2021/08/14 by Jianyu Chen, Chen, Jianyu, Yutaka Shimizu +7 · 1 citation
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Electrical engineering #Fuzzy Systems and Optimization #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2108.06533
openalex publication_date 2021/08/14 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
Motion planning under uncertainty is of significant importance for safety-critical systems such as autonomous vehicles. Such systems have to satisfy necessary constraints (e.g., collision avoidance) with potential uncertainties coming from either disturbed system dynamics or noisy sensor measurements. However, existing motion planning methods cannot efficiently find the robust optimal solutions under general nonlinear and non-convex settings. In this paper, we formulate such problem as chance-constrained Gaussian belief space planning and propose the constrained iterative Linear Quadratic Gaussian (CILQG) algorithm as a real-time solution. In this algorithm, we iteratively calculate a Gaussian approximation of the belief and transform the chance-constraints. We evaluate the effectiveness of our method in simulations of autonomous driving planning tasks with static and dynamic obstacles. Results show that CILQG can handle uncertainties more appropriately and has faster computation time than baseline methods.