齐尧, 何滨兵, 潘世举, 穆巍炜, 章永进, 徐友春. 基于重组优化的轮式移动机器人路径处理方法[J]. 机器人, 2023, 45(1): 70-77. DOI: 10.13973/j.cnki.robot.210378
引用本文: 齐尧, 何滨兵, 潘世举, 穆巍炜, 章永进, 徐友春. 基于重组优化的轮式移动机器人路径处理方法[J]. 机器人, 2023, 45(1): 70-77. DOI: 10.13973/j.cnki.robot.210378
QI Yao, HE Binbing, PAN Shiju, MU Weiwei, ZHANG Yongjin, XU Youchun. Path Processing Method for Wheeled Mobile Robots Based on Rearrangement and Optimization[J]. ROBOT, 2023, 45(1): 70-77. DOI: 10.13973/j.cnki.robot.210378
Citation: QI Yao, HE Binbing, PAN Shiju, MU Weiwei, ZHANG Yongjin, XU Youchun. Path Processing Method for Wheeled Mobile Robots Based on Rearrangement and Optimization[J]. ROBOT, 2023, 45(1): 70-77. DOI: 10.13973/j.cnki.robot.210378

基于重组优化的轮式移动机器人路径处理方法

Path Processing Method for Wheeled Mobile Robots Based on Rearrangement and Optimization

  • 摘要: 针对轮式移动机器人采集GNSS(全球卫星导航系统)路径过程中容易出现无效路段的问题, 提出了基于自适应分段重组的无效路径剔除方法和多目标优化的路径平滑方法。该方法首先按照航向将采集的GNSS路径划分为DRD(drive-reverse-drive)形式的路段组合, 通过设定剔除规则来剔除其中的无效路段, 并对剔除无效路段后的离散路段按照邻域路段长度进行第2次分段, 以实现有效路段快速重组。其次以重组后的路径点集为决策变量, 建立多目标优化函数、端点渐近约束和矩形区域约束, 转化为二次规划型进行最优化求解, 在保证路径平滑的同时减小与原有路径的位置差。实验结果表明, 该方法能够有效处理不同道路形态下采集的无效GNSS路径, 优化结果与重组路径平均位置偏差小于0.2m, 平均处理时间为8.8ms, 处理后的路径可用于无人车轨迹跟随。

     

    Abstract: In order to solve the problem of invalid segments in collected GNSS (global navigation satellite system) paths of wheeled mobile robots, an invalid path elimination method based on self-adaptive segmentation and rearrangement is proposed, and a path smoothing method based on multi-objective optimization is presented. Firstly, the collected GNSS paths are divided into DRD (drive-reverse-drive) segments by points' heading, and some elimination rules are set to eliminate the invalid segments. For the discrete segments after eliminating invalid segments, they are divided for the second time according to the length of adjacent segments, so as to achieve rapid rearrangement of effective segments. Then a multiobjective optimization function, asymptotic boundary point constraints and rectangular region constraints are established with the rearranged point sets as decision variables, and the multi-objective optimization problem is transformed into quadratic programming for optimal solution, in order to reduce the position difference with the original path while improving the path smoothness. Experimental results show that the proposed method can effectively deal with invalid GNSS paths collected in different road forms, the average position difference between the optimized results and rearranged path is less than 0.2 m, the average processing time is 8.8 ms, and the processed path can be applied to trajectory following of unmanned vehicle.

     

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