Abstract
In recent years, on-orbit operations have become a research hotspot. Traditional on-orbit assembly using only robotic arms suffers from low precision. Therefore, this paper proposes a Macro-Micro Coordinated Robotic System (MMCRS) consisting of a robotic arm and a Stewart platform. The paper analyzes the kinematic characteristics of the MMCRS for the on-orbit peg-in-hole assembly problem and introduces a Multi-Agent Reinforcement Learning algorithm, Full-State MADDPG. A staged, variable-parameter reward function is designed, and the effectiveness of the algorithm is verified through simulations.
| Original language | English |
|---|---|
| Pages (from-to) | 2113-2118 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 59 |
| Issue number | 20 |
| DOIs | |
| Publication status | Published - 1 Aug 2025 |
| Event | 23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025 - Harbin, China Duration: 2 Aug 2025 → 6 Aug 2025 |
Keywords
- Dynamic Reward Function
- Macro-Micro Coordinated Robots
- MARL
- On-Orbit Assembly
- Peg-in-Hole