基于改进TEB算法的阿克曼移动机器人局部路径规划研究

Research on local path planning of Akamain mobile robot based on improved TEB algorithm

目标点跟踪是移动机器人导航和路径规划中的关键任务,其中局部路径规划尤为受到关注。作为一种局部路径规划算法,时间弹性带(TimeElasticBand,TEB)算法具有较好的适应性和鲁棒性。然而,在复杂动态环境中使用传统TEB算法时,机器人在障碍密集区可能会出现颠簸现象,并且在发生碰撞时速度过快且轨迹不够平滑。为了解决这些问题,文章以阿克曼移动机器人为研究对象,对传统TEB算法进行了改进,采用了一种区域规划策略,使机器人能够优先选择安全区域行驶。此外,设计了一种自适应调节参数的动态模块,用于在发生碰撞冲突时实时调节机器人的速度,优化其运动轨迹。最后,通过仿真平台进行实现。实验结果表明,改进后的机器人在障碍密集区的轨迹更加平滑,速度变化得到了更好地控制。

Target point tracking is a key task in mobile robot navigation and path planning,with local path planning receiving particular attention.As a local path planning algorithm,the Time Elastic Band(TEB)algorithm has good adaptability and robustness.However,when using traditional TEB algorithms in complex dynamic environments,robots may experience bumps in areas with dense obstacles,and their speed may be too fast and their trajectory may not be smooth enough when collisions occur.To address these issues,the article focuses on the Akamain mobile robot and improves the traditional TEB algorithm by adopting an area planning strategy that allows the robot to prioritize safe areas for travel.In addition,a dynamic module with adaptive parameter adjustment has been designed to adjust the speed of the robot in real-time and optimize its motion trajectory in the event of collision conflicts.Finally,it will be implemented through a simulation platform.The experimental results show that the improved robot has a smoother trajectory in areas with dense obstacles and better control over speed changes.