Safe Autonomous Exploration and Adaptive Path Planning Strategy Using Signed Distance Field

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 text 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 text real-world experiments on a text 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.