PO-SRPP: A Decentralized Pivoting Path Planning Method for Self-Reconfigurable Satellites

Abstract

While there is ample research on hardware design and reconfiguration control for modular self-reconfigurable satellites, relatively few reconfiguration planning algorithms, especially algorithms used in real-world reconfiguration have been developed. Decentralized path planning, which only uses partial observation for each module to make decision is an important problem for real-world task. This article presents partially observable self-reconfiguration path planning, addressing the reconfiguration path planning problem for a single module using partial observations while aiming to maximize the policy learning efficiency. An end-to-end algorithm is proposed by employing a recurrent Q-learning algorithm and a deep neural network, where a Long Short Term Memory network is used to remember useful features from historical observations. Moreover, a 3-D convolutional neural network is used to automatically extract high-level features from observation data and is shown to significantly increase the learning efficiency. Experiments performed on a test rig of electromagnetic self-reconfigurable satellite verified the potency of the proposed algorithm.

Dr. Ehecatl Antonio del Rio Chanona

Principal Investigator of OptiML

Antonio del Rio Chanona is the head of the Optimisation and Machine Learning for Process Systems Engineering group based in thee Department of Chemical Engineering, as well as the Centre for Process Systems Engineering at Imperial College London. His work is at the forefront of integrating advanced computer algorithms from optimization, machine learning, and reinforcement learning into engineering systems, with a particular focus on bioprocess control, optimization, and scale-up. Dr. del Rio Chanona earned his PhD from the Department of Chemical Engineering and Biotechnology at the University of Cambridge, where his outstanding research earned him the prestigious Danckwerts-Pergamon award for the best PhD dissertation of 2017. He completed his undergraduate studies at the National Autonomous University of Mexico (UNAM), which laid the foundation for his expertise in engineering.