Risk based motion planning and navigation in uncertain dynamic environment

Navigation in large dynamic spaces has been often adressed using deterministic representations, fast updating and reactive avoidance strategies. However, probabilistic representations are much more informative and their use in mapping and prediction methods improves the quality of obtained results. The paper proposes a new concept to integrate a probabilist collision risk function linking planning and navigation methods with the perception and the prediction of the dynamic environments. Moving obstacles are supposed to move along typical motion patterns represented by Gaussian Processes. The likelihood of the obstacles’ future trajectory and the probability of occupation are used to compute the risk of collision. The proposed planning algorithm is a sampling-based partial planner guided by the risk of collision. The perception and prediction information are updated on-line and reused by the planner. The decision takes into account the most recent estimation. Results show the performance for a robotic wheelchair in a simulated environment among multiple dynamic obstacles.