Intelligent Exploration Approaches Based on Utility Functions Optimization for Multi-Agent Environment Applications
Source abstract
Abstract
In this work, the problem of exploring an unknown environment with a team of agents and search different targets on it is considered. The key problem to be solved in multiple agents is choosing appropriate target points for the individual agents to simultaneously explore different regions of the environment. An intelligent approach is presented to coordinate several agents using a market-based model to identify the appropriate task for each agent. It is proposed to compare the fitting of the market utility function using neural networks and optimize this function using genetic algorithms to avoid heavy computation in the Non-Polynomial (NP: nondeterministic polynomial time) path-planning problem. An indoor environment inspires the proposed approach with homogeneous physical agents, and its performance is tested in simulations. The results show that the proposed approach allocates agents effectively to the environment and enables them to carry out their mission quickly.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Optimization and Search Problems · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia
References (40)
Show source-provided references
1. Exploring unknown environments with obstacles10.1007/s00453-001-0067-x ↗
2. Apostolopoulos, D.S., Pedersen, L., Shamah, B.N., Shillcutt, K., Wagner, M.D., and Whittaker, W.L. (2001, January 21–26). Robotic Antarctic meteorite search: Outcomes. Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea.
3. Hougen, D.F., Benjaafar, S., Bonney, J.C., Budenske, J.R., Dvorak, M., Gini, M., French, H., Krantz, D.G., Li, P.Y., and Malver, F. (2000, January 24–28). Miniature robotic system for reconnaissance and surveillance. Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA.
4. Sugiyama, H., Tsujioka, T., and Murata, M. (2005, January 19–22). Collaborative movement of rescue robots for reliable and effective networking in disaster area. Proceedings of the 2005 International Conference on Collaborative Computing: Networking, Applications and Worksharing, San Jose, CA, USA.
5. Autonomous pool cleaning: Self localization and autonomous navigation for cleaning10.1023/A:1008962901812 ↗
6. Integrating exploration and localization for mobile robots10.1177/105971239900700204 ↗
7. Burgard, W., Moors, M., Fox, D., Simmons, R., and Thrun, S. (2000, January 24–28). Collaborative multi-robot exploration. Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA.
8. Cao, Y.U., Fukunaga, A.S., Kahng, A.B., and Meng, F. (1995, January 5–9). Cooperative mobile robotics: Antecedents and directions. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Pittsburgh, PA, USA.
9. Many robots make short work: Report of the sri international mobile robot team
10. Probabilistic approach to collaborative multi-robot localization10.1023/A:1008937911390 ↗
11. Yamauchi, B. (1997, January 10–11). Frontier-based approach for autonomous exploration. Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, Monterey, CA, USA.
12. Calisi, D., Farinelli, A., Locchi, L., and Nardi, D. (2005, January 6–9). Autonomous navigation and exploration in a rescue environment. Proceedings of the 2005 IEEE International Workshop on Safety, Security and Rescue Robotics, Kobe, Japan.
13. Da Silva Lubanco, D.L., Pichler-Scheder, M., and Schlechter, T. (2020, January 12–15). A Novel Frontier-Based Exploration Algorithm for Mobile Robots. Proceedings of the 2020 6th International Conference on Mechatronics and Robotics Engineering, ICMRE 2020, Barcelona, Spain.10.1109/ICMRE49073.2020.9064866 ↗
14. Yamauchi, B. (, January May). Frontier-based exploration using multiple robots. Proceedings of the International Conference on Autonomous Agents, Minneapolis, MN, USA.
15. Ko, J., Stewart, B., Fox, D., Konolige, K., and Limketkai, B. (2003, January 27–31). A Practical, Decision-theoretic Approach to Multi-robot Mapping and Exploration. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA.
16. Fang, G., Dissanayake, G., and Lau, H. (2004, January 1–4). A behaviour-based optimisation strategy for multi-robot exploration. Proceedings of the 2004 IEEE Conference on Robotics, Automation and Mechatronics, Singapore.
17. Coordinated multi-robot exploration10.1109/TRO.2004.839232 ↗
18. Cheng, C., and Leng, G. (October, January 28). Cooperative Search Algorithms for Distributed Autonomous Agents. Proceedings of the IEEE/RSJ International Conference on Intelligent Agents and Systems, Sendai, Japan.
19. Ogras, U.Y., Dagci, O.H., and Ozguner, U. (2004, January 5). Cooperative control of mobile robots for target search. Proceedings of the IEEE International Conference on Mechatronics 2004, ICM’04, Istanbul, Turkey.
20. Heterogeneous teams of modular robots for mapping and exploration10.1023/A:1008933826411 ↗
21. Grabowski, R., Khosla, P., and Choset, H. (2003, January 27–31). Autonomous Exploration via Regions of Interest. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA.
22. Mataric, M., and Sukhatme, G. (2001, January 22–25). Task-allocation and coordination of multiple robots for planetary exploration. Proceedings of the 10th International Conference on Advanced Robotics, Buda, Hungary.
23. Beetz, M., Hertzberg, J., Ghallab, M., and Pollack, M.E. (2002). Collaborative Exploration of Unknown Environments with Teams of Mobile Robots. Advances in Plan-Based Control of Robotic Agents. Lecture Notes in Computer Science, Springer.
24. Simmons, R., Apfelbaum, D., Burgard, W., and Fox, D. (August, January 30). Coordination for multi-robot exploration and mapping. Proceedings of the Seventeenth National Conference on Artificial Intelligence, Austin, TX, USA.
25. Berhault, M., Huang, H., Keskinocak, P., Koenig, S., Elmaghraby, W., Griffin, P., and Kleywegt, A. (2003, January 27–31). Robot Exploration with Combinatorial Auctions. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA.
26. A multi-robot system for adaptive exploration of a fast-changing environment: Probabilistic modeling and experimental study10.1080/095400999116304 ↗
27. Wooldridge, M. (2009). An Introduction to Multi-Agent Systems, John Wiley and Sons Ltd.. [2nd ed.].
28. Shi, H., Li, J., and Li, Z. (2020). A distributed strategy for cooperative autonomous robots using pedestrian behavior for multi-target search in the unknown environment. Sensors, 20.10.3390/s20061606 ↗
29. Blatt, F., and Szczerbicka, H. (2017). Combining the Multi-Agent Flood Algorithm with Frontier-Based Exploration In Search & Rescue Applications. Simulation Series, Proceedings of the 2017 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), Seattle, WA, Canada, 9–12 July 2017, IEEE.10.23919/SPECTS.2017.8046775 ↗
30. Gomez, C., Hernandez, A.C., and Barber, R. (2019). Topological frontier-based exploration and map-building using semantic information. Sensors, 19.10.3390/s19204595 ↗
31. Communicating Multi-UAV System for Cooperative SLAM-based Exploration10.1007/s10846-019-01062-6 ↗
32. Autonomous Robotic Exploration Based on Frontier Point Optimization and Multistep Path Planning10.1109/ACCESS.2019.2909307 ↗
33. Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments10.1109/LRA.2019.2891991 ↗
34. Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment10.1109/TNNLS.2019.2927869 ↗
35. Chen, Z., Subagdja, B., and Tan, A.H. (2019, January 18–21). End-to-end Deep Reinforcement Learning for Multi-agent Collaborative Exploration. Proceedings of the 2019 IEEE International Conference on Agents, ICA 2019, Jinan, China.10.1109/AGENTS.2019.8929192 ↗
36. Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning10.1109/TVT.2020.3034800 ↗
37. Distributed multi-agent deep reinforcement learning for cooperative multi-robot pursuit
38. Hou, Y., Ruan, X., Zhu, X., and Li, C. (2018, January 25–27). Frontier-based exploration on continuous radial basis function neural network map. Proceedings of the Chinese Control Conference, CCC, Wuhan, China.10.23919/ChiCC.2018.8482673 ↗
39. Shrestha, R., Tian, F.P., Feng, W., Tan, P., and Vaughan, R. (2019, January 20–24). Learned map prediction for enhanced mobile robot exploration. Proceedings of the IEEE International Conference on Robotics and Automation, Montreal, QC, Canada.10.1109/ICRA.2019.8793769 ↗
40. Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator10.1145/272991.272995 ↗