A deep learning strategy for on-orbit servicing via space robotic manipulator

Autonomous robotic systems are currently being addressed as a critical element in the development of present and future on-orbit operations. Modern missions are calling for systems capable of reproducing human’s decision-making process thus enhancing their performance. Generally, space manipulators are mounted on a floating spacecraft in a microgravity environment, consequently leading to a mutual influence between the robotic arms and the platform dynamics, thus making the motion planning and control design more challenging than those of terrestrial robots. Another aspect to be considered is that space robots are designed as lightweight systems, resulting in a significant dynamic coupling between their rigid motion and structural elasticity. These effects involve critical issues in modelling their dynamics and designing a suitable controller. In this context, Deep Neural Network (DNN) architectures and the related Deep Learning (DL) techniques have widely proved to have powerful capability in solving data-driven nonlinear modelling problems and they can hence represent a viable solution for space activities. The present paper deals with the design of a DNN controller for a space manipulator system, which has to follow a specific path for a typical on-orbit servicing mission. The goal is to provide proper control inputs autonomously adapting to the given desired trajectory. Structural flexibility and joint friction features are implemented in the dynamic model as well.