Hierarchical Integration of Model Predictive and Fuzzy Logic Control for Combined Coverage and Target-Oriented Search-and-Rescue via Robots with Imperfect Sensors

Abstract

AbstractSearch-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.