|Place of Conferral||中国科学院自动化研究所智能化大厦第七会议室|
Recently, the accuracy and execution efficiency of the robot have been improved significantly with the development of robotics, which plays an increasingly important role in modern industry. Assembly is one of the key links in modern industrial production. Robots are applied in the field of automatic assembly to replace repetitive assembly actions and improve production efficiency. They can reduce labor consumption and have achieved great success in automobile manufacturing, aerospace and other fields. In recent years, with the further rapid development of manufacturing industry, higher requirements have been put forward for the flexibility and intelligence of robot assembly due to the diversity of assembly objects and assembly environment and complexity of assembly process. Therefore, it is urgent to study the assembly strategy learning algorithm and system with certain adaptability.
This paper aimed at peg-in-hole assembly problem, which is the typical assembly problem, and proposed the algorithms based on the actual contact force prediction and robot autonomous learning. Moreover, the paper proposed a simulation system and a practical implementation system to solve the problem of active compliant assembly. The paper has three main contributions:
1. The simulation and practical systems of robot peg-in-hole compliant assembly were proposed. A simulation experiment platform based on Robot Operating System (ROS) was designed, which can quickly iterate the strategy learning algorithm, so that the robot can learn the compliant assembly strategy autonomously. A practical assembly system was designed for large length-diameter ratio peg-in-hole assembly problem, which provided a verification platform for the proposed compliant assembly algorithm.
2. A novel contact force/torque prediction and analysis model for flexible assembly of robot peg-in-hole assembly was proposed. We established a novel force/torque prediction model with measured data to obtain the precision actual contact force/torque which is critical for assembly control. A contact analysis model was built to estimate the assembly contact states. At last, based on the proposed contact force/torque prediction and analysis model, the paper designed a robot pose adjustment strategy. Through the experimental results in the actual assembly experimental platform, the results demonstrated that the proposed algorithm can meet the requirements of large length-diameter ratio peg-in-hole assembly. The force/torque error predicted by the model is less than 1%, and the average force/torque in the assembly process is less than 5N / 0.5N·m.
3. An assembly strategy learning algorithm based on action-reverse action memory was proposed. Firstly, the elements of the algorithm based on the reinforcement learning principle were defined in this paper. A novel action-reverse action memory method was proposed, which can effectively reduce the redundant exploration in the contact state space and improve the learning speed of assembly strategy. Finally, the paper proposed the assembly strategy fast learning algorithm based on the classic reinforcement learning algorithm. The peg-in-hole assembly experiments were completed on the simulation experiment system. The simulation results demonstrated that the learned strategy can be extended and control contact force well.
|王宇辰. 机器人轴孔柔顺装配策略学习研究[D]. 中国科学院自动化研究所智能化大厦第七会议室. 中国科学院自动化研究所,2019.|
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