Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian processes

— The paper describes a navigation algorithm for dynamic, uncertain environment. Moving obstacles are supposed to move on typical patterns which are pre-learned and are represented by Gaussian processes. The planning algorithm is based on an extension of the Rapidly-exploring Random Tree algorithm, where the likelihood of the obstacles 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 of the navigation algorithm for a car-like robot moving among dynamic obstacles with probabilistic trajectory prediction.

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Chiara Fulgenzi, Christopher Tay, Anne Spalanzani, Christian Laugier
• Keywords: Algorithm, IROS 2008, Navigation Algorithm, Random Tree Algorithm, Robotics