Abstract
Multi-robot systems are increasingly used in several industry automation and warehouse management applications, mostly with a centralized hub for coordination. Several decentralized infrastructures have been studied for using multi-robot systems in real mission scenarios like search-and-rescue, area coverage and exploration. However, despite designing rigorous methods for using multi-robot systems in a decentralized setting, long-term field deployments still seem unfeasible. The lack of proper infrastructure for tackling fault-detection is one of the great challenges in this regard. We propose FLAM (https://github.com/MISTLab/FLAM), a fault localization and mapping algorithm that detects faults in a robotic system and uses them to build a map of the environmental hazards, effectively providing risk-awareness to the robotic team.
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Burgard, W., Moors, M., Stachniss, C., Schneider, F.E.: Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005). https://ieeexplore.ieee.org/abstract/document/1435481
Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30552-1_2
Winfield, A.F., Nembrini, J.: Safety in numbers: fault-tolerance in robot swarms. Int. J. Model. Identif. Control 1(1), 30–37 (2006). https://www.inderscienceonline.com/doi/abs/10.1504/IJMIC.2006.008645
Bjerknes, J.D., Winfield, A.F.T.: On fault tolerance and scalability of swarm robotic systems. In: Martinoli, A., et al. (eds.) Distributed Autonomous Robotic Systems. STAR, vol. 83, pp. 431–444. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-32723-0_31
Yang, G.-Z., et al.: The grand challenges of Science Robotics. Sci. Robot. 3(14), eaar7650 (2018). https://www.science.org/doi/abs/10.1126/scirobotics.aar7650
Khalastchi, E., Kalech, M.: On fault detection and diagnosis in robotic systems. ACM Comput. Surv. 51(1), 1–24 (2018). https://doi.org/10.1145/3146389
Christensen, A.L.: Fault detection in autonomous robots. Ph.D., Université Libre de Bruxelles (2008)
Lau, H.K.: Error detection in swarm robotics: a focus on adaptivity to dynamic environments. Ph.D. dissertation, University of York (2012)
Tarapore, D., Lima, P.U., Carneiro, J., Christensen, A.L.: To err is robotic, to tolerate immunological: fault detection in multirobot systems. Bioinspiration Biomimetics 10(1), 016014 (2015)
Tarapore, D., Christensen, A.L., Timmis, J.: Generic, scalable and decentralized fault detection for robot swarms. PLoS ONE 12(8), e0182058 (2017)
Tarapore, D., Timmis, J., Christensen, A.L.: Fault detection in a swarm of physical robots based on behavioral outlier detection. IEEE Trans. Robot. 35(6), 1516–1522 (2019)
Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000)
Christensen, A.L., O’Grady, R., Birattari, M., Dorigo, M.: Fault detection in autonomous robots based on fault injection and learning. Auton. Robot. 24(1), 49–67 (2008). https://doi.org/10.1007/s10514-007-9060-9
O’Keeffe, J., Tarapore, D., Millard, A.G., Timmis, J.: Towards fault diagnosis in robot swarms: an online behaviour characterisation approach. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds.) TAROS 2017. LNCS (LNAI), vol. 10454, pp. 393–407. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64107-2_31
O’Keeffe, J., Tarapore, D., Millard, A.G., Timmis, J.: Adaptive online fault diagnosis in autonomous robot swarms. Front. Robot. AI 5, 131 (2018)
Hunt, E.R., Jenkinson, G., Wilsher, M., Dettmann, C.P., Hauert, S.: SPIDER: a bioinspired swarm algorithm for adaptive risk-taking. In: Artificial Life Conference Proceedings, pp. 44–51. MIT Press, Cambridge (2020)
Ono, M., Williams, B.C.: An efficient motion planning algorithm for stochastic dynamic systems with constraints on probability of failure. In: AAAI, pp. 1376–1382 (2008)
Vitus, M.P., Tomlin, C.J.: On feedback design and risk allocation in chance constrained control. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference, pp. 734–739. IEEE (2011)
Vielfaure, D., Arseneault, S., Lajoie, P.-Y., Beltrame, G.: DORA: distributed online risk-aware explorer (2021)
Amigoni, F., Banfi, J., Basilico, N.: Multirobot exploration of communication-restricted environments: a survey. IEEE Intell. Syst. 32(6), 48–57 (2017). https://ieeexplore.ieee.org/abstract/document/8267592
Otte, M.: An emergent group mind across a swarm of robots: collective cognition and distributed sensing via a shared wireless neural network. Int. J. Robot. Res. 37(9), 1017–1061 (2018)
Pinciroli, C., Lee-Brown, A., Beltrame, G.: A tuple space for data sharing in robot swarms. In: Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONETICS), pp. 287–294 (2016). https://carlo.pinciroli.net/pdf/Pinciroli:BICT2015.pdf
Pinciroli, C., Beltrame, G.: Buzz: a programming language for robot swarms. IEEE Softw. 33(4), 97–100 (2016)
Majcherczyk, N., Pinciroli, C.: SwarmMesh: a distributed data structure for cooperative multi-robot applications. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4059–4065. IEEE (2020). https://ieeexplore.ieee.org/abstract/document/9197403
Kobayashi, F., Sakai, S., Kojima, F.: Sharing of exploring information using belief measure for multi robot exploration. In: 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE 2002. Proceedings (Cat. No. 02CH37291), vol. 2, pp. 1544–1549 (2002)
Kobayashi, F., Sakai, S., Kojima, F.: Determination of exploration target based on belief measure in multi-robot exploration. In: Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No. 03EX694), vol. 3, pp. 1545–1550 (2003)
Indelman, V.: Cooperative multi-robot belief space planning for autonomous navigation in unknown environments. Auton. Robot. 42(2), 353–373 (2018). https://doi.org/10.1007/s10514-017-9620-6
Stachniss, C., Burgard, W.: Mapping and exploration with mobile robots using coverage maps. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), Las Vegas, Nevada, USA, vol. 1, pp. 467–472. IEEE (2003)
Pinciroli, C., et al.: ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5027–5034 (2011)
K-Team, “Khepera IV” (2021). https://www.k-team.com/khepera-iv
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Ricard, G., Vielfaure, D., Beltrame, G. (2024). FLAM: Fault Localization and Mapping. In: Bourgeois, J., et al. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-031-51497-5_5
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