Abstract: The motion planning of an Autonomous Land Vehicle（ALV） in an obstacle environment is a nonholonomic constraint global optimization. For this optimization problem, this paper proposes an motion planning method based on parameterized kinematic model and an improved PSO algorithm. The kinematic model of ALV is divided into a parameterized arclength-curvature model and a velocity model. The PSO algorithm is improved by chaotic mapping method. The improved PSO algorithm is used in the parameter optimization of arclength-curvature model. The simulation result shows the effectiveness of the proposed method in solving the problem of ALV motion planning.

Key words: autonomous land vehicle, motion planning, parameterized model, particle swarm optimization

摘要： 自主地面车辆在障碍物环境下的运动规划问题是一个包含非完整约束条件的全局优化问题。针对该优化问题，提出了一种基于参数化运动模型和改进粒子群优化算法的运动规划方法。该方法将车辆运动模型解耦为参数化弧长-曲率模型和速度模型，并采用混沌映射方法对粒子群优化算法进行了改进，将改进的粒子群优化算法应用于弧长-曲率模型中的参数优化问题。仿真结果证明了该方法的有效性，是自主地面车辆运动规划的一种较好方法。

关键词: 自主地面车辆, 运动规划, 参数化模型, 粒子群优化