Enabling Robots to Autonomously Search Dynamic Cluttered Post-Disaster Environments

TL;DR

This paper presents an integrated control framework enabling autonomous robots to efficiently and safely search dynamic, cluttered post-disaster environments, improving rescue operations by handling uncertainties and obstacles.

Contribution

It introduces a novel combined control architecture with heuristic motion planning and robust tracking, outperforming existing methods in safety and efficiency.

Findings

Up to 42.3% better target reaching performance

Effective handling of uncertainties and dynamic obstacles

Superior safety and collision avoidance in simulations

Abstract

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.