摘要
针对目前主流机器人路径导航算法存在计算量大、计算时间长及在复杂环境中无法实时规划等问题,提出一种改进的局部路径算法。在A^(*)算法规划路径的基础之上,以机器人当前位置规划出的部分路径作为局部路径,将截取的路径分为多个节点,根据机器人与其邻近节点的坐标计算相对距离与相对角度,求解机器人水平与垂直方向速度,并将其发送给下位机控制机器人运动。使用科大讯飞推出的智能车平台进行导航效果测试,比较小车在TEB(time elastic band)算法和改进算法控制下的运动时间与碰撞障碍物次数。结果表明:相比于TEB算法,小车在改进算法的控制下碰撞次数明显减少,在长度约30 m的运行过程中平均用时减少12 s、平均速度提高0.2 m·s^(-1),速度变化较稳定。改进算法结构相对简单,在路径规划过程中能够减少所需计算量,可提高机器人整体运动的稳定性。
Aiming at the problems of the current mainstream robot path navigation algorithms,such as large amount of computation,long computation time and real-time planning in complex environments,an improved local path algorithm was proposed.Based on the path planning of A^(*) algorithm,the partial path planned by the current position of the robot was taken as the local path,the intercepted path was divided into multiple nodes,the relative distance and angle were calculated according to the coordinates of robot and the nearest node,the horizontal and vertical velocities of the robot were solved,and they were sent to the lower computer to control the robot movement.The navigation effect was tested using the intelligent vehicle platform introduced by iFLYTEK,and the movement time and the number of collisions with obstacles were compared under the control of TEB algorithm and improved algorithm respectively.The results show that compared with TEB algorithm,the number of collisions of the car under the control of improved algorithm is significantly reduced,the average time is reduced by 12 s,the average speed is increased by 0.2 m·s^(-1),and the speed variation is relatively stable during the operation process with a length of about 30 m.The improved algorithm has a relatively simple structure,can reduce the amount of computation required in the path planning process,and can improve the stability of the overall motion of the robot.
作者
赵卫东
章争生
陈文博
ZHAO Weidong;ZHANG Zhengsheng;CHEN Wenbo(School of Electrical&Information Engineering,Anhui University of Technology,Maanshan 243032,China)
出处
《安徽工业大学学报（自然科学版）》
CAS
2023年第1期70-75,共6页
Journal of Anhui University of Technology（Natural Science）
基金
安徽省自然科学基金项目(2108085MF225)。
