LI Wenhai,LI Chaorong,HUANG Yingfei,et al.Design of Intelligent Vehicle Obstacle Detection and Navigation System based on ROS and YOLOv5s[J].Journal of Chengdu University of Information Technology,2023,38(06):661-667.[doi:10.16836/j.cnki.jcuit.2023.06.007]
基于ROS与YOLOv5s的智能车障碍物检测导航系统的设计
- Title:
- Design of Intelligent Vehicle Obstacle Detection and Navigation System based on ROS and YOLOv5s
- 文章编号:
- 2096-1618(2023)06-0661-07
- Keywords:
- ROS; YOLOv5s; obstacle detection; path planning; autonomous navigation; smart car
- 分类号:
- TP249
- 文献标志码:
- A
- 摘要:
- 针对智能车障碍物检测与自主导航任务中存在的识别准确率差、检出率低，以及自主导航路径规划器稳定性差等问题，设计一种基于ROS实验平台的障碍物检测与自主导航路径规划系统。系统以YOLOv5s作为障碍物检测算法框架，以A*算法与TEB算法融合作为自主导航算法框架，改善了智能车障碍物检测精度低与路径规划不稳定的问题。实验结果表明，搭载该系统的智能车能够完成障碍物检测、自主导航的任务，路径规划平均成功率达到96.67%，障碍物检测准确率在92%以上，综合任务成功率在90%以上，具有障碍物检测准确率高，自主导航路径规划稳定性强的特性。
- Abstract:
- Aiming at the problems of poor recognition accuracy and low detection rate in obstacle detection and autonomous navigation tasks of intelligent vehicles, as well as poor stability of autonomous navigation path planner, this paper designed an obstacle detection and autonomous navigation path planning system based on ROS experimental platform. This system uses YOLOv5s as the obstacle detection algorithm framework and the fusion of A* algorithm and TEB algorithm as the autonomous navigation algorithm framework, which improves the accuracy of obstacle detection and makes path planning of intelligent vehicles more stable. The results show that the intelligent vehicle equipped with the system can complete the tasks of obstacle detection and autonomous navigation, and the average success rate of path planning is 96.67%, the accuracy rate of obstacle detection is more than 92%, and the success rate of comprehensive tasks is more than 90%. It has the characteristics of high accuracy rate of obstacle detection and strong stability of autonomous navigation path planning.
参考文献/References:
[1] 黄文涵.基于单目视觉的智能车感知算法研究与应用[D].南京:东南大学,2021.
[2] 张凯祥,朱明.基于YOLOv5的多任务自动驾驶环境感知算法[J].计算机系统应用,2022,31(9):226-232.
[3] 张强,鲁守银,张家瑞,等.融合安全A*算法和改进人工势场法的巡检机器人路径规划[J].计算机时代,2022(11):29-33.
[4] 温淑慧,问泽藤,刘鑫,等.基于ROS的移动机器人自主建图与路径规划[J].沈阳工业大学学报,2022,44(1):90-94.
[5] 郭烈,齐国栋,赵一兵,等.融合A*与TEB算法的机器人多任务导航调度研究[J/OL].华中科技大学学报(自然科学版):2022-10-18.
[6] 常皓,杨巍.基于全向移动模型的Gmapping算法[J].计量与测试技术,2016,43(10):1-4.
[7] 李涌.基于激光SLAM的智能隧道巡检机器人自主移动平台研究[D].西安:长安大学,2021.
[8] 过佳颖.基于ROS平台的导航机器人局部路径规划的研究与优化[J].现代信息科技,2022,6(5):144-148.
[9] 常宏.基于MPC的局部路径规划与路径跟踪控制研究[J].汽车实用技术,2022,47(4):38-41.
[10] 贾屿.基于TEB算法的无人驾驶汽车路径规划与避障技术研究[D].合肥:合肥工业大学,2021.
[11] 代婉玉,张丽娟,吴佳峰,等.改进TEB算法的局部路径规划算法研究[J].计算机工程与应用,2022,58(8):283-288.
[12] 刘鹤,李建义.基于YOLOv5s模型的车辆类型检测算法[J].廊坊师范学院学报(自然科学版),2022,22(3):24-28.
[13] 毛涛.基于YOLOv5的小目标检测算法研究[D].淮南:安徽理工大学,2021.
相似文献/References:
[1]孙光灵,周云龙.自注意力结合上下文解耦的交通车辆检测[J].成都信息工程大学学报,2024,39(04):422.[doi:10.16836/j.cnki.jcuit.2024.04.005]
SUN Guangling,ZHOU Yunlong.Traffic Vehicle Detection based on Self-Attention Combined with Context Decoupling[J].Journal of Chengdu University of Information Technology,2024,39(06):422.[doi:10.16836/j.cnki.jcuit.2024.04.005]
备注/Memo
收稿日期:2022-11-12
基金项目:安徽省大学生创新创业训练计划资助项目(202213614003)