Key research and development program of China under grant 2019YFB1312002
传统TEB(Time Elastic Band)算法在杂乱场景下规划易出现倒退、大转向等异常行为,造成加速度跳变,控制指令不平滑,机器人受到大冲击,不利于移动机器人轨迹跟踪.本文提出了一种改进TEB算法,通过增加危险惩罚因子约束能规划更安全的运动轨迹、增加加速度跳变抑制约束减小运动中最大冲击、增加末端平滑约束减小末端冲击,实现目标点平滑、准确到达.然后构建图优化问题,以机器人的位姿和时间间隔为节点,目标函数和约束函数为边,利用问题的稀疏性快速获得相应时刻点的控制量.最后,通过基于机器人操作系统的大量对比仿真测试,以及真实差速机器人上的物理实验对提出的改进TEB算法进行性能验证.结果表明改进TEB算法在复杂环境中能规划出更安全、平滑的轨迹,减小机器人所受冲击,实现移动机器人更合理的运动.
The traditional TEB algorithm is prone to abnormal behaviors such as backsliding and large steering in the messy scene planning, which causes acceleration jumps, unsmooth control commands, and large impacts on the robot, which is not conducive to the trajectory tracking of the mobile robot. This paper proposes an improved TEB algorithm, which can plan a safer motion trajectory by adding the hazard penalty factor constraint,reduce the maximum impact in motion by adding the acceleration jump suppression constraint, and reduce the end impact by adding the smooth ends constraint, so as to achieve the smooth and accurate arrival of the target point. Then construct a graph optimization problem, take the pose and time interval of the robot as the nodes, the objective function and the constraint function as the edges, and use the sparsity of the problem to quickly obtain the control amount at the corresponding time point. Finally, the performance of the proposed improved TEB algorithm is verified by a large number of comparison tests of robot operating system simulation and physical experiments of real differential robot planning algorithm. The results show that the improved TEB algorithm can plan safer and smoother trajectory in complex environment, reduce the impact of the robot, and realize more reasonable movement of the robot.