquín, and Patricio Nebot. "Combining Satellite Images and Cadastral Information for Outdoor Autonomous Mapping and Navigation: A Proof-of-Concept Study in Citric Groves." Algorithms 12, no. 9 (September 11, 2019): 193. http://dx.doi.org/10.3390/a12090193.

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The development of robotic applications for agricultural environments has several problems which are not present in the robotic systems used for indoor environments. Some of these problems can be solved with an efficient navigation system. In this paper, a new system is introduced to improve the navigation tasks for those robots which operate in agricultural environments. Concretely, the paper focuses on the problem related to the autonomous mapping of agricultural parcels (i.e., an orange grove). The map created by the system will be used to help the robots navigate into the parcel to perform maintenance tasks such as weed removal, harvest, or pest inspection. The proposed system connects to a satellite positioning service to obtain the real coordinates where the robotic system is placed. With these coordinates, the parcel information is downloaded from an online map service in order to autonomously obtain a map of the parcel in a readable format for the robot. Finally, path planning is performed by means of Fast Marching techniques using the robot or a team of two robots. This paper introduces the proof-of-concept and describes all the necessary steps and algorithms to obtain the path planning just from the initial coordinates of the robot.
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Liu, Zai Xin, Long Xiang Yang, and Jin Ge Wang. "Soccer Robot Path Planning Based on Evolutionary Artificial Field." Advanced Materials Research 562-564 (August 2012): 955–58. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.955.

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To improve the success rate of Soccer Robot Path Planning, artificial potential field is amended, autonomous potential field is presented to solve the path planning problem by analyzing shortcomings of the basic shooting algorithm, the autonomous potential field function centering on the soccer robot is constructed, and the robot’s movement in the new potential field is analyzed, the modified artificial potential field model and autonomous potential field model is contrasted, each vicinal potential energy of the modified artificial potential field model and autonomous potential field model is analyzed. The simulated results demonstrate that this method can optimize the path of a soccer robot, decrease the complexity, enhance the real time capability, perform the shooting action better, and improve the success rate of a soccer robot shooting a goal.
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Keerthana, V., C. Kiruthiga, P. Kiruthika, V. Sowmiya, and R. Manikadan. "NAVIGATION OF MOBILE ROBOT- ALGORITHM FOR PATH PLANNING & COLLISION AVOIDANCE- A REVIEW." International Journal of Research -GRANTHAALAYAH 5, no. 1 (January 31, 2017): 198–205. http://dx.doi.org/10.29121/granthaalayah.v5.i1.2017.1735.

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The field of autonomous mobile robotics has recently gained many researchers’ interests. Due to the specific needs required by various applications of mobile robot systems, especially in navigation, designing a real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. The main objective of our project is applications based mobile robot systems, especially in navigation, designing real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. The main objective behind using the obstacle avoidance approach is to obtain a collision-free trajectory from the starting point to the target in monitoring environments. The ability of the robot to follow a path, detects obstacles, and navigates around them to avoid collision. It also shows that the robot has been successfully following very congested curves and has avoided any obstacle that emerged on its path. Motion planning that allows the robot to reach its target without colliding with any obstacles that may exist in its path. To avoid collision in the mobile robot environment, providing a path planning& line following approach. Line following, path planning, collision avoidance, back propagation, improved memory, detecting long distance obstacles. Cheap and economical than the former one. Also work with back propagation technique.
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Zagradjanin, Novak, Aleksandar Rodic, Dragan Pamucar, and Bojan Pavkovic. "Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning." Information Technology and Control 50, no. 2 (June 17, 2021): 357–74. http://dx.doi.org/10.5755/j01.itc.50.2.28234.

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This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.
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Saeedi, Sajad, Carl Thibault, Michael Trentini, and Howard Li. "3D Mapping for Autonomous Quadrotor Aircraft." Unmanned Systems 05, no. 03 (July 2017): 181–96. http://dx.doi.org/10.1142/s2301385017400064.

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Autonomous navigation in global positioning system (GPS)-denied environments is one of the challenging problems in robotics. For small flying robots, autonomous navigation is even more challenging. These robots have limitations such as fast dynamics and limited sensor payload. To develop an autonomous robot, many challenges including two-dimensional (2D) and three-dimensional (3D) perception, path planning, exploration, and obstacle avoidance should be addressed in real-time and with limited resources. In this paper, a complete solution for autonomous navigation of a quadrotor rotorcraft is presented. The proposed solution includes 2D and 3D mapping with several autonomous behaviors such as target localization and displaying maps on multiple remote tablets. Multiple tests were performed in simulated and indoor/outdoor environments to show the effectiveness of the proposed solution.
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Cai, Jianxian, Xiaogang Ruan, and Pengxuan Li. "Autonomous Path Planning Scheme Research for Mobile Robot." Cybernetics and Information Technologies 16, no. 4 (December 1, 2016): 113–25. http://dx.doi.org/10.1515/cait-2016-0072.

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Abstract An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the function of operate action. The results of the simulation experiment in mobile robot showed that the designed autonomous path-planning strategy lets the robot realize autonomous navigation path planning. The robot learns to select autonomously according to the bionic orientate action and have fast convergence rate and higher adaptability.
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40

Van Pham, Hai, Farzin Asadi, Nurettin Abut, and Ismet Kandilli. "Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications." Applied Sciences 9, no. 9 (May 9, 2019): 1909. http://dx.doi.org/10.3390/app9091909.

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Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.
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41

Lisowski, Józef. "Synthesis of a Path-Planning Algorithm for Autonomous Robots Moving in a Game Environment during Collision Avoidance." Electronics 10, no. 6 (March 13, 2021): 675. http://dx.doi.org/10.3390/electronics10060675.

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This paper describes and illustrates the optimization of a safe mobile robot control process in collision situations using the model of a multistep matrix game of many participants in the form of a dual linear programming problem. The synthesis of non-cooperative and cooperative game control software was performed in Matlab/Simulink software to determine the safe path of the robot when passing a greater number of other robots and obstacles. The operation of the game motion control algorithm of a mobile robot is illustrated by computer simulations made in the Matlab/Simulink program of two real previously recorded navigation situations while passing dozens of other autonomous mobile robots.
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42

Le, Anh Vu, Nguyen Huu Khanh Nhan, and Rajesh Elara Mohan. "Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots." Sensors 20, no. 2 (January 13, 2020): 445. http://dx.doi.org/10.3390/s20020445.

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Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots.
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43

teja, T. Ravi. "Autonomous robot motion path planning using shortest path planning algorithms." IOSR Journal of Engineering 3, no. 01 (January 2013): 65–69. http://dx.doi.org/10.9790/3021-03116569.

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44

Karur, Karthik, Nitin Sharma, Chinmay Dharmatti, and Joshua E. Siegel. "A Survey of Path Planning Algorithms for Mobile Robots." Vehicles 3, no. 3 (August 4, 2021): 448–68. http://dx.doi.org/10.3390/vehicles3030027.

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Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.
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45

DAS, SUBIR KUMAR. "Local Path Planning of Mobile Robot Using Critical-PointBug Algorithm Avoiding Static Obstacles." IAES International Journal of Robotics and Automation (IJRA) 5, no. 3 (September 1, 2016): 182. http://dx.doi.org/10.11591/ijra.v5i3.pp182-189.

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<p align="left">Path planning is an essential task for the navigation of Autonomous Mobile Robot. This is one of the basic problems in robotics. Path planning algorithms are classified as global or local, depending on the knowledge of surrounding environment. In local path planning, the environment is unknown to the robot, and sensors are used to detect the obstacles and to avoid collision. Bug algorithms are one of the frequently used path planning algorithms where a mobile robot moves to the target by detecting the nearest obstacle and avoiding it with limited information about the environment. This proposed Critical-PointBug algorithm, is a new Bug algorithm for path planning of mobile robots. This algorithm tries to minimize traversal of obstacle border by searching few important points on the boundary of obstacle area as a rotation point to goal and end with a complete path from source to goal.</p>
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46

Lee, Sooyong, and Jae-Bok Song. "Generalized Voronoi Diagram based Path Planning for a Mobile Robot in Dynamic Environment(Autonomous Path Planning,Session: MP1-A)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 25. http://dx.doi.org/10.1299/jsmeicam.2004.4.25_2.

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47

Le, Anh, Ping-Cheng Ku, Thein Than Tun, Nguyen Huu Khanh Nhan, Yuyao Shi, and Rajesh Mohan. "Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot." Energies 12, no. 6 (March 23, 2019): 1136. http://dx.doi.org/10.3390/en12061136.

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The efficiency of energy usage applied to robots that implement autonomous duties such as floor cleaning depends crucially on the adopted path planning strategies. Energy-aware for complete coverage path planning (CCPP) in the reconfigurable robots raises interesting research, since the ability to change the robot’s shape needs the dynamic estimate energy model. In this paper, a CCPP for a predefined workspace by a new floor cleaning platform (hTetro) which can self-reconfigure among seven tetromino shape by the cooperation of hinge-based four blocks with independent differential drive modules is proposed. To this end, the energy consumption is represented by travel distances which consider operations of differential drive modules of the hTetro kinematic designs to fulfill the transformation, orientation correction and translation actions during robot navigation processes from source waypoint to destination waypoint. The optimal trajectory connecting all pairs of waypoints on the workspace is modeled and solved by evolutionary algorithms of TSP such as Genetic Algorithm (GA) and Ant Optimization Colony (AC) which are among the well-known optimization approaches of TSP. The evaluations across several conventional complete coverage algorithms to prove that TSP-based proposed method is a practical energy-aware navigation sequencing strategy that can be implemented to our hTetro robot in different real-time workspaces. Moreover, The CCPP framework with its modulation in this paper allows the convenient implementation on other polynomial-based reconfigurable robots.
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48

Xie, Li, Christian Henkel, Karl Stol, and Weiliang Xu. "Power-minimization and energy-reduction autonomous navigation of an omnidirectional Mecanum robot via the dynamic window approach local trajectory planning." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141875456. http://dx.doi.org/10.1177/1729881418754563.

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To improve the energy efficiency of the Mecanum wheel, this article extends the dynamic window approach by adding a new energy-related criterion for minimizing the power consumption of autonomous mobile robots. The energy consumption of the Mecanum robot is first modeled by considering major factors. Then, the model is utilized in the extended dynamic window approach–based local trajectory planner to additionally evaluate the omnidirectional velocities of the robot. Based on the new trajectory planning objective that minimizes power consumption, energy-reduction autonomous navigation is proposed via the combinational cost objectives of low power consumption and high speed. Comprehensive experiments are performed in various autonomous navigation task scenarios, to validate the energy consumption model and to show the effectiveness of the proposed technique in minimizing the power consumption and reducing the energy consumption. It is observed that the technique effectively takes advantage of the Mecanum robot’s redundant maneuverability, can cope with any type of path and is able to fulfil online obstacle avoidance.
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Lewis, Michael, Huadong Wang, Shih Yi Chien, Prasanna Velagapudi, Paul Scerri, and Katia Sycara. "Process and Performance in Human-Robot Teams." Journal of Cognitive Engineering and Decision Making 5, no. 2 (June 2011): 186–208. http://dx.doi.org/10.1177/1555343411409323.

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The authors are developing a theory for human control of robot teams based on considering how control difficulty grows with team size. Current work focuses on domains, such as foraging, in which robots perform largely independent tasks. Such tasks are particularly amenable to analysis because effects on performance and cognitive resources are predicted to be additive, and tasks can safely be allocated across operators because of their independence. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an urban search-and-rescue (USAR) task. Two possible ways to organize operators were identified: as individual assignments of robots to operators, assigned robots, or as a shared pool in which operators service robots from the population as needed. The experiment compares two-person teams of operators controlling teams of 12 robots each in the assigned-robots condition or sharing control of 24 robots in the shared-pool condition using either waypoint control in the manual condition or autonomous path planning in the autonomy condition. Automating path planning improved system performance, but process measures suggest it may weaken situation awareness.
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Ayawli, Ben Beklisi Kwame, Xue Mei, Moquan Shen, Albert Yaw Appiah, and Frimpong Kyeremeh. "Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment." Information Technology And Control 48, no. 2 (June 25, 2019): 179–94. http://dx.doi.org/10.5755/j01.itc.48.2.21390.

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This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments
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