Precise trajectory optimization for articulated wheeled vehicles in cluttered environments. (February 2016)

• Record Type: Journal Article
• Authors: Li, Bai; Shao, Zhijiang
• Is Part Of: Advances in engineering software. Volume 92(2016)
• Page Start: 40
• Page End: 47
• Publication Date: 2016-02
• Subjects: Articulated vehicle -- Trajectory planning -- Mesh generation -- Collision avoidance -- Large-scale optimization -- Computational guidance and control; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2015.10.008
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: An articulated vehicle trajectory optimizer is proposed. Constraint violations between every two adjacent collocation points are considered. A simple but accurate collision avoidance judgment is utilized. Large-scale constraints are incorporated into the optimization objective. Various optimization objectives can be handled by our trajectory optimizer uniformly. Abstract: Trajectory planning refers to planning a time-dependent path connecting the initial and final configurations with some special constraints simultaneously considered. It is a critical aspect in autonomously driving an articulated vehicle. In this paper, trajectory planning is formulated as a dynamic optimization problem that contains kinematic differential equations, mechanical/environmental constraints, boundary conditions and an optimization objective. The prevailing numerical methods for solving the formulated dynamic optimization problem commonly disregard the constraint satisfactions between every two adjacent discretized mesh points, thus resulting in failure when the planned motions are actually implemented. As a remedy for this limitation, the concept of minute mesh grid is proposed, which improves the constraint satisfactions between adjacent rough mesh points. On the basis of accurate penalty functions, large-scale constraints are successfully incorporated into the optimization criterion, thus transforming the dynamic optimization problem into a static one with simple bounds on the decision variables. Simulation results verify that our proposed methodology can provide accurate results and can deal with various optimization objectives uniformly.