The end-effector is a key device for direct contact and operation between the operator and the workpiece, and its mechanical structure will directly affect the quality of the machine and expand its application. The theoretical research and technical implementation of the end-effector for compliance control are facing a lot of urgent challenges to be solved, therefore, the research results of active compliance control of robot end-effectors have a very broad application prospect. This paper describes the design and research results of different end-effectors under impedance-based control, hybrid force/position control, and intelligent flexible control methods, respectively. Under each control method, the structural characteristics and the optimized control scheme under different drives are introduced. Finally, key techniques for achieving compliance control are derived by summarizing, which broadens the engineering applications and provides methods and ideas for future research.
With space technology development, the spatial robotic arm plays an increasingly important role in space activities. Spatial robotic arms can effectively replace humans to complete in-orbit service tasks. The trajectory planning is the basis of robotic arm motion. Its merit has an essential impact on the quality of the completed operation. The research on spatial robotic arm trajectory planning has not yet formed a broad framework categorization, so it is necessary to analyze and deeply summarize the existing research systematically. This paper introduces the current situation of space obstacle avoidance trajectory planning and motion trajectory planning. It discusses the basic principle and practical application of the spatial robotic arm trajectory planning method. The future development trend has also been prospected.
The modular robot is becoming a prevalent research object in robots because of its unique configuration advantages and performance characteristics. It is possible to form robot configurations with different functions by reconfiguring functional modules. This paper focuses on studying the modular robot’s configuration design and self-reconfiguration process and hopes to realize the industrial application of the modular self-reconfiguration robot to a certain extent. We design robotic configurations with different DOF based on the cellular module of the hexahedron and perform the kinematic analysis of the structure. An innovative design of a modular reconfiguration platform for conformational reorganization is presented, and the collaborative path planning between different modules in the reconfiguration platform is investigated. We propose an optimized ant colony algorithm for reconfiguration path planning and verify the superiority and rationality of this algorithm compared with the traditional ant colony algorithm for platform path planning through simulation experiments.
Background: With the rapid development of spatial technology and mankind's continuous exploration of the space domain, expandable space trusses play an important role in the construction of space station piggyback platforms. Therefore, the study of the in-orbit assembly strategy for space trusses has become increasingly important in recent years. The spatial truss assembly strategy proposed in this paper is fast and effective, and it is applied for the construction of future large-scale space facilities effectively. Objective: The four-prismatic truss periodic module is taken as the research object, and the assembly process of the truss and the assembly behaviors of the spatial cellular robot serving for on-orbit assembly are expressed. Methods: The article uses a reinforcement learning algorithm to study the coupling of truss assembly sequence and robot action sequence, then uses a q-learning algorithm to plan the strategy of the truss cycle module. Results: The robot is trained through the greedy strategy and avoids the failure problem caused by assembly uncertainty. The simulation experiment proves that the Q-learning algorithm of reinforcement learning used for planning the on-orbit assembly sequence of the truss periodic module structures is feasible, and the optimal assembly sequence with the least number of assembly steps obtained by this strategy. Conclusion: In order to address the on-orbit assembly issues of large spatial truss structures in the space environment, we trained the robots through greedy strategy to prevent failure due to the uncertainty conditions both in the strategy analysis and in the simulation study.Finally, the Q-learning algorithm in reinforcement learning is used to plan the on-orbit assembly sequence in the truss cycle module, which can obtain the optimal assembly sequence in the minimum number of assembly steps.
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