Optimal Stochastic Vehicle Path Planning Using Covariance Steering

• DOI: 10.48550/arxiv.1809.03380
• Publication Date: 2018-01-01
• Authors: Kazuhide Okamoto; Panagiotis Tsiotras
• Keywords: 0209 industrial biotechnology; Optimization and Control (math.OC); FOS: Mathematics; 02 engineering and technology; Mathematics - Optimization and Control

This work addresses the problem of vehicle path planning in presence obstacles and uncertainties, which is a fundamental robotics. While many algorithms have been proposed for decades, them dealt with only deterministic environments or open-loop uncertainty, i.e., uncertainty system state not controlled and, typically, increases time due to exogenous disturbances, leads design potentially conservative nominal paths. In order deal disturbances reduce generally, lower-level feedback controller used. We conjecture that, if planner can consider closed-loop evolution it compute less but still feasible To this end, we develop new approach that based on optimal covariance steering, explicitly steers stochastic linear systems additive noise under non-convex chance constraints. The framework verified using simple numerical simulations.