Enabling Robots to Autonomously Search Dynamic Cluttered Post-Disaster Environments

Robots will bring search and rescue (SaR) in disaster response to another level, in case they can autonomously take over dangerous SaR tasks from humans. A main challenge for autonomous SaR robots is to safely navigate in cluttered environments with uncertainties, while avoiding static and moving obstacles. We propose an integrated control framework for SaR robots in dynamic, uncertain environments, including a computationally efficient heuristic motion planning system that provides a nominal (assuming there are no uncertainties) collision-free trajectory for SaR robots and a robust motion tracking system that steers the robot to track this reference trajectory, taking into account the impact of uncertainties. The control architecture guarantees a balanced trade-off among various SaR objectives, while handling the hard constraints, including safety. The results of various computer-based simulations, presented in this paper, showed significant out-performance (of up to 42.3%) of the proposed integrated control architecture compared to two commonly used state-of-the-art methods (Rapidly-exploring Random Tree and Artificial Potential Function) in reaching targets (e.g., trapped victims in SaR) safely, collision-free, and in the shortest possible time.

Model Predictive Fuzzy Control: A Hierarchical Multi-Agent Control Architecture for Outdoor Search-and-Rescue Robots

Autonomous robots deployed in unknown search-and-rescue (SaR) environments can significantly improve the efficiency of the mission by assisting in fast localisation and rescue of the trapped victims. We propose a novel integrated hierarchical control architecture, called model predictive fuzzy control (MPFC), for autonomous mission planning of multi-robot SaR systems that should efficiently map an unknown environment: We combine model predictive control (MPC) and fuzzy logic control (FLC), where the robots are locally controlled by computationally efficient FLC controllers, and the parameters of these local controllers are tuned via a centralised MPC controller, in a regular or event-triggered manner. The proposed architecture provides three main advantages: (1) The control decisions are made by the FLC controllers, thus the real-time computation time is affordable. (2) The centralised MPC controller optimises the performance criteria with a global and predictive vision of the system dynamics, and updates the parameters of the FLC controllers accordingly. (3) FLC controllers are heuristic by nature and thus do not take into account optimality in their decisions, while the tuned parameters via the MPC controller can indirectly incorporate some level of optimality in local decisions of the robots. A simulation environment for victim detection in a disaster environment was designed in MATLAB using discrete, 2-D grid-based models. While being comparable from the point of computational efficiency, the integrated MPFC architecture improves the performance of the multi-robot SaR system compared to decentralised FLC controllers. Moreover, the performance of MPFC is comparable to the performance of centralised MPC for path planning of SaR robots, whereas MPFC requires significantly less computational resources, since the number of the optimisation variables in the control problem are reduced.