Research on Orbit Assembly Strategy of Large-scale Space Truss Structure - Dai, Ye; Xiang, Chao-Fang; Bao, Yu-Dong; Qi, Yun-Shan; Qu, Wen-Yin; Zhang, Qi-Hao (Recent Patents on Engineering, 2023)
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.
Keywords: reinforcement learning