Probabilistic motion planning among moving obstacles following typical motion patterns

Abstract

The paper presents a navigation algorithm for dynamic probabilistic environments. The static environment is unknown; moving pedestrians are detected and tracked on-line. Pedestrians are supposed to move along typical motion patterns represented by HMMs. The planning algorithm is based on an extension of the rapidly-exploring random tree algorithm, where the likelihood of the obstacles future trajectory and the probability of collision is explicitly taken into account. The algorithm is used in a partial motion planner, and the probability of collision is updated in real-time according to the most recent estimation. Results show the performance for a car-like robot in a simulated environment among multiple dynamic obstacles.