电子测量与仪器学报

谢赛宝,刘春阳,陈帆,黄艳,隋新,马喜强,杨晓康.基于视觉的自主机器人障碍识别与路径规划[J].电子测量与仪器学报,2022,36(12):185-192

基于视觉的自主机器人障碍识别与路径规划

Obstacle recognition and path planning method based on mobile robot

DOI：

英文关键词:mobile robots depth cameras obstacle recognition path planning

基金项目:国家自然科学基金(52105574)、河南省科技攻关计划(222102220079) 、河南省高等学校青年骨干教师培养计划(2019GGJS082)、河南省高等学校重点科研项目基础研究计划(17A460003)项目资助

作者	单位
谢赛宝	1 河南科技大学机电工程学院
刘春阳	1 河南科技大学机电工程学院
陈帆	2.中国航空工业集团公司洛阳电光设备研究所
黄艳	1 河南科技大学机电工程学院
隋新	1 河南科技大学机电工程学院
马喜强	1 河南科技大学机电工程学院
杨晓康	1 河南科技大学机电工程学院

Author	Institution
Xie Saibao	1. College of Mechanical and Electrical Engineering, Henan University of Science and Technology
Liu Chunyang	1. College of Mechanical and Electrical Engineering, Henan University of Science and Technology
Chen Fan	2. China Aviation Industry Corporation Luoyang Electro-Optical Equipment Research Institute
Huang Yan	1. College of Mechanical and Electrical Engineering, Henan University of Science and Technology
Sui Xin	1. College of Mechanical and Electrical Engineering, Henan University of Science and Technology
Ma Xiqiang	1. College of Mechanical and Electrical Engineering, Henan University of Science and Technology
Yang Xiaokang	1. College of Mechanical and Electrical Engineering, Henan University of Science and Technology

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中文摘要:

障碍物的识别与行走路径的规划是机器人实现自主移动的必要手段。本文基于深度相机提出一种由深度连续性与彩色特征点融合的障碍识别方法,通过深度相机获取物体的空间位置信息,映射到已有的地图中,构建障碍物空间。又提出一种 PRM-D∗的路径规划方法,先使用改进的随机概率路线图(PRM)完成整体路径规划工作,再根据相机识别的障碍物,设置局部地图,使用基于图搜索的 D∗算法进行局部动态规划,完成动态避障任务。通过实验,所提障碍物识别方法即使在昏暗的室内环境中,其对障碍物的检测准确率也大于 80%,常规环境检测准确率高于 95%,具有较好的鲁棒性与实时性;PRM-D∗的路径规划方法在缩短总体规划时间的同时,确保了路径规划的成功率,单次动态规划时间小于 0. 02 s,具有良好的动态避障性能。

英文摘要:

Obstacle recognition and path planning are the necessary means for robot to move autonomously. Based on depth camera, this paper proposes an obstacle recognition method based on the fusion of depth continuity and color feature points. The spatial location information of objects is obtained by depth camera and mapped to the existing map to construct the obstacle space. A path planning method of PRM-D∗ is proposed. Firstly, the improved random probability roadmap ( PRM) is used to complete the overall path planning. Then, according to the obstacles identified by the camera, the local map is set up, and the D∗ algorithm based on graph search is used to carry out local dynamic planning to complete the dynamic obstacle avoidance task. Through the experiment, the detection accuracy of the proposed obstacle recognition method is greater than 80% even in dim indoor environment, and the accuracy of conventional environmental detection is higher than 95%, and it has good robustness and real-time performance; The path planning method of PRM-D∗ not only shortens the overall planning time, but also ensures the success rate of path planning. The single dynamic planning time is less than 0. 02 s, and has good dynamic obstacle avoidance performance.

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