Journal articles: 'Autonomous search'

1

Gelenbe, Erol, and Yonghuan Cao. "Autonomous search for mines." European Journal of Operational Research 108, no. 2 (July 1998): 319–33. http://dx.doi.org/10.1016/s0377-2217(97)00373-1.

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Huo, Jianwen, Manlu Liu, Konstantin A. Neusypin, Haojie Liu, Mingming Guo, and Yufeng Xiao. "Autonomous Search of Radioactive Sources through Mobile Robots." Sensors 20, no. 12 (June 19, 2020): 3461. http://dx.doi.org/10.3390/s20123461.

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The research of robotic autonomous radioactivity detection or radioactive source search plays an important role in the monitoring and disposal of nuclear safety and biological safety. In this paper, a method for autonomously searching for radioactive sources through mobile robots was proposed. In the method, by using a partially observable Markov decision process (POMDP), the search of autonomous unknown radioactive sources was realized according to a series of radiation information measured by mobile robot. First, the factors affecting the accuracy of radiation measurement during the robot’s movement were analyzed. Based on these factors, the behavior set of POMDP was designed. Secondly, the parameters of the radioactive source were estimated in the Bayesian framework. In addition, through the reward strategy, autonomous navigation of the robot to the position of the radiation source was achieved. The search algorithm was simulated and tested, and the TurtleBot robot platform was used to conduct a real search experiment on the radio source Cs-137 with an activity of 37 MBq indoors. The experimental results showed the effectiveness of the method. Additionally, from the experiments, it could been seen that the robot was affected by the linear velocity, angular velocity, positioning accuracy and the number of measurements in the process of autonomous search for the radioactive source. The proposed mobile robot autonomous search method can be applied to the search for lost radioactive sources, as well as for the leakage of substances (nuclear or chemical) in nuclear power plants and chemical plants.

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Mansor, Hasmah, Muhamad Haziq Norhisam, Zulkifli Zainal Abidin, and Teddy Surya Gunawan. "Autonomous surface vessel for search and rescue operation." Bulletin of Electrical Engineering and Informatics 10, no. 3 (June 1, 2021): 1701–8. http://dx.doi.org/10.11591/eei.v10i3.2599.

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Search and rescue operation is performed to save human life, for example during natural disasters, unfortunate incidents on the land, in the deepwater, or lakes. There were incidents happened to the search and rescue crew during the operation although they were well trained. A new method using robotic technology is important to reduce the crew's risk during operations. This research proposed a development of an autonomous surface vessel for search and rescue operations for deepwater applications. The proposed autonomous surface vessel is equipped with a global positioning system (GPS) and underwater sensor to search for the victims, black box, debris, or other evidence on the surface and underwater. The vessel was designed with monitoring and control via radio frequency wireless communication. The autonomous surface vessel prototype was developed and tested successfully with the telemetry at the ground station. The ground station acts as the control centre of the overall system. Results showed the vessel successfully operated autonomously. The operator at the ground station was able to monitor the sensor data and control the vessel's manoeuvre according to the created path. The telemetry coverage to monitor the water surroundings and control the vessel's manoeuvre was around 100 meters.

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Harel, David, Assaf Marron, and Joseph Sifakis. "Autonomics: In search of a foundation for next-generation autonomous systems." Proceedings of the National Academy of Sciences 117, no. 30 (July 21, 2020): 17491–98. http://dx.doi.org/10.1073/pnas.2003162117.

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The potential benefits of autonomous systems are obvious. However, there are still major issues to be dealt with before developing such systems becomes a commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next-generation autonomous systems is a must, and term the desired foundation “autonomics.” We focus on three main challenges: 1) how to specify autonomous system behavior in the face of unpredictability; 2) how to carry out faithful analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems; and 3) how to build such systems by combining executable modeling techniques from software engineering with artificial intelligence and machine learning.

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SYAFITRI, NIKEN, RATNA SUSANA, IHSAN FARRASSALAM AMMARPRAWIRA, MOCHAMAD SEPTONI FAUZI, and ARBI ABDUL JABBAAR. "The Autonomous Disaster Victim Search Robot using the Waypoint Method." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 2 (May 19, 2020): 347. http://dx.doi.org/10.26760/elkomika.v8i2.347.

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ABSTRAKPada kasus bencana alam, penyebaran robot pada area bencana sangat bermanfaat bagi tim SAR dalam proses pencarian korban. Khususnya, jika robot yang disebar merupakan robot otonom untuk meningkatkan proses pencarian, secara efektif dan efisien. Pada artikel ini, kami mengusulkan sebuah robot otonom untuk pencarian korban bencana yang ditanamkan metode waypoint dengan mengeksploitasi sensor, GPS dan divais komunikasi. Hasil yang didapat menunjukkan bahwa robot ini mampu mencari korban bencana secara otonom dengan mendeteksi pergerakan kecil dan temperatur korban. Kemudian, koordinat lokasi korban dikirim ke tim SAR untuk operasi penyelamatan. Robot ini juga mampu melakukan perjalanan pulang ke posko asal untuk pengisian daya ketika daya baterainya hampir habis.Kata kunci: robot otonom, waypoint, bencana, pencarian korban, perjalanan pulang ABSTRACTIn the case of disaster, the deployment of robots is immensely helpful for SAR team in the process of searching the victims. Especially, if the deployed robots are autonomous to enhance the searching process, effectively and efficiently. In this paper, we propose an autonomous robot for searching the disaster victims embedded by automatic waypoint method by exploiting sensors, GPS and communication devices. Result shows that this robot is able to search the victims autonomously by detecting their slight movement and temperature then sending the coordinates to the SAR team for the rescue operation. This robot is also able to do the return trip autonomously to the initial post for charging when its power is almost running out.Keywords: autonomous robot, waypoint, disaster, victim searching, return trip

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Piacentini, Chiara, Sara Bernardini, and J. Christopher Beck. "Autonomous Target Search with Multiple Coordinated UAVs." Journal of Artificial Intelligence Research 65 (August 8, 2019): 519–68. http://dx.doi.org/10.1613/jair.1.11635.

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Search and tracking is the problem of locating a moving target and following it to its destination. In this work, we consider a scenario in which the target moves across a large geographical area by following a road network and the search is performed by a team of unmanned aerial vehicles (UAVs). We formulate search and tracking as a combinatorial optimization problem and prove that the objective function is submodular. We exploit this property to devise a greedy algorithm. Although this algorithm does not offer strong theoretical guarantees because of the presence of temporal constraints that limit the feasibility of the solutions, it presents remarkably good performance, especially when several UAVs are available for the mission. As the greedy algorithm suffers when resources are scarce, we investigate two alternative optimization techniques: Constraint Programming (CP) and AI planning. Both approaches struggle to cope with large problems, and so we strengthen them by leveraging the greedy algorithm. We use the greedy solution to warm start the CP model and to devise a domain-dependent heuristic for planning. Our extensive experimental evaluation studies the scalability of the different techniques and identifies the conditions under which one approach becomes preferable to the others.

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Dollarhide, Robert L., Arvin Agah, and Gary J. Minden. "Evolving controllers for autonomous robot search teams." Artificial Life and Robotics 5, no. 3 (September 2001): 178–88. http://dx.doi.org/10.1007/bf02481466.

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Soto, Ricardo, Broderick Crawford, Wenceslao Palma, Karin Galleguillos, Carlos Castro, Eric Monfroy, Franklin Johnson, and Fernando Paredes. "Boosting autonomous search for CSPs via skylines." Information Sciences 308 (July 2015): 38–48. http://dx.doi.org/10.1016/j.ins.2015.01.035.

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de Cubber, Geert, Haris Balta, and Claude Lietart. "Teodor: A Semi-Autonomous Search and Rescue and Demining Robot." Applied Mechanics and Materials 658 (October 2014): 599–605. http://dx.doi.org/10.4028/www.scientific.net/amm.658.599.

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In this paper, we present a ground robotic system which is developed to deal with rough outdoor conditions. The platform is to be used as an environmental monitoring robot for 2 main application areas: - Humanitarian demining: The vehicle is equipped with a specialized multi-channel metal detector array. An unmanned aerial system supports it for locating suspected locations of mines, which can then be confirmed by the ground vehicle. - Search and rescue: The vehicle is equipped with human victim detection sensors and a 3D camera enabling it to assess the traversability of the terrain in front of the robot in order to be able to navigate autonomously. The paper discusses both the mechanical design of these platforms as the autonomous perception capabilities on board of these vehicles.

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Liu, Yuan, Min Wang, Zhou Su, Jun Luo, Shaorong Xie, Yan Peng, Huayan Pu, Jiajia Xie, and Rui Zhou. "Multi-AUVs Cooperative Target Search Based on Autonomous Cooperative Search Learning Algorithm." Journal of Marine Science and Engineering 8, no. 11 (October 26, 2020): 843. http://dx.doi.org/10.3390/jmse8110843.

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As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.

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Gelenbe, Erol, Nestor Schmajuk, John Staddon, and John Reif. "Autonomous search by robots and animals: A survey." Robotics and Autonomous Systems 22, no. 1 (November 1997): 23–34. http://dx.doi.org/10.1016/s0921-8890(97)00014-6.

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12

Dollarhide, Robert L., and Arvin Agah. "Modeling and Simulation of Autonomous Robot Search Teams." Systems Analysis Modelling Simulation 42, no. 12 (January 2002): 1739–61. http://dx.doi.org/10.1080/911178267.

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13

Bischoff, Joschka, Michal Maciejewski, Tilmann Schlenther, and Kai Nagel. "Autonomous Vehicles and their Impact on Parking Search." IEEE Intelligent Transportation Systems Magazine 11, no. 4 (2019): 19–27. http://dx.doi.org/10.1109/mits.2018.2876566.

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Dollarhide, Robert L., and Arvin Agah. "Modeling and Simulation of Autonomous Robot Search Teams." Systems Analysis Modelling Simulation 42, no. 12 (January 1, 2003): 1739–61. http://dx.doi.org/10.1080/02329290309362.

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15

Yang, Sheng Qing, Jian Qiao Yu, and Si Yu Zhang. "Cooperative Search of Autonomous Vehicles for Unknown Targets." Applied Mechanics and Materials 281 (January 2013): 3–9. http://dx.doi.org/10.4028/www.scientific.net/amm.281.3.

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Motivated by recent research on cooperative search of autonomous vehicles, a new approach for searching unknown targets is introduced in this paper. The unknown targets are assumed to be static. ZAMBONI search in spiral curve form is considered to implement the cooperation of vehicles. Algorithms that based on geometry underlying search process are discussed to make vehicles act in the spiral curves form. The receding horizon control is introduced for obstacle avoidance which can result in a feasible trajectory during the search process. Simulations of the hybrid method based on ZAMBONI search and receding horizon control show promising results.

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Kim, Kyung Min, and Suk-Joon Ko. "Path Search for Autonomous Train via Dial Algorithm." Journal of Korean Institute of Communications and Information Sciences 43, no. 4 (April 30, 2018): 775–83. http://dx.doi.org/10.7840/kics.2018.43.4.775.

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17

Fujisawa, Kae, Soichiro Hayakawa, Takeshi Aoki, Tatsuya Suzuki, and Shigeru Okuma. "A Real-Time Action Search for Autonomous Mobile Robot." Journal of the Robotics Society of Japan 17, no. 4 (1999): 503–12. http://dx.doi.org/10.7210/jrsj.17.503.

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18

Kim, Ho-Duck, Han-Ul Yoon, and Kwee-Bo Sim. "Strategy of Object Search for Distributed Autonomous Robotic Systems." International Journal of Fuzzy Logic and Intelligent Systems 6, no. 3 (September 1, 2006): 264–69. http://dx.doi.org/10.5391/ijfis.2006.6.3.264.

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19

Bakar, B. Abu, and S. M. Veres. "Direct Policy Search Method in Fault Tolerant Autonomous Systems." IFAC Proceedings Volumes 43, no. 10 (2010): 336–41. http://dx.doi.org/10.3182/20100826-3-tr-4015.00062.

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20

Aloba, Leo T. "Classification and features of search autonomous underwater vehicles control." Collection of Scientific Publications NUS, no. 1 (2019): 89–97. http://dx.doi.org/10.15589/znp2019.1(475).12.

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21

ITO, TOSHIKATSU. "In Search of an Autonomous History of Southeast Asia." Southeast Asia: History and Culture, no. 22 (1993): 122–51. http://dx.doi.org/10.5512/sea.1993.122.

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Yoon, Seokhoon, and Chunming Qiao. "Cooperative Search and Survey Using Autonomous Underwater Vehicles (AUVs)." IEEE Transactions on Parallel and Distributed Systems 22, no. 3 (March 2011): 364–79. http://dx.doi.org/10.1109/tpds.2010.88.

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Taqwim Safrudin, Ahsanu, Surya Darma, and Santoso Sukirno. "Object detection for autonomous search and rescue quadrotor application." Journal of Physics: Conference Series 1528 (April 2020): 012017. http://dx.doi.org/10.1088/1742-6596/1528/1/012017.

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Silva, Jorge, Alberto Speranzon, João Borges de Sousa, and Karl Henrik Johansson. "Hierarchical search strategy for a team of autonomous vehicles." IFAC Proceedings Volumes 37, no. 8 (July 2004): 567–72. http://dx.doi.org/10.1016/s1474-6670(17)32038-4.

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Furukawa, Tomonari, Lin Chi Mak, Hugh Durrant-Whyte, and Rajmohan Madhavan. "Autonomous Bayesian Search and Tracking, and its Experimental Validation." Advanced Robotics 26, no. 5-6 (January 2012): 461–85. http://dx.doi.org/10.1163/156855311x617461.

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Bi, Yingcai, Menglu Lan, Jiaxin Li, Shupeng Lai, and Ben M. Chen. "A lightweight autonomous MAV for indoor search and rescue." Asian Journal of Control 21, no. 4 (July 2019): 1732–44. http://dx.doi.org/10.1002/asjc.2162.

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Calisi, Daniele, Alessandro Farinelli, Luca Iocchi, and Daniele Nardi. "Multi-objective exploration and search for autonomous rescue robots." Journal of Field Robotics 24, no. 8-9 (2007): 763–77. http://dx.doi.org/10.1002/rob.20216.

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Betts, Kevin M., and Mikel D. Petty. "Automated Search-Based Robustness Testing for Autonomous Vehicle Software." Modelling and Simulation in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/5309348.

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Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing) and the method most commonly used today (Monte Carlo testing). The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1) finding the single most challenging test case and (2) finding the set of fifty test cases with the highest mean degree of challenge.

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Ristic, Branko, Alex Skvortsov, and Ajith Gunatilaka. "A study of cognitive strategies for an autonomous search." Information Fusion 28 (March 2016): 1–9. http://dx.doi.org/10.1016/j.inffus.2015.06.008.

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Flint, Matthew, Emmanuel Fernandez, and Marios Polycarpou. "Stochastic Models of a Cooperative Autonomous UAV Search Problem." Military Operations Research 8, no. 4 (September 1, 2003): 13–32. http://dx.doi.org/10.5711/morj.8.4.13.

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Doki, Kae, Soichiro Hayakawa, Tatsuya Suzuki, Shigeru Okuma, and Takeshi Aoki. "Intelligent Control for Autonomous Mobile Robot based on Information Criterion on Virtual Environmental." Journal of Robotics and Mechatronics 13, no. 2 (April 20, 2001): 114–24. http://dx.doi.org/10.20965/jrm.2001.p0114.

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This paper proposes a new intelligent control method for autonomous mobile robots to acquire appropriate actions based on a real-time action search. In the proposed method, information criterion on the virtual environment is defined based on Kullback-Leibler divergence, which measures quality of the environmental information used for the action search. The robot searches suitable actions based on the environmental information whose quality is improved step by step. By applying the proposed method, the trade-off between calculation amount and information quality is taken into account, and the search process converges faster. The proposed method is applied to the moving obstacles avoidance problem, and its usefulness is verified through some simulation results.

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Gardner, James A. "In Search of Sub-National Constitutionalism." European Constitutional Law Review 4, no. 2 (June 2008): 325–43. http://dx.doi.org/10.1017/s1574019608003258.

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Federalism as a consequence of local/cultural self-determination – Degree of autonomous human rights protection on a subnational level – Analysis in terms of two dimensions: subnational constitutionalism and contestatory federalism – Contrast between US and European models – Opposition between contestatory federalism and subsidiarity – Rise of international human rights protection diminishes importance of subnational human rights protection

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Almeshal, Abdullah, and Mohammad Alenezi. "A Vision-Based Neural Network Controller for the Autonomous Landing of a Quadrotor on Moving Targets." Robotics 7, no. 4 (November 13, 2018): 71. http://dx.doi.org/10.3390/robotics7040071.

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Time constraints is the most critical factor that faces the first responders’ teams for search and rescue operations during the aftermath of natural disasters and hazardous areas. The utilization of robotic solutions to speed up search missions would help save the lives of humans who are in need of help as quickly as possible. With such a human-robot collaboration, by using autonomous robotic solutions, the first response team will be able to locate the causalities and possible victims in order to be able to drop emergency kits at their locations. This paper presents a design of vision-based neural network controller for the autonomous landing of a quadrotor on fixed and moving targets for Maritime Search and Rescue applications. The proposed controller does not require prior information about the target location and depends entirely on the vision system to estimate the target positions. Simulations of the proposed controller are presented using ROS Gazebo environment and are validated experimentally in the laboratory using a Parrot AR Drone system. The simulation and experimental results show the successful control of the quadrotor in autonomously landing on both fixed and moving landing platforms.

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Yu, Liang, and Da Lin. "Bayesian-Based Search Decision Framework and Search Strategy Analysis in Probabilistic Search." Scientific Programming 2020 (November 18, 2020): 1–15. http://dx.doi.org/10.1155/2020/8865381.

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In this paper, a sequence decision framework based on the Bayesian search is proposed to solve the problem of using an autonomous system to search for the missing target in an unknown environment. In the task, search cost and search efficiency are two competing requirements because they are closely related to the search task. Especially in the actual search task, the sensor assembled by the searcher is not perfect, so an effective search strategy is needed to guide the search agent to perform the task. Meanwhile, the decision-making method is crucial for the search agent. If the search agent fully trusts the feedback information of the sensor, the search task will end when the target is “detected” for the first time, which means it must take the risk of founding a wrong target. Conversely, if the search agent does not trust the feedback information of the sensor, it will most likely miss the real target, which will waste a lot of search resources and time. Based on the existing work, this paper proposes two search strategies and an improved algorithm. Compared with other search methods, the proposed strategies greatly improve the efficiency of unmanned search. Finally, the numerical simulations are provided to demonstrate the effectiveness of the search strategies.

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Suryadibrata, Alethea, Julio Cristian Young, and Richard Luhulima. "Review of Various A* Pathfinding Implementations in Game Autonomous Agent." IJNMT (International Journal of New Media Technology) 6, no. 1 (August 30, 2019): 43–49. http://dx.doi.org/10.31937/ijnmt.v6i1.1075.

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Among many pathfinding algorithms, A* search algorithm is an algorithm that most commonly used in grid-based pathfinding. This is due to implementation of A* search which proven to be able to generate the optimal path in a relatively short time by combining two characteristics of Djikstra's and best-first search algorithm. In the implementation of A* search, the selection of heuristic function and data structure can affect the performance of the algorithm. The purpose of this research is to find the best heuristic function and data structure with regards of the performance in A* search implementation as a pathfinding algorithm in a 3D platform. In the experiment, some known heuristic functions and data structures will be tested on the various 3D platform with a different size and obstacle percentage. Based on experiment that have been done, euclidean squared distance is best heuristic function for 3D pathfinding problem, with regards of the implementation performance. In addition, we also found that binary heap is the best data structure to be implemented for 3D pathfinding problem, with regards of implementation performance.

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Murakami, Hisashi, and Yukio-Pegio Gunji. "Autonomous change of behavior for environmental context: An intermittent search model with misunderstanding search pattern." Mathematical Methods in the Applied Sciences 40, no. 18 (July 10, 2017): 7013–21. http://dx.doi.org/10.1002/mma.4508.

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Brooks, Harvey. "AUTONOMOUS SCIENCE AND SOCIALLY RESPONSIVE SCIENCE: A Search for Resolution." Annual Review of Energy and the Environment 26, no. 1 (November 2001): 29–48. http://dx.doi.org/10.1146/annurev.energy.26.1.29.

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Varela, Gervasio, Pilar Caamaño, Félix Orjales, Álvaro Deibe, Fernando López-Peña, and Richard J. Duro. "Autonomous UAV based search operations using Constrained Sampling Evolutionary Algorithms." Neurocomputing 132 (May 2014): 54–67. http://dx.doi.org/10.1016/j.neucom.2013.03.060.

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Chen, Yanyan, Michael G. H. Bell, and Klaus Bogenberger. "Risk-Averse Autonomous Route Guidance by a Constrained A* Search." Journal of Intelligent Transportation Systems 14, no. 3 (July 26, 2010): 188–96. http://dx.doi.org/10.1080/15472450.2010.484753.

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El-Fakdi, A., M. Carreras, and J. Batlle. "Direct Policy Search Reinforcement Learning for Autonomous Underwater Cable Tracking." IFAC Proceedings Volumes 41, no. 1 (2008): 155–60. http://dx.doi.org/10.3182/20080408-3-ie-4914.00028.

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Wettergren, Thomas A., and C. Michael Traweek. "The Search Benefits of Autonomous Mobility in Distributed Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 2 (January 2012): 797040. http://dx.doi.org/10.1155/2012/797040.

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Kim, Youchang, Dongjoo Shin, Jinsu Lee, and Hoi-Jun Yoo. "BRAIN: A Low-Power Deep Search Engine for Autonomous Robots." IEEE Micro 37, no. 5 (September 2017): 11–19. http://dx.doi.org/10.1109/mm.2017.3711641.

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Choutri, Kheireddine, Mohand Lagha, and Laurent Dala. "A Fully Autonomous Search and Rescue System Using Quadrotor UAV." International Journal of Computing and Digital Systems 10, no. 1 (April 1, 2021): 403–14. http://dx.doi.org/10.12785/ijcds/100140.

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Chen, Wen-Hua, Callum Rhodes, and Cunjia Liu. "Dual Control for Exploitation and Exploration (DCEE) in autonomous search." Automatica 133 (November 2021): 109851. http://dx.doi.org/10.1016/j.automatica.2021.109851.

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45

Yan, Ming Zhong, and Da Qi Zhu. "An Algorithm of Complete Coverage Path Planning for Autonomous Underwater Vehicles." Key Engineering Materials 467-469 (February 2011): 1377–85. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1377.

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Complete coverage path planning (CCPP) is an essential issue for Autonomous Underwater Vehicles’ (AUV) tasks, such as submarine search operations and complete coverage ocean explorations. A CCPP approach based on biologically inspired neural network is proposed for AUVs in the context of completely unknown environment. The AUV path is autonomously planned without any prior knowledge of the time-varying workspace, without explicitly optimizing any global cost functions, and without any learning procedures. The simulation studies show that the proposed approaches are capable of planning more reasonable collision-free complete coverage paths in unknown underwater environment.

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VILASÍS-CARDONA, XAVIER, and MIREIA VINYOLES-SERRA. "TWO NEURON CNNS: SEARCH FOR LIMIT CYCLES." International Journal of Bifurcation and Chaos 20, no. 04 (April 2010): 1137–73. http://dx.doi.org/10.1142/s0218127410026368.

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In this paper, we show sufficient conditions for the existence of limit cycles in the general continuous time two-neuron autonomous CNN. We find that different types of limit cycles correspond to different regions in the template parameter space. Actually, we are able to predict the CNN behavior from the template values for the full parameter range, except for two small bounded regions.

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Cavazza, Marc. "A Motivational Model of BCI-Controlled Heuristic Search." Brain Sciences 8, no. 9 (August 31, 2018): 166. http://dx.doi.org/10.3390/brainsci8090166.

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Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.

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Hwang, Jimin, Neil Bose, Hung Duc Nguyen, and Guy Williams. "Acoustic Search and Detection of Oil Plumes Using an Autonomous Underwater Vehicle." Journal of Marine Science and Engineering 8, no. 8 (August 17, 2020): 618. http://dx.doi.org/10.3390/jmse8080618.

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We introduce an adaptive sampling method that has been developed to support the Backseat Driver control architecture of the Memorial University of Newfoundland (MUN) Explorer autonomous underwater vehicle (AUV). The design is based on an acoustic detection and in-situ analysis program that allows an AUV to perform automatic detection and autonomous tracking of an oil plume. The method contains acoustic image acquisition, autonomous triggering, and thresholding in the search stage. A new biomimetic search pattern, the bumblebee flight path, was designed to maximize the spatial coverage in the oil plume detection phase. The effectiveness of the developed algorithm was validated through simulations using a two-dimensional planar plume model and a 90-degree scanning sensor model. The results demonstrate that the bumblebee search design combined with a genetic solution for the Traveling Salesperson Problem outperformed a conventional lawnmower survey, reducing the AUV travel distance by up to 75.3%. Our plume detection strategy, using acoustic sensing, provided data of plume location, distribution, and density, over a sector in contrast with traditional chemical oil sensors that only provide readings at a point.

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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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Jain, Vanita, Dharmender Saini, Monu Gupta, Neeraj Joshi, Anubhav Mishra, Vishakha Bansal, and D. Jude Hemanth. "A Comprehensive Review on Design of Autonomous Robotic Boat for Rescue Applications." Mathematical Problems in Engineering 2021 (June 21, 2021): 1–17. http://dx.doi.org/10.1155/2021/6614002.

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New technologies are advancing and emerging day by day to improve the safety of humans by making use of various autonomous technologies. The continuous utilization of autonomous vehicles/systems in search and rescue (SAR) operations is a challenging research area particularly for marine-based activities. A comprehensive systematic literature review (SLR) providing an overview of improvisations that have been done in the field of autonomous technologies for search and rescue operation over the last five years has been compiled in this paper. A methodology for using autonomous vehicles in water for SAR operations has been incorporated and demonstrated. The focus of this study is to look at the various techniques and address different challenges faced for human beings’ safety during rescue operation. The comparison of results achieved for various technologies and algorithms is highlighted in this paper. This literature survey proves to be a good source of information for fellow researchers to precisely analyze the study results.

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Journal articles on the topic 'Autonomous search'

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