2019/07/17 by Luka Petrović, Petrović, Luka, Juraj Peršić +5
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.1907.07521
Accepted to European Conference on Mobile Robots (ECMR) 2019
arxiv created 2019/07/17 · openalex publication_date 2019/07/17 · arxiv updated 2019/07/18 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. Such methods efficiently find solutions even for high degree-of-freedom robots. However, a globally optimal solution is often intractable in practice and state-of-the-art trajectory optimization methods are thus prone to local minima, especially in cluttered environments. In this paper, we propose a novel motion planning algorithm that employs stochastic optimization based on the cross-entropy method in order to tackle the local minima problem. We represent trajectories as samples from a continuous-time Gaussian process and introduce heteroscedasticity to generate powerful trajectory priors better suited for collision avoidance in motion planning problems. Our experimental evaluation shows that the proposed approach yields a more thorough exploration of the solution space and a higher success rate in complex environments than a current Gaussian process based state-of-the-art trajectory optimization method, namely GPMP2, while having comparable execution time.