摘要
针对复杂干扰环境下的移动机器人智能导航问题,多种导航方法被提出和应用,其中偏振光导航有良好的自主性和抗干扰性。基于偏振光传感器的组合导航系统较传统视觉算法能有效提高航向角和位置精度,可满足室外机器人导航要求,但欠缺路径规划功能。为实现偏振光移动机器人的智能导航应用,基于此系统,融入智能规划算法,实现路径规划功能。传统路径规划算法存在搜索效率低、生成路径曲率非连续等问题,为此提出一种改进A^(*)算法和轨迹优化方法,采用8邻域搜索方式,并改进了其启发函数,最后,利用梯度插补方式平滑路径。仿真及实验表明,改进后方案路径长度平均减小6.1%,路径转折点个数平均减少53.8%,实现了基于偏振光的移动机器人的智能导航。
Aiming at the problem of smart navigation of mobile robots in complex interference environments, a variety of navigation methods have been proposed and applied. Among them, polarization skylight navigation has good autonomy and anti-interference. The combined navigation system based on the polarization sensor can effectively improve the heading angle and position accuracy compared with the traditional vision algorithm, and can meet the requirements of outdoor robot navigation, but lacks the path planning function. In order to realize the intelligent navigation application of the polarization mobile robot, based on this system, the intelligent planning algorithm was incorporated to realize the path planning function. However, traditional path planning algorithms had problems such as low search efficiency and noncontinuous path curvatures. To this end, an improved A^(*) algorithm and a trajectory optimization method were proposed. The eight-neighbor search method was selected, and the heuristic function was improved. Finally, gradient interpolation was used to smooth the path. Simulations and experiments show that the path length of the improved scheme is reduced by 6.1% on average, and the number of path turning points is reduced by 53.8% on average, realizing the intelligent navigation of the polarization mobile robot.
作者
褚金奎
李晋
李金山
张志超
Chu Jinkui;Li Jin;Li Jinshan;Zhang Zhichao(School of Mechanical Engineering,Dalian University of Technology,Dalian,Liaoning 116023,China)
出处
《机电工程技术》
2022年第8期1-5,共5页
Mechanical & Electrical Engineering Technology
基金
国家自然科学基金及创新研究群体项目(编号:52175265,51675076,51621064)
中央高校基本科研业务费项目(编号:DUT21ZD101,DUT21GF308,DUT20LAB303)。
关键词
移动机器人
偏振光传感器
路径规划
轨迹优化
mobile robots
polarization sensor
path planning
trajectory optimization
