Risk-Aware Path Planning Under Uncertainty in Dynamic Environments

This study develops a novel sampling-based path planning approach, simultaneously achieving uncertainty reduction of localization and avoidance of dynamic obstacles. The proposed path planner can generate a set of path primitives and the path selection takes into account the localization uncertainty, the collision-risk, and the cost-to-go to the target area. The weights of these quantities for selecting an optimal path are tuned adaptively by using the entropy weight method. In the process of path primitive generation, we propose an adaptive planning horizon scheme that can generate a longer path with lower localization uncertainty. Particularly, to further reduce the localization uncertainty of the path primitive, we propose a sampling strategy that is capable of biasing the sampling points to the information-rich areas. To reduce the collision-risk, we propose to calculate the probability of collision by taking the uncertainty of both the robot and the dynamic objects into consideration. The proposed approach and its key components are verified in extensive experiments in both simulation and real-world environments. The proposed method is demonstrated to be capable of efficiently guiding the robot to the designated location with lower localization uncertainty and higher success rate in obstacle avoidance.