基于改进粒子群算法的避障轨迹规划

Obstacle avoidance trajectory planning based on improved particle swarm optimization algorithm

针对机器人任务空间的全局避障轨迹规划问题,提出参数寻优的方法完成避障。首先推导出用于轨迹描述的多项式函数,通过改变参数值来改变轨迹形状进而避开障碍。其次以机器人关节转角增量最小和运动时间最短为目标,使用罚函数处理避障条件建立优化模型,将问题转化为求解最优参数,提出指数曲线递减和动态调整策略改进粒子群算法,完成寻优。最后利用Matlab完成机器人的运动学建模和工作空间分析,仿真验证轨迹规划结果,同时绘制机器人实时运动的关节数据曲线。结果表明,通过改进粒子群算法对参数寻优并使用五次多项式作轨迹描述完成避障规划,相比标准算法提高了收敛速度得到全局最优解。

Aiming at the problem of global obstacle avoidance trajectory planning of task space robot,a parameter optimization method is proposed to complete obstacle avoidance.Firstly,the polynomial function for trajectory description is derived,and the trajectory shape is changed by changing the parameter value,so as to avoid obstacles.Secondly,aiming at the minimum increment of robot joint angle and the shortest movement time,the penalty function is used to deal with the obstacle avoidance conditions,the optimization model is established,the problem is transformed into solving the optimal parameters,the exponential curve decreasing and dynamic adjustment strategy are proposed,and the particle swarm optimization algorithm is improved to complete the optimization.Finally,the kinematics modeling and workspace analysis of the robot are completed by Matlab,the trajectory planning results are verified by simulation,and the joint data curve of the robot's real-time motion is drawn at the same time.The results show that the improved particle swarm optimization algorithm optimizes the parameters and uses the quintic polynomial as the trajectory description to complete the obstacle avoidance planning,which improves the convergence speed and obtains the global optimal solution compared with the standard algorithm.