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Risk-aware Collection Strategies for Multirobot Foraging in Hazardous Environments

Published:06 July 2022Publication History
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Abstract

Existing studies on the multirobot foraging problem often assume safe settings, in which nothing in an environment hinders the robots’ tasks. In many real-world applications, robots have to collect objects from hazardous environments like earthquake rescue, where possible risks exist, with possibilities of destroying robots. At this stage, there are no targeted algorithms for foraging robots in hazardous environments, which can lead to damage to the robot itself and reduce the final foraging efficiency. A motivating example is a rescue scenario, in which the lack of a suitable solution results in many victims not being rescued after all available robots have been destroyed. Foraging robots face a dilemma after some robots have been destroyed: whether to take over tasks of the destroyed robots or continue executing their remaining foraging tasks. The challenges that arise when attempting such a balance are twofold: (1) the loss of robots adds new constraints to traditional problems, complicating the structure of the solution space, and (2) the task allocation strategy in a multirobot team affects the final expected utility, thereby increasing the dimension of the solution space. In this study, we address these challenges in two fundamental environmental settings: homogeneous and heterogeneous cases. For the former case, a decomposition and grafting mechanism is adopted to split this problem into two weakly coupled problems: the foraging task execution problem and the foraging task allocation problem. We propose an exact foraging task allocation algorithm, and graft it to another exact foraging task execution algorithm to find an optimal solution within the polynomial time. For the latter case, it is proven \( \mathcal {NP} \)-hard to find an optimal solution in polynomial time. The decomposition and grafting mechanism is also adopted here, and our proposed greedy risk-aware foraging algorithm is grafted to our proposed hierarchical agglomerative clustering algorithm to find high-utility solutions with low computational overhead. Finally, these algorithms are extensively evaluated through simulations, demonstrating that compared with various benchmarks, they can significantly increase the utility of objects returned by robots before all the robots have been stopped.

REFERENCES

  1. [1] Agmon Noa. 2017. Robotic strategic behavior in adversarial environments. In Proceedings of the International Joint Conference on Artificial Intelligence. 51065110.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Agmon Noa, Kaminka Gal A., and Kraus Sarit. 2011. Multi–robot adversarial patrolling: Facing a full-knowledge opponent. J. Artif. Intell. Res. 42, 1 (2011), 887916.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Ai The Jin and Kachitvichyanukul Voratas. 2009. A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Comput. Operat. Res. 36, 5 (2009), 16931702.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. [4] Alers Sjriek, Bloembergen Daan, Hennes Daniel, Jong Steven De, Kaisers Michael, Lemmens Nyree, Tuyls Karl, and Weiss Gerhard. 2011. Bee-inspired foraging in an embodied swarm. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems. 13111312.Google ScholarGoogle Scholar
  5. [5] Archetti Claudia, Bianchessi Nicola, and Speranza M. Grazia. 2013. The capacitated team orienteering problem with incomplete service. Optimiz. Lett. 7, 7 (2013), 14051417.Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Arslan Omur, Guralnik Dan P., and Koditschek Daniel E.. 2016. Coordinated robot navigation via hierarchical clustering. IEEE Trans. Robot. 32, 2 (2016), 352371.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Baker Chris A. B., Ramchurn Sarvapali, Teacy W. T. Luke, and Jennings Nicholas R.. 2016. Planning search and rescue missions for UAV teams. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems. 17771782.Google ScholarGoogle Scholar
  8. [8] Balch Tucker and Hybinette Maria. 2000. Social potentials for scalable multi–robot formations. In Proceedings of the International Conference on Robotics and Automation. 7380.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Beck Zoltán, Teacy Luke, Rogers Alex, and Jennings Nicholas R.. 2016. Online planning for collaborative search and rescue by heterogeneous robot teams. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems. 10241033.Google ScholarGoogle Scholar
  10. [10] Bellman Richard. 1966. Dynamic programming. Science 153, 3731 (1966), 3437.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Campbell Ann M., Gendreau Michel, and Thomas Barrett W.. 2011. The orienteering problem with stochastic travel and service times. Ann. Operat. Res. 186, 1 (2011), 6181.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Cao Zhiguang, Guo Hongliang, and Zhang Jie. 2017. A multiagent-based approach for vehicle routing by considering both arriving on time and total travel time. ACM Trans. Intell. Syst. Technol. 9, 3 (2017), 121.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Castello Eduardo, Yamamoto Tomoyuki, Libera Fabio Dalla, Liu Wenguo, Winfield Alan F. T., Nakamura Yutaka, and Ishiguro Hiroshi. 2016. Adaptive foraging for simulated and real robotic swarms: The dynamical response threshold approach. Swarm Intell. 10, 1 (2016), 131.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Chatzistergiou Andreas and Viglas Stratis D.. 2014. Fast heuristics for near-optimal task allocation in data stream processing over clusters. In Proceedings of the ACM International Conference on Conference on Information and Knowledge Management. 15791588.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Chen Cen, Cheng Shih-Fen, and Lau Hoong Chuin. 2014. Multi-agent orienteering problem with time-dependent capacity constraints. Web Intell. Agent Syst. 12, 4 (2014), 347358.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Choi Han-Lim, Brunet Luc, and How Jonathan P.. 2009. Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25, 4 (2009), 912926.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Chung Jen Jen, Smith Andrew J., Skeele Ryan, and Hollinger Geoffrey A.. 2019. Risk-aware graph search with dynamic edge cost discovery. Int. J. Robot. Res. 38, 2-3 (2019), 182195.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Cormen Thomas H., Leiserson Charles E., Rivest Ronald L., and Stein Clifford. 2009. Introduction to Algorithms. MIT Press, Cambridge, MA.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Dantzig George B. and Ramser John H.. 1959. The truck dispatching problem. Manage. Sci. 6, 1 (1959), 8091.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Deneubourg J. L., Aron Serge, Goss Simon, and Pasteels Jacques M.. 1990. The self-organising exploratory pattern of the argentine ant. J. Insect Behav. 3, 2 (1990), 159168.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] DeRonne Kevin W. and Karypis George. 2013. Pareto optimal pairwise sequence alignment. IEEE/ACM Trans. Comput. Biol. Bioinf. 10, 2 (2013), 481493.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Ding Chris and He Xiaofeng. 2002. Cluster merging and splitting in hierarchical clustering algorithms. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 139146.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Dinh Thai, Fukasawa Ricardo, and Luedtke James. 2018. Exact algorithms for the chance-constrained vehicle routing problem. Math. Program. 172, 1 (2018), 105138.Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Erdoǧan Güneş and Laporte Gilbert. 2013. The orienteering problem with variable profits. Networks 61, 2 (2013), 104116.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Feinerman Ofer and Traniello James F. A.. 2016. Social complexity, diet, and brain evolution: Modeling the effects of colony size, worker size, brain size, and foraging behavior on colony fitness in ants. Behav. Ecol. Sociobiol. 70, 7 (2016), 10631074.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Fridman Natalie, Amir Doron, Douchan Yinon, and Agmon Noa. 2019. Satellite detection of moving vessels in marine environments. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 94529459.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Garnier Simon, Tache Faben, Combe Maud, Grimal Anne, and Theraulaz Guy. 2007. Alice in pheromone land: An experimental setup for the study of ant-like robots. In Proceedings of the Swarm Intelligence Symposium. IEEE, 3744.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] Gautrais Jacques, Jost Christian, and Theraulaz Guy. 2008. Key behavioural factors in a self-organised fish school model. In Annales Zoologici Fennici, Vol. 45. BioOne, 415428.Google ScholarGoogle Scholar
  29. [29] Gendreau Michel, Laporte Gilbert, and Séguin René. 1996. Stochastic vehicle routing. Eur. J. Operat. Res. 88, 1 (1996), 312.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Ghiani Gianpaolo and Improta Gennaro. 2000. An efficient transformation of the generalized vehicle routing problem. Eur. J. Operat. Res. 122, 1 (2000), 1117.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Golden Bruce L., Raghavan Subramanian, and Wasil Edward A.. 2008. The Vehicle Routing Problem: Latest Advances and New Challenges. Vol. 43. Springer Science & Business Media.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Huber Peter J.. 2004. Robust Statistics. Vol. 523. John Wiley & Sons.Google ScholarGoogle Scholar
  33. [33] Iliadis Ilias. 2000. Optimal PNNI complex node representations for restrictive costs and minimal path computation time. IEEE/ACM Trans. Netw. 8, 4 (2000), 493506.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Johnson David S.. 1985. The np-completeness column: An ongoing guide. J. Algor. 6, 3 (1985), 434451.Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Jorgensen Stefan, Chen Robert H., Milam Mark B., and Pavone Marco. 2018. The team surviving orienteers problem: Routing teams of robots in uncertain environments with survival constraints. Auton. Robots 42, 4 (2018), 927952.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Khan Asif, Yanmaz Evsen, and Rinner Bernhard. 2014. Information merging in multi-UAV coperative search. In Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, 31223129.Google ScholarGoogle Scholar
  37. [37] Kitjacharoenchai Patchara, Ventresca Mario, Moshref-Javadi Mohammad, Lee Seokcheon, Tanchoco Jose M. A., and Brunese Patrick A.. 2019. Multiple traveling salesman problem with drones: Mathematical model and heuristic approach. Comput. Industr. Eng. 129 (2019), 1430.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. [38] Kulić Dana, Takano Wataru, and Nakamura Yoshihiko. 2008. Incremental learning, clustering and hierarchy formation of whole body motion patterns using adaptive hidden markov chains. Int. J. Robot. Res. 27, 7 (2008), 761784.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] Labadie Nacima, Melechovskỳ Jan, and Calvo Roberto Wolfler. 2011. Hybridized evolutionary local search algorithm for the team orienteering problem with time windows. J. Heurist. 17, 6 (2011), 729753.Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Laporte Gilbert, Asef-Vaziri Ardavan, and Sriskandarajah Chelliah. 1996. Some applications of the generalized travelling salesman problem. J. Operat. Res. Soc. 47, 12 (1996), 14611467.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Lerman Kristina, Jones Chris, Galstyan Aram, and Matarić Maja J.. 2006. Analysis of dynamic task allocation in multi-robot systems. Int. J. Robot. Res. 25, 3 (2006), 225241.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. [42] Liemhetcharat Somchaya, Yan Rui, Yan Rui, and Tee Keng Peng. 2015. Continuous foraging and information gathering in a multi-agent team. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems. 13251333.Google ScholarGoogle Scholar
  43. [43] Lin Efrat Sless, Agmon Noa, and Kraus Sarit. 2019. Multi-robot adversarial patrolling: Handling sequential attacks. Artif. Intell. 274 (2019), 125.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Lu Qi, Hecker Joshua P., and Moses Melanie E.. 2016. The MPFA: A multiple-place foraging algorithm for biologically-inspired robot swarms. In Proceedings of the International Conference on Intelligent Robots and Systems. IEEE, 38153821.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Neamatollahi Peyman, Abrishami Saeid, Naghibzadeh Mahmoud, Moghaddam Mohammad Hossein Yaghmaee, and Younis Ossama. 2017. Hierarchical clustering-task scheduling policy in cluster-based wireless sensor networks. IEEE Trans. Industr. Inf. 14, 5 (2017), 18761886.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Nikolova Evdokia and Karger David R.. 2008. Route planning under uncertainty: The canadian traveller problem. In Proceedings of the AAAI Conference on Artificial Intelligence. 969974.Google ScholarGoogle Scholar
  47. [47] Panait Liviu and Luke Sean. 2004. A pheromone-based utility model for collaborative foraging. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems. IEEE, 3643.Google ScholarGoogle Scholar
  48. [48] Parrish Julia K., Viscido Steven V., and Grunbaum Daniel. 2002. Self-organized fish schools: An examination of emergent properties. Biol. Bull. 202, 3 (2002), 296305.Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Pillac Victor, Gendreau Michel, Guéret Christelle, and Medaglia Andrés L.. 2013. A review of dynamic vehicle routing problems. Eur. J. Operat. Res. 225, 1 (2013), 111.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Pitonakova Lenka, Crowder Richard, and Bullock Seth. 2016. Information flow principles for plasticity in foraging robot swarms. Swarm Intell. 10, 1 (2016), 3363.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Pitre Ryan R., Li X. Rong, and Delbalzo R.. 2012. UAV route planning for joint search and track missions–an information-value approach. IEEE Trans. Aerosp. Electr. Syst. 48, 3 (2012), 25512565.Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Psaraftis Harilaos N., Wen Min, and Kontovas Christos A.. 2016. Dynamic vehicle routing problems: Three decades and counting. Networks 67, 1 (2016), 331.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. [53] Raine Nigel E., Ings Thomac C., Dornhaus Anna, Saleh Nehal, and Chittka Lars. 2006. Adaptation, genetic drift, pleiotropy, and history in the evolution of bee foraging behavior. Adv. Study Behav. 36 (2006), 305354.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Ramchurn Sarvapali D., Farinelli Alessandro, Macarthur Kathryn S., and Jennings Nicholas R.. 2010. Decentralized coordination in robocup rescue. Comput. J. 53, 9 (2010), 14471461.Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Rao N. S. V., Stoltzfus Neal, and Iyengar S. S.. 1991. A ‘Retraction’ method for learned navigation in unknown terrains for a circular robot. IEEE Trans. Robot. Autom. 7, 5 (1991), 699707.Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Reinelt Gerhard. 1991. TSPLIB—A traveling salesman problem library. ORSA J. Comput. 3, 4 (1991), 376384.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Sarbu Costel, Zehl Katharina, and Einax Jürgen W.. 2007. Fuzzy divisive hierarchical clustering of soil data using gustafson–kessel algorithm. Chemometr. Intell. Lab. Syst. 86, 1 (2007), 121129.Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Shapira Yaniv and Agmon Noa. 2015. Path planning for optimizing survivability of multi-robot formation in adversarial environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 45444549.Google ScholarGoogle Scholar
  59. [59] Shell Dylan A. and Matarić Maja J.. 2006. On foraging strategies for large-scale multi-robot systems. In Proceedings of the International Conference on Intelligent Robots and Systems. 27172723.Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Simonin Olivier, Charpillet François, and Thierry Eric. 2014. Revisiting wavefront construction with collective agents: An approach to foraging. Swarm Intell. 8, 2 (2014), 113138.Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Sless Efrat, Agmon Noa, and Kraus Sarit. 2014. Multi-robot adversarial patrolling: Facing coordinated attacks. In Proceedings of the International Conference on Autonomous Agents & Multiagent Systems. 10931100.Google ScholarGoogle Scholar
  62. [62] Talamali Mohamed S., Bose Thomas, Haire Matthew, Xu Xu, Marshall James A. R., and Reina Andreagiovanni. 2019. Sophisticated collective foraging with minimalist agents: A swarm robotics test. Int. J. Robot. Res. 35, 12 (2019), 132.Google ScholarGoogle Scholar
  63. [63] Thammawichai Mason, Baliyarasimhuni Sujit P., Kerrigan Eric C., and Sousa João B.. 2017. Optimizing communication and computation for multi-UAV information gathering applications. IEEE Trans. Aerosp. Electr. Syst. 54, 2 (2017), 601615.Google ScholarGoogle ScholarCross RefCross Ref
  64. [64] Tillman Frank A.. 1969. The multiple terminal delivery problem with probabilistic demands. Transport. Sci. 3, 3 (1969), 192204.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. [65] Toscano Benjamin J., Gownaris Natasha J., Heerhartz Sarah M., and Monaco Cristián J.. 2016. Personality, foraging behavior and specialization: Integrating behavioral and food web ecology at the individual level. Oecologia 182, 1 (2016), 5569.Google ScholarGoogle ScholarCross RefCross Ref
  66. [66] Vansteenwegen Pieter, Souffriau Wouter, and Oudheusden Dirk Van. 2011. The orienteering problem: A survey. Eur. J. Operat. Res. 209, 1 (2011), 110.Google ScholarGoogle ScholarCross RefCross Ref
  67. [67] Wagner Israel A., Lindenbaum Michael, and Bruckstein Alfred M.. 1998. Efficiently searching a graph by a smell-oriented vertex process. Ann. Math. Artif. Intell. 24, 1 (1998), 211223.Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. [68] Wagner Israel A., Lindenbaum Michael, and Bruckstein Alfred M.. 2000. Mac versus PC: Determinism and randomness as complementary approaches to robotic exploration of continuous unknown domains. Int. J. Robot. Res. 19, 1 (2000), 1231.Google ScholarGoogle ScholarCross RefCross Ref
  69. [69] Winfield Alan F. T.. 2009. Foraging robots. In Encyclopedia of Complexity and Systems Science, 36823700.Google ScholarGoogle ScholarCross RefCross Ref
  70. [70] Winfield Alan F. T.. 2009. Towards an engineering science of robot foraging. Distrib. Auton. Robot. Syst. (2009), 185192.Google ScholarGoogle Scholar
  71. [71] Xiong Tengke, Wang Shengrui, Mayers André, and Monga Ernest. 2012. DHCC: Divisive hierarchical clustering of categorical data. Data Min. Knowl. Discov. 24, 1 (2012), 103135.Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. [72] Yehoshua Roi and Agmon Noa. 2015. Adversarial modeling in the robotic coverage problem. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems. 17.Google ScholarGoogle Scholar
  73. [73] Yehoshua Roi and Agmon Noa. 2016. Multi-robot adversarial coverage. In Proceedings of the European Conference on Artificial Intelligence. 14931501.Google ScholarGoogle Scholar
  74. [74] Yehoshua Roi, Agmon Noa, and Kaminka Gal A.. 2016. Robotic adversarial coverage of known environments. Int. J. Robot. Res. 35, 12 (2016), 14191444.Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. [75] Zhang Xiaomei, Wu Yibo, Huang Lifu, Ji Heng, and Cao Guohong. 2019. Expertise-aware truth analysis and task allocation in Mobile crowdsourcing. IEEE Trans. Mobile Comput. (2019).Google ScholarGoogle Scholar

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        cover image ACM Transactions on Autonomous and Adaptive Systems
        ACM Transactions on Autonomous and Adaptive Systems  Volume 16, Issue 3-4
        December 2021
        150 pages
        ISSN:1556-4665
        EISSN:1556-4703
        DOI:10.1145/3543993
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        Publication History

        • Published: 6 July 2022
        • Online AM: 26 March 2022
        • Revised: 1 January 2022
        • Accepted: 1 January 2022
        • Received: 1 April 2021
        Published in taas Volume 16, Issue 3-4

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