基于视觉机器人障碍点云映射避障规划及仿真

doi:10.16182/j.issn1004731x.joss.23-0618

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (9): 2149-2158.doi: 10.16182/j.issn1004731x.joss.23-0618

• 研究论文 • 上一篇

基于视觉机器人障碍点云映射避障规划及仿真

霍韩淋¹, 邹湘军¹^,², 陈燕¹, 周馨曌², 陈明猷¹, 李承恩¹, 潘耀强¹, 唐昀超³

^1.华南农业大学工程学院, 广东广州 510000
^2.佛山市中科农业机器人与智慧农业创新研究院, 广东佛山 528000
^3.仲恺农业工程学院城乡建设学院, 广东广州 510080

收稿日期:2023-05-24 修回日期:2023-06-25 出版日期:2024-09-15 发布日期:2024-09-30
通讯作者: 唐昀超
第一作者简介:霍韩淋(1999-)，男，硕士生，研究方向为农业采摘机器人。
基金资助:
国家自然科学基金(32171909);东莞市2021年度省乡村振兴战略专项资金“大专项+任务清单”(20211800400013);广东省科学技术厅计划(2021A1313030016)

Visual Robot Obstacle Avoidance Planning and Simulation Using Mapped Point Clouds

Huo Hanlin¹, Zou Xiangjun¹^,², Chen Yan¹, Zhou Xinzhao², Chen Mingyou¹, Li Chengen¹, Pan Yaoqiang¹, Tang Yunchao³

^1.College of Engineering, South China Agricultural University, Guangzhou 510000, China
^2.Foshan -Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics, Foshan 528000, China
^3.College of Urban and rural Construction, Zhongkai College of Agricultural Engineering, Guangzhou 510080, China

Received:2023-05-24 Revised:2023-06-25 Online:2024-09-15 Published:2024-09-30
Contact: Tang Yunchao

摘要/Abstract

摘要：

针对复杂非结构化果园环境下的视觉点云障碍物识别的数据量大、冗杂度高，严重影响采摘作业的实时性及效率，基于点云分割提出了一种点云压缩算法，旨在提升了点云障碍物的识别效率及环境自适应性。采用了基于Informed RRT*及结合逆投影算法Mapping-based Informed RRT*(M-Informed RRT*)降维求解采摘路径。通过构建一个强实时性和高鲁棒性的机器人“采样-感知-避障”一体化作业系统，实现了高效的障碍物识别和路径规划。ROS采摘机器人的实验数据证明了本算法的可行性，显著提升了采摘作业的效率。

关键词: 双目视觉, 路径规划, 点云映射, ROS仿真, 采摘避障

Abstract:

In response to the large and complex data volume and high redundancy of visual point cloud obstacle recognition in complex unstructured orchard environments, which severely impacts the real-time performance and efficiency of harvesting operations, a point cloud compression algorithm is proposed based on point cloud segmentation to enhance the efficiency of point cloud obstacle recognition and environmental adaptability. An Informed RRT* based approach is used combined with an inverse projection algorithm, mapping-based informed RRT*(M-Informed RRT*) to solve the harvesting path problem. By constructing a highly real-time and robust integrated robot system for sampling, perception, and obstacle avoidance, efficient obstacle recognition and path planning are achieved. Experimental data from ROS based picking robots demonstrates the feasibility of this algorithm and significantly improves the efficiency of harvesting operations.

Key words: binocular vision, path planning, point cloud segmentation, ROS simulation, picking obstacle avoidance

中图分类号:

TP249

霍韩淋,邹湘军,陈燕等 . 基于视觉机器人障碍点云映射避障规划及仿真[J]. 系统仿真学报, 2024, 36(9): 2149-2158.

Huo Hanlin,Zou Xiangjun,Chen Yan,et al . Visual Robot Obstacle Avoidance Planning and Simulation Using Mapped Point Clouds[J]. Journal of System Simulation, 2024, 36(9): 2149-2158.

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参考文献 20

1	林俊强, 王红军, 邹湘军, 等. 基于DPPO的移动采摘机器人避障路径规划及仿真[J]. 系统仿真学报, 2023, 35(8): 1692-1704.
	Lin Junqiang, Wang Hongjun, Zou Xiangjun, et al. Obstacle Avoidance Path Planning and Simulation of Mobile Picking Robot Based on DPPO[J]. Journal of System Simulation, 2023, 35(8): 1692-1704.
2	王思玉, 牛成强, 李钊, 等. 典型果蔬采摘机器人研究现状与趋势[J]. 新疆农机化, 2021(6): 14-17, 21.
	Wang Siyu, Niu Chengqiang, Li Zhao, et al. Current Research Status and Development Trends of Typical Fruit and Vegetable Harvest Robot[J]. Xinjiang Agricultural Mechanization, 2021(6): 14-17, 21.
3	邹湘军, 孙健, 何汉武, 等. 虚拟现实技术的演变发展与展望[J]. 系统仿真学报, 2004, 16(9): 1905-1909.
	Zou Xiangjun, Sun Jian, He Hanwu, et al. The Development and Prospects of Virtual Reality[J]. Journal of System Simulation, 2004, 16(9): 1905-1909.
4	吴烽云, 叶雅欣, 陈思宇, 等. 复杂环境下香蕉多目标特征快速识别研究[J]. 华南农业大学学报, 2022, 43(2): 96-104.
	Wu Fengyun, Ye Yaxin, Chen Siyu, et al. Research on Fast Recognition of Banana Multi-target Features by Visual Robot in Complex Environment[J]. Journal of South China Agricultural University, 2022, 43(2): 96-104.
5	Cao Xiaoman, Yan Hansheng, Huang Zhengyan, et al. A Multi-objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator[J]. Agronomy, 2021, 11(11): 2286.
6	Ye Lei, Duan Jieli, Yang Zhou, et al. Collision-free Motion Planning for the Litchi-picking Robot[J]. Computers and Electronics in Agriculture, 2021, 185: 106151.
7	Liu Xiaohuan, Zhang Degan, Zhang Jie, et al. A Path Planning Method Based on the Particle Swarm Optimization Trained Fuzzy Neural Network Algorithm[J]. Cluster Computing, 2021, 24(3): 1901-1915.
8	Volkan Pehlivanoglu Y, Pehlivanoglu Perihan. An Enhanced Genetic Algorithm for Path Planning of Autonomous UAV in Target Coverage Problems[J]. Applied Soft Computing, 2021, 112: 107796.
9	张佳尚, 陈志华. 基于预添加虚拟力的改进人工势场算法[J]. 兵器装备工程学报, 2023, 44(3): 219-225.
	Zhang Jiashang, Chen Zhihua. The Improved Artificial Potential Field Algorithm Based on Pre-added Virtual Force[J]. Journal of Ordnance Equipment Engineering, 2023, 44(3): 219-225.
10	Gai Shengnan, Sun Rui, Chen Shunjun, et al. 6-DOF Robotic Obstacle Avoidance Path Planning Based on Artificial Potential Field Method[C]//2019 16th International Conference on Ubiquitous Robots (UR). Piscataway: IEEE, 2019: 165-168.
11	张师瑜, 任永杰, 张腾. 基于视觉定位与改进RRT的机械臂自动拾放系统[J]. 自动化与仪器仪表, 2022(7): 221-228.
	Zhang Shiyu, Ren Yongjie, Zhang Teng. Robotic Arm Automatic Pick-and-place System Based on Visual Positioning and Improved RRT Algorithm[J]. Automation & Instrumentation, 2022(7): 221-228.
12	曾赟, 丁爱萍, 何欣. 多传感器信息融合的机械臂避障路径规划方法[J]. 机械设计与制造, 2023, 392(10): 258-262.
	Zeng Yun, Ding Aiping, He Xin. Obstacle Avoidance Path Planning Method of Manipulator Based on Multi-sensor Information Fusion[J]. Machinery Design & Manufacture, 2023, 392(10): 258-262.
13	Abdi Ali, Adhikari Dibash, Ju Hong Park. A Novel Hybrid Path Planning Method Based on Q-learning and Neural Network for Robot Arm[J]. Applied Sciences, 2021, 11(15): 6770.
14	Wen Shuhuan, Chen Jianhua, Wang Shen, et al. Path Planning of Humanoid Arm Based on Deep Deterministic Policy Gradient[C]//2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). Piscataway: IEEE, 2018: 1755-1760.
15	Kulkarni Prasad, Goswami Dip, Guha Prithwijit, et al. Path Planning for a Statically Stable Biped Robot Using PRM and Reinforcement Learning[J]. Journal of Intelligent and Robotic Systems, 2006, 47(3): 197-214.
16	卢英, 王慧琴, 佟威, 等. 基于Harris-张正友平面标定法的摄像机标定算法[J]. 西安建筑科技大学学报(自然科学版), 2014, 46(6): 860-864, 870.
	Lu Ying, Wang Huiqin, Tong Wei, et al. Based on Harris-Zhang Zhengyou Plane Calibration Method of Camera Calibration Algorithm[J]. Journal of Xi'an University of Architecture & Technology(Natural Science Edition), 2014, 46(6): 860-864, 870.
17	莫宇达, 邹湘军, 叶敏, 等. 基于Sylvester方程变形的荔枝采摘机器人手眼标定方法[J]. 农业工程学报, 2017, 33(4): 47-54.
	Mo Yuda, Zou Xiangjun, Ye Min, et al. Hand-eye Calibration Method Based on Sylvester Equation Deformation for Lychee Harvesting Robot[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(4): 47-54.
18	李仁忠, 杨曼, 冉媛, 等. 基于方法库的点云去噪与精简算法[J]. 激光与光电子学进展, 2018, 55(1): 243-249.
	Li Renzhong, Yang Man, Ran Yuan, et al. Point Cloud Denoising and Simplification Algorithm Based on Method Library[J]. Laser & Optoelectronics Progress, 2018, 55(1): 243-249.
19	Karaman S, Frazzoli E. Sampling-based Algorithms for Optimal Motion Planning[J]. The International Journal of Robotics Research, 2011, 30(7): 846-894.
20	Gammell J D, Srinivasa S S, Barfoot T D. Informed RRT^*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE, 2014: 2997-3004.

测试方法	平均规划时间/s	迭代次数	平均代价函数/mm	平均机械臂执行时间/s
RRT*	9.56	200	465.82	7.45
M-Informed RRT*	7.23	200	461.93	6.34

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基于视觉机器人障碍点云映射避障规划及仿真

Visual Robot Obstacle Avoidance Planning and Simulation Using Mapped Point Clouds

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