Abstract
Search-and-rescue (SaR) in unknown environments is a crucial task with life-threatening risks. SaR requires precise, optimal, and fast decisions to be made. Robots are promising candidates expected to execute various SaR tasks autonomously. While humans use heuristics to effectively deal with uncertainties of SaR, optimisation of multiple objectives (e.g., the mission time, the area covered, the number of victims detected), in the presence of physical and control constraints, is a mathematical challenge that requires machine computations. Thus including both human-inspired and mathematical capabilities in decision making of SaR robots is highly desired. However, developing control approaches that exhibit both capabilities has been significantly ignored in literature. Moreover, coordinating the decisions of the robots in large-scale SaR missions with affordable computation costs is an open challenge. Finally, in real-life, due to defects (e.g., in the sensors of the robots) or environmental factors (e.g., smoke) data perceived by SaR robots may be prone to uncertainties. We introduce a hierarchical multi-agent control architecture that simultaneously provides the following advantages: exploiting non-homogeneous and imperfect perception capabilities of SaR robots; improving the global performance as it is provided by centralised controllers; computational efficiency and robustness to failure of the central controller as offered by decentralised control methods. The integrated structure of the proposed control framework allows to combine human-inspired and mathematical decision making methods, via respectively fuzzy logic and model predictive control, in a coordinated and computationally efficient way. Our results for various computer-based simulations show that while the area coverage with the proposed control approach is comparable to existing heuristic methods that are particularly developed for coverage-oriented SaR, our approach has a significantly better performance regarding locating the trapped victims. Furthermore, with comparable computation times, the proposed control approach successfully avoids conflicts that may appear in non-cooperative control methods. In summary, the proposed multi-agent control system is capable of combining coverage-oriented and target-oriented SaR in a balanced and coordinated way.
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Data Availability
The data points, files, and codes for creating the figures represented in the results of this article are available online at https://figshare.com/s/9762330a7473363433ab.
References
Casper, J., Murphy, R.R.: Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center. IEEE Trans. Syst. Man Cybern. B (Cybernetics) 33(3), 367–385 (2003)
Coburn, A.W., Spence, R.J.S., Pomonis, A.: Factors determining human casualty levels in earthquakes: mortality prediction in building collapse. In: Proceedings of the 10th World Conference on Earthquake Engineering, vol. 10, pp 5989–5994, Rotterdam, Netherlands (1992)
Riley, J.M., Endsley, M.R.: The hunt for situation awareness: Human-robot interaction in search and rescue. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 48, pp 693–697, Los Angeles, CA (2004)
Shimanski, C.: Situational awareness in search and rescue operations. In: International Technical Rescue Symposium (2005)
Chandarana, M., Hughes, D., Lewis, M., Sycara, K., Scherer, S.: Planning and monitoring multi-job type swarm search and service missions. J. Intell. Robot. Syst. 101(44), 1–14 (2021)
Hong, A., Igharoro, O., Liu, Y., Niroui, F., Nejat, G., Benhabib, B.: Investigating human-robot teams for learning-based semi-autonomous control in urban search and rescue environments. J. Intell. Robot. Syst. 94, 669–686 (2019)
Jamshidnejad, A., Frazzoli, E.: Adaptive optimal receding-horizon robot navigation via short-term policy development. In: 15th International Conference on Control, Automation, Robotics and Vision, pp 21–28. IEEE (2018)
Beck, Z., Teacy, W.L.T., Jennings, N.R., Rogers, A.C.: Online planning for collaborative search and rescue by heterogeneous robot teams. In: Proceedings of the International Conference on Autonomous Agents & Multiagent Systems, pp 1024–1033, Singapore (2016)
de Alcantara Andrade, F.A., Hovenburg, A.R., de Lima, L.N., Rodin, C.D., Johansen, T.A., Storvold, R., Correia, C.A.M., Haddad, D.B.: Autonomous unmanned aerial vehicles in search and rescue missions using real-time cooperative model predictive control. Sensors 19(19), 4067 (2019)
San Juan, V., Santos, M., Andújar, J.M.: Intelligent UAV map generation and discrete path planning for search and rescue operations. Complexity 2018 (2018)
Yao, P., Zhao, Z.: Improved Glasius bio-inspired neural network for target search by multi-agents. Inform. Sci. 568, 40–53 (2021)
Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. Robot. Auton. Syst. 61(12), 1258–1276 (2013)
Koenig, S., Liu, Y.: Terrain coverage with ant robots: a simulation study. In: Proceedings of the 5th International Conference on Autonomous Agents, pp 600–607, Montreal, Canada (2001)
Wagner, I.A., Altshuler, Y., Yanovski, V., Bruckstein, A.M.: Cooperative cleaners: A study in ant robotics. Int. J. Robot. Res. 27(1), 127–151 (2008)
Yang, S.X., Luo, C.: A neural network approach to complete coverage path planning. IEEE Trans. Syst. Man Cybern. B (Cybernetics) 34(1), 718–724 (2004)
Yang, Y., Polycarpou, M.M., Minai, A.A.: Multi-UAV cooperative search using an opportunistic learning method. J. Dyn. Syst. Measu. Control 129 (2007)
Tutsoy, O., Barkana, D.E., Balikci, K.: A novel exploration-exploitation-based adaptive law for intelligent model-free control approaches. IEEE Trans. Cybern., 1–9. https://doi.org/10.1109/TCYB.2021.3091680 (2021)
Tutsoy, O., Barkana, D.E., Colak, S.: Learning to balance a Nao robot using reinforcement learning with symbolic inverse kinematic. Trans. Inst. Meas. Control. 39(11), 1735–1748 (2017)
Arnold, R., Jablonski, J., Abruzzo, B., Mezzacappa, E.: Heterogeneous UAV multi-role swarming behaviors for search and rescue. In: IEEE Conference on Cognitive and Computational Aspects of Situation Management, pp 122–128, Victoria, BC, Canada (2020)
Farrokhsiar, M., Pavlik, G., Najjaran, H.: An integrated robust probing motion planning and control scheme: A tube-based MPC approach. Robot. Auton. Syst. 61(12), 1379–1391 (2013)
Hoy, M., Matveev, A.S., Savkin, A.V.: Collision free cooperative navigation of multiple wheeled robots in unknown cluttered environments. Robot. Auton. Syst. 60(10), 1253–1266 (2012)
Cooper, J.R.: Optimal multi-agent search and rescue using potential field theory. In: AIAA Scitech 2020 Forum, p 0879 (2020)
Paez, D., Romero, J.P., Noriega, B., Cardona, G.A., Calderon, J.M.: Distributed particle swarm optimization for multi-robot system in search and rescue operations. IFAC-PapersOnLine 54(4), 1–6 (2021)
Choi, H., Brunet, L., How, J.P.: Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)
Liu, Y., Nejat, G.: Robotic urban search and rescue: A survey from the control perspective. J. Intell. Robot. Syst. 72(2), 147–165 (2013)
Best, G., Hollinger, G.A.: Decentralised self-organising maps for multi-robot information gathering. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 4790–4797. IEEE/RSJ, Las Vegas (2020)
Otte, M., Kuhlman, M.J., Sofge, D.: Auctions for multi-robot task allocation in communication limited environments. Auton. Robot. 44, 547–584 (2020)
Kashino, Z., Nejat, G., Benhabib, B.: Aerial wilderness search and rescue with ground support. J. Intell. Robot. Syst. 99, 147–163 (2020)
Tol, D., Hoekstra, J., Jamshidnejad, A.: A bi-level local and global model predictive control architecture for air traffic management. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp 361–365 (2021)
Khamis, A.M., Elmogy, A.M., Karray, F.O.: Complex task allocation in mobile surveillance systems. J. Intell. Robot. Syst. 64(1), 33–55 (2011)
Elston, J., Frew, E.W.: Hierarchical distributed control for search and tracking by heterogeneous aerial robot networks. In: IEEE International Conference on Robotics and Automation, pp 170–175, Pasadena, CA, USA (2008)
Chandler, P.R., Pachter, M., Rasmussen, S.: UAV cooperative control. In: Proceedings of the American Control Conference, vol. 1, pp 50–55, Arlington, VA, USA (2001)
Grogan, S., Pellerin, R., Gamache, M.: The use of unmanned aerial vehicles and drones in search and rescue operations - a survey. In: Proceedings of the PROLOG, Hull, UK (2018)
Sampedro, C., Rodriguez-Ramos, A., Bavle, H., Carrio, A., de la Puente, P., Campoy, P.: A fully-autonomous aerial robot for search and rescue applications in indoor environments using learning-based techniques. J. Intell. Robot. Syst. 95, 601–627 (2019)
Krzysiak, R., Butail, S.: Information-based control of robots in search-and-rescue missions with human prior knowledge. IEEE Trans. Human-Mach. Syst. 52(1), 52–63 (2021)
Ganesan, S., Shakya, M., Aqueel, A.F., Nambiar, L.M.: Small disaster relief robots with swarm intelligence routing. In: Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief, pp 123–127, Kollam, India (2011)
Wang, W., Joshi, R., Kulkarni, A., Leong, W.K., Leong, B.: Feasibility study of mobile phone WiFi detection in aerial search and rescue operations. In: Proceedings of the 4th Asia-Pacific Workshop on Systems, pp 1–6, Singapore (2013)
Dousai, N.M.K., Lončarić, S.: Detecting humans in search and rescue operations based on ensemble learning. IEEE Access 10, 26481–26492 (2022)
Llasag, R., Marcillo, D., Grilo, C., Silva, C.: Human detection for search and rescue applications with UAVs and mixed reality interfaces. In: 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), pp 1–6 (2019)
Pinheiro, G.P.M., Miranda, R.K., Praciano, B.J.G., Santos, G.A., Mendonça, F.L.L., Javidi, E., da Costa, J.P.J., de Sousa, R.T.J.: Multi-sensor wearable health device framework for real-time monitoring of elderly patients using a mobile application and high-resolution parameter estimation. Frontiers in Human Neuroscience (2022)
Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)
Yen, J.Y.: Finding the k shortest loopless paths in a network. Manag. Sci. 17(11), 712–716 (1971)
Jamshidnejad, A., Papamichail, I., Papageorgiou, M., De Schutter, B.: Sustainable model-predictive control in urban traffic networks: Efficient solution based on general smoothening methods. IEEE Trans. Control Syst. Technol. 26(3), 813–827 (2018)
Diehl, M., Bock, H.G., Schlöder, J.P.: A real-time iteration scheme for nonlinear optimization in optimal feedback control. SIAM J. Control. Optim. 43(5), 1714–1736 (2005)
Kreinovich, V., Kosheleva, O., Shahbazova, S.N.: Why triangular and trapezoid membership functions: A simple explanation. In: Shahbazova, S.N., Sugeno, M., Kacprzyk, J. (eds.) Recent Developments in Fuzzy Logic and Fuzzy Sets: Dedicated to Lotfi A. Zadeh, pp 25–31. Springer (2020)
Funding
This research has been jointly supported by the NWO Talent Programme Veni project “Autonomous drones flocking for search-and-rescue” (18120), which has been financed by the Netherlands Organisation for Scientific Research (NWO), and by the TU Delft AI Labs & Talent programme.
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Author C. de Koning contributed to designing and implementing the experiments. Authors C. de Koning and A. Jamshidnejad contributed to the analysis and interpretation of the results, development of the theoretical contributions, and composition of the manuscript. Author C. de Koning prepared the first draft of the manuscript. Author A. Jamshidnejad supervised the study design, and has reviewed, edited, and prepared the final version of the paper. Supervision and management of the project, as well as the funding acquisition have been done by A. Jamshidnejad. Both authors have approved the final version of the manuscript.
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de Koning, C., Jamshidnejad, A. Hierarchical Integration of Model Predictive and Fuzzy Logic Control for Combined Coverage and Target-Oriented Search-and-Rescue via Robots with Imperfect Sensors. J Intell Robot Syst 107, 40 (2023). https://doi.org/10.1007/s10846-023-01833-2
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DOI: https://doi.org/10.1007/s10846-023-01833-2