Alberto Viseras
ABSTRACT
Multi-robot teams can play a crucial role in many applications such as exploration, or search and rescue operations. One of the most important problems within the multi-robot context is path planning. This has been shown to be particularly challenging, as the team of robots must deal with additional constraints, e.g. inter-robot collision avoidance, while searching in a much larger action space. Previous works have proposed solutions to this problem, but they present two major drawbacks: (i) algorithms suffer from a high computational complexity, or (ii) algorithms require a communication link between any two robots within the system. This paper presents a method to solve this problem, which is both computationally efficient and only requires local communication between neighboring agents. We formulate the multirobot path planning as a distributed constraint optimization problem. Specifically, in our approach the asynchronous distributed constraint optimization algorithm (Adopt) [15] is combined with sampling-based planners to obtain collision free paths, which allows us to take into account both kinematic and kinodynamic constraints of the individual robots. The paper analyzes the performance and scalability of the approach using simulations, and presents real experiments employing a team of several robots.
- B. Awerbuch. A new distributed depth-first-search algorithm. Information Processing Letters, 20(3):147--150, 1985. Google Scholar
Cross Ref
- N. Ayanian and V. Kumar. Decentralized feedback controllers for multiagent teams in environments with obstacles. IEEE Transactions on Robotics, 26(5):878--887, 2010. Google ScholarDigital Library
- V. R. Desaraju, J. P. How, V. R. Desaraju, and J. P. How. Decentralized path planning for multi-agent teams with complex constraints. Auton Robot, 32:385--403, 2012. Google ScholarDigital Library
- A. Farinelli, A. Rogers, A. Petcu, and N. R. Jennings. Decentralised coordination of low-power embedded devices using the max-sum algorithm. In Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems-Volume 2, pages 639--646. International Foundation for Autonomous Agents and Multiagent Systems, 2008. Google ScholarDigital Library
- S. K. Gan, R. Fitch, and S. Sukkarieh. Online decentralized information gathering with spatial-temporal constraints. Autonomous Robots, 37(1):1--25, 2014. Google ScholarCross Ref
- D. Habib, H. Jamal, and S. A. Khan. Employing multiple unmanned aerial vehicles for co-operative path planning. International Journal of Advanced Robotic Systems, 10:1--10, 2013. Google ScholarCross Ref
- M. A. Hsieh, A. Cowley, J. F. Keller, L. Chaimowicz, B. Grocholsky, V. Kumar, C. J. Taylor, Y. Endo, R. C. Arkin, B. Jung, D. F. Wolf, G. S. Sukhatme, and D. C. MacKenzie. Adaptive teams of autonomous aerial and ground robots for situational awareness. Journal of Field Robotics, 24:991--1014, 2007. Google ScholarDigital Library
- K. Kim, J. Campbell, W. Duong, Y. Zhang, and G. Fainekos. DisCoF + : Asynchronous DisCoF with Flexible Decoupling for Cooperative Pathfinding in Distributed Systems. In IEEE International Conference on Automation Science and Engineering (CASE), pages 369--376. IEEE, 2015. Google ScholarCross Ref
- Y. Kuwata and J. P. How. Cooperative distributed robust trajectory optimization using receding horizon MILP. IEEE Transactions on Control Systems Technology, 19(2):423--431, 2011. Google ScholarCross Ref
- Y. Kuwata, J. Teo, S. Karaman, G. Fiore, E. Frazzoli, and J. P. How. Motion planning in complex environments using closed-loop prediction. In Proc. AIAA Guidance, Navigation, and Control Conf. and Exhibit, 2008. Google ScholarCross Ref
- S. M. LaValle and J. J. Kuffner. Randomized kinodynamic planning. The International Journal of Robotics Research, 20(5):378--400, 2001. Google ScholarCross Ref
- A. R. Leite, F. Enembreck, and J.-P. A. Barthès. Distributed constraint optimization problems: Review and perspectives. Expert Systems with Applications, 41(11):5139--5157, 2014. Google ScholarCross Ref
- D. Levine, B. Luders, and J. How. Information-Theoretic Motion Planning for Constrained Sensor Networks. Journal of Aerospace Information Systems, 10(10):476--496, 2013. Google ScholarCross Ref
- I. Maza, F. Caballero, J. Capitan, J. M. de Dios, and A. Ollero. A distributed architecture for a robotic platform with aerial sensor transportation and self-deployment capabilities. Journal of Field Robotics, 28(3):303--328, 2011. Google ScholarDigital Library
- P. J. Modi, W.-M. Shen, M. Tambe, and M. Yokoo. ADOPT: Asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence, 161(1):149--180, 2005. Google ScholarDigital Library
- O. Purwin, R. D'Andrea, and J. W. Lee. Theory and implementation of path planning by negotiation for decentralized agents. Robotics and Autonomous Systems, 56(5):422--436, 2008. Google ScholarDigital Library
- N. Santoro. Design and analysis of distributed algorithms, volume 56. John Wiley & Sons, 2006. Google ScholarDigital Library
- Z. G. Saribatur, E. Erdem, and V. Patoglu. Cognitive factories with multiple teams of heterogeneous robots: Hybrid reasoning for optimal feasible global plans. IEEE International Conference on Intelligent Robots and Systems, (Iros):2923--2930, 2014.Google ScholarCross Ref
- P. Scerri, S. Owens, B. Yu, and K. Sycara. A decentralized approach to space deconfliction. FUSION 2007 - 2007 10th International Conference on Information Fusion, 2007. Google ScholarCross Ref
- P. Surynek. An Optimization Variant of Multi-Robot Path Planning is Intractable. AAAI, pages 1261--1263, 2010. Google ScholarDigital Library
- J. Swigart and S. Lall. An explicit state space solution for a decentralized two-player optimal linear-quadratic regulator. Proc.\ American Control Conference (ACC.2010), 1:6385--6390, 2010.Google ScholarCross Ref
- J. van den Berg, J. Snoeyink, M. Lin, and D. Manocha. Centralized path planning for multiple robots: Optimal decoupling into sequential plans. Robotics: Science and Systems V, 2009.Google ScholarCross Ref
- P. Velagapudi, K. Sycara, and P. Scerri. Decentralized prioritized planning in large multirobot teams. IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, pages 4603--4609, 2010. Google ScholarCross Ref
- A. Viseras Ruiz, T. Wiedemann, C. Manss, L. Magel, J. Mueller, D. Shutin, and L. Merino. Decentralized multi-agent exploration with online-learning of gaussian processes. In Robotics and Automation (ICRA), 2016 IEEE International Conference on. IEEE, 2016. Google ScholarCross Ref
- G. Wagner and H. Choset. Subdimensional expansion for multirobot path planning. Artificial Intelligence, 219:1--24, 2015. Google ScholarDigital Library
- C. Wei, K. V. Hindriks, and C. M. Jonker. Multi-robot cooperative pathfinding: A decentralized approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8481 LNAI(PART 1):21--31, 2014. Google ScholarDigital Library
- Y. Zhang, K. Kim, and G. Fainekos. Discof: Cooperative pathfinding in distributed systems with limited sensing and communication range. 2016.Google Scholar
- Z. Ziyang, G. Chen, Z. Qiannan, and D. Ruyi. Cooperative Path Planning for Multiple UAVs Formation. The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, 210016:469--473, 2014.Google Scholar
Index Terms
- An asynchronous distributed constraint optimization approach to multi-robot path planning with complex constraints
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