3D multi-robot patrolling with a two-level coordination strategy
Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks.
Keywords3D patrolling 3D multi-robot systems Distributed multi-robot coordination UGVs
This work was supported by the European Union’s Seventh Framework Programme for research, technological development and demonstration under the TRADR Project No. FP7-ICT-609763.
- Acevedo, J. J., Arrue, B. C., Daz-Bez, J. M., Ventura, I., Maza, I., & Ollero, A. (2013). Decentralized strategy to ensure information propagation in area monitoring missions with a team of UAVs under limited communications. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 565–574).Google Scholar
- Agmon, N., Kaminka, G. A., & Kraus, S. (2014). Multi-robot adversarial patrolling: Facing a full-knowledge opponent. CoRR abs/1401.3903.Google Scholar
- Agmon, N., Kraus, S., & Kaminka, G. A. (2008a). Multi-robot perimeter patrol in adversarial settings. In ICRA (pp. 2339–2345).Google Scholar
- Agmon, N., Sadov, V., Kaminka, G. A., & Kraus, S. (2008b). The impact of adversarial knowledge on adversarial planning in perimeter patrol. In Proceedings of the 7th international joint conference on autonomous agents and multiagent systems—Volume 1, AAMAS’08 (pp. 55–62). International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
- Ahmadi, M., & Stone, P. (2006). A multi-robot system for continuous area sweeping tasks. In ICRA (pp. 1724–1729).Google Scholar
- Andrade, R. D. C., Macedo, H. T., Ramalho, G. L., & Ferraz, C. A. (2001). Distributed mobile autonomous agents in network management. In Proceedings of international conference on parallel and distributed processing techniques and applications.Google Scholar
- Bereg, S., Caraballo, L. E., Díaz-Báñez, J. M., & Lopez, M. A. (2016). Resilience of a synchronized multi-agent system. ArXiv e-prints.Google Scholar
- Cabrita, G., Sousa, P., Marques, L., & De Almeida, A. (2010). Infrastructure monitoring with multi-robot teams. In IROS (pp. 18–22).Google Scholar
- Caccamo, S., Parasuraman, R., Freda, L., Gianni, M., & Ögren, P. (2017). Rcamp: A resilient communication-aware motion planner for mobile robots with autonomous repair of wireless connectivity. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE.Google Scholar
- Chevaleyre, Y. (2004). Theoretical analysis of the multi-agent patrolling problem. In Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology (pp. 302–308).Google Scholar
- Colas, F., Mahesh, S., Pomerleau, F., Liu, M., & Siegwart, R. (2013). 3D path planning and execution for search and rescue ground robots. In 2013 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 722–727). IEEE.Google Scholar
- Diankov, R., Kuffner, J. (2007). Randomized statistical path planning. In IEEE/RSJ international conference on intelligent robots and systems. IROS 2007 (pp. 1–6). IEEE.Google Scholar
- Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., et al. (2011). On the segmentation of 3D lidar point clouds. In ICRA.Google Scholar
- Dubé, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., & Cadena, C. (2017a). Segmatch: Segment based place recognition in 3D point clouds. In ICRA (pp. 5266–5272). IEEE.Google Scholar
- Dubé, R., Gawel, A., Sommer, H., Nieto, J., Siegwart, R., & Cadena, C. (2017b). An online multi-robot slam system for 3D lidars. In IROS.Google Scholar
- Elmaliach, Y., Agmon, N., & Kaminka, G. A. (2007). Multi-robot area patrol under frequency constraints. In ICRA (pp. 385–390).Google Scholar
- Ferri, F., Gianni, M., Menna, M., & Pirri, F. (2014). Point cloud segmentation and 3D path planning for tracked vehicles in cluttered and dynamic environments. In Proceedings of the 3rd IROS Workshop on Robots in Clutter: Perception and Interaction in Clutter.Google Scholar
- Iocchi, L., Marchetti, L., & Nardi, D. (2011). Multi-robot patrolling with coordinated behaviours in realistic environments. In IROS (pp. 2796–2801).Google Scholar
- Jung, M. F., Beane, M., Forlizzi, J., Murphy, R., & Vertesi, J. (2017). Robots in group context: Rethinking design, development and deployment. In Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems (pp. 1283–1288). ACM.Google Scholar
- Karaman, S., & Frazzoli, E. (2010). Incremental sampling-based algorithms for optimal motion planning. Robotics Science and Systems VI, 104, 2.Google Scholar
- Kruijff, G. J. M., Pirri, F., Gianni, M., Papadakis, P., Pizzoli, M., Sinha, A., et al. (2012). Rescue robots at earthquake-hit Mirandola, Italy: A field report. In 2012 IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 1–8). IEEE.Google Scholar
- Kruijff-Korbayová, I., Freda, L., Gianni, M., Ntouskos, V., Hlaváč, V., Kubelka, V., et al. (2016). Deployment of ground and aerial robots in earthquake-struck amatrice in Italy (brief report). In 2016 IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 278–279). IEEE.Google Scholar
- LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press, http://planning.cs.uiuc.edu/. Accessed Dec 2018.
- Machado, A., Ramalho, G., Zucker, J. D., & Drogoul, A. (2002). Multi-agent patrolling: An empirical analysis of alternative architectures. In International workshop on multi-agent systems and agent-based simulation (pp. 155–170). Springer.Google Scholar
- Menna, M., Gianni, M., Ferri, F., & Pirri, F. (2014). Real-time autonomous 3D navigation for tracked vehicles in rescue environments. In IROS (pp. 696–702).Google Scholar
- Pippin, C., & Christensen, H. (2014). Trust modeling in multi-robot patrolling. In ICRA (pp. 59–66).Google Scholar
- Portugal, D. (2017). patrolling\(\_\)sim - Multi-Robot Patrolling Stage/ROS Simulation Package, http://wiki.ros.org/patrolling_sim. Accessed February 20, 2017.
- Portugal, D., & Rocha, R. (2010). Msp algorithm: Multi-robot patrolling based on territory allocation using balanced graph partitioning. In Proceedings of the 2010 ACM symposium on applied computing (pp. 1271–1276). New York, NY, USA: ACM.Google Scholar
- Portugal, D., & Rocha, R. (2011). A survey on multi-robot patrolling algorithms. In Technological Innovation for Sustainability (pp. 139–146).Google Scholar
- Portugal, D., & Rocha, R. P. (2013c). Retrieving topological information for mobile robots provided with grid maps (pp. 204–217). Berlin: Springer.Google Scholar
- Portugal, D., & Rocha, R. P. (2013d). Scalable, fault-tolerant and distributed multi-robot patrol in real world environments. In IROS (pp. 4759–4764).Google Scholar
- Rohmer, E., Singh, S. P. N., & Freese, M. (2013). V-rep: A versatile and scalable robot simulation framework. In Proceedings of The International Conference on Intelligent Robots and Systems (IROS).Google Scholar
- Sak, T., Wainer, J., & Goldenstein, S. K. (2008). Probabilistic multiagent patrolling (pp. 124–133). Berlin: Springer.Google Scholar
- Santana, H., Ramalho, G., Corruble, V., & Ratitch, B. (2004). Multi-agent patrolling with reinforcement learning. In Proceedings of the 3rd international joint conference on autonomous agents and multiagent systems—Volume 3, AAMAS’04 (pp. 1122–1129). IEEE Computer Society.Google Scholar
- Schwarz, M. (2017). nimbro\(\_\)network - ROS transport for high-latency, low-quality networks, https://github.com/AIS-Bonn/nimbro_network. Accessed February 20, 2017.
- Sempé, F., & Drogoul, A. (2003). Adaptive patrol for a group of robots. In 2003 IEEE/RSJ international conference on intelligent robots and systems. (IROS 2003). Proceedings (Vol. 3, pp. 2865–2869). IEEE.Google Scholar
- Shahriari, M., & Biglarbegian, M. (2016). A new conflict resolution method for multiple mobile robots in cluttered environments with motion-liveness. IEEE Transactions on Cybernetics, PP(99), 1–12.Google Scholar
- Walcott-Bryant, A., Kaess, M., Johannsson, H., & Leonard, J. J. (2012). Dynamic pose graph slam: Long-term mapping in low dynamic environments. In 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1871–1878). IEEE.Google Scholar
- Worst, R., Dubé, R., Svoboda, T., Freda, L., et al. (2017). Dr 6.3: Multi-robot task adaptation, http://www.tradr-project.eu/wp-content/uploads/dr.6.3.main_public.pdf. TRADR deliverable. Accessed April 15, 2018.
- Worst, R., Zimmermann, E., Reuter, D., et al. (2018). Dr 6.4: Persistence in long-term human-robot teaming for robot assisted disaster response, http://www.tradr-project.eu/wp-content/uploads/dr.6.4.main_public.pdf. TRADR deliverable. Accessed October 13, 2018.
- Yan, C., & Zhang, T. (2016). Multi-robot patrol: A distributed algorithm based on expected idleness. International Journal of Advanced Robotic Systems, 13(6), 1729881416663,666.Google Scholar
- Yehoshua, R., Agmon, N., & Kaminka, G. A. (2013). Robotic adversarial coverage: Introduction and preliminary results. In IROS (pp. 6000–6005).Google Scholar
- Zimmermann, K., Zuzanek, P., Reinstein, M., & Hlavac, V. (2014). Adaptive traversability of unknown complex terrain with obstacles for mobile robots. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 5177–5182). IEEE.Google Scholar