Knowledge Commons of Institute of Automation,CAS
面向杂乱环境抓取的机器人操作协同技术研究 | |
卢宁![]() | |
2021-05 | |
页数 | 96 |
学位类型 | 硕士 |
中文摘要 | 机器人抓取操作面临各种环境的干扰以及动态变化,尤其在密集杂乱环境中,物体位姿状态千变万化且相互遮挡,机器人仅依靠单一抓取操作往往难以完成抓取任务,多操作协同是提高抓取成功率的有效途径。本文针对密集杂乱场景下的抓取问题,开展基于视觉感知的多操作协同技术研究,从机器人吸-抓可供性学习、推-抓协同操作和面向杂质性目标的精准抓取方面开展讨论。本文的主要工作如下: |
英文摘要 | Robotic grasping is faced with various environmental interference and dynamic changes, especially in dense clutter where the poses of objects are changeable and mutually occluded. It is difficult for robot to complete the grasping task only relying on prehensile actions. Multi-manipulation synergy is an effective way to improve the success rate of grasping. This thesis focuses on the research of vision-based manipulation synergy technology in dense-clutter grasping task, and discusses the aspects of robot suction-grasp affordance learning, push-grasp synergy manipulation and precise grasping for impurity targets. The main contents of this thesis are as follows: (1) According to mutual complement of different actions in dense clutter, a suction-grasp manipulation method based on data-driven modeling is proposed. The manipulation database is constructed by precisely annotating suction and grasp proposals over images from the real typical scenes. And the pixel-wise probability maps of the affordances for two primitive actions are learned through a fully convolutional neural network. With the analysis of affordance distribution, the synergy planner based on heuristic rules is designed to realize robot suction-grasp manipulation. The experimental results show the effectiveness of this method in dense-clutter grasping tasks. (2) A push-grasp manipulation method based on reinforcement learning is proposed to deal with the problem of high training cost and poor generalization of data-driven method. This method involves training fully convolutional networks that map from visual observations to actions in a Q-learning framework to learn the Q-value of manipulation points through environment interaction, and combines with manipulation synergy mechanism to realize the selection of the best action. To improve the efficiency of pushing action, a learning mechanism based on object dispersion degree metric and knowledge induction is proposed. The mechanism actively guides robot to disperse the objects and reduces the search range of the state space, which greately accelerate the policy convergence. The experimental results show that the proposed method lowers the invalid action rate, improves the efficiency of policy training, and has better performance in random scenes and challenging scenes. (3) Aiming at the grasping task of impurity targets, a push-grasp manipulation policy based on attention mechanism is proposed. The attention module includes the saliency detection and location for visible targets, and clustering analysis of environmental objects and reasoning of hidden regions for invisible objects. To improve the effectiveness of the target-oriented pushing action, an active pushing policy based on the target-centric dispersion degree is proposed to guide the robot to isolate the targets from other objects. The experimental results verify the effectiveness of our method in actively locating and grasping the impurity targets in dense clutter. |
关键词 | 机器人抓取,操作协同,深度学习,强化学习,面向目标的抓取 |
语种 | 中文 |
七大方向——子方向分类 | 智能机器人 |
文献类型 | 学位论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44867 |
专题 | 多模态人工智能系统全国重点实验室_智能机器人系统研究 |
推荐引用方式 GB/T 7714 | 卢宁. 面向杂乱环境抓取的机器人操作协同技术研究[D]. 中国科学院自动化研究所. 中国科学院大学,2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Thesis36m.pdf(36903KB) | 学位论文 | 开放获取 | CC BY-NC-SA |
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