Autonomous exploration in unknown environment has remained challenging due to unexpected collisions, stuckness and slowdowns around obstacles. This paper reports a novel approach based on Signed Distance Field (SDF), to optimize path planning algorithms and autonomous exploration strategy for safe and adaptive navigation in search and rescue missions. A quantitative criterion is established for evaluating the safety of planned trajectories. Simulation results show that the proposed SDF-A* path planner outperforms traditional methods with a 30.10% increase in path safety (i.e. average distance from robot to obstacles) and a 64.11% reduction in time consumption; The proposed SDF-based Safe Autonomous Exploration Strategy, combined with SDF-A* path planner, outperform traditional methods, leading to significant increases (47.06%) in path safety and reductions (44.75% and 15.32%) in exploration time and path length, respectively. The viability, efficiency, and safety of the proposed methods are further validated through real-world experiments on a three-wheeled differential steering robot equipped with Jetson Nano and RPLIDAR-A3 lidar. Results show that the proposed approach adapts to different indoor environments and map configurations without prior parameter settings.
INDEX TERMSPath planning; autonomous exploration; path safety; signed distance field.