Simulation of Robot Motion and Control of Complex Movements Using Reinforcement Learning Algorithms

The simulation and control of complex robotic movements in dynamic and unpredictable environments are critical challenges in the fields of robotics, aerospace, and satellite technology. Traditional control methods often struggle to adapt to the highly variable and uncertain conditions encountered in aerospace environments. This paper examines the application of Reinforcement Learning (RL) algorithms, specifically Q-learning and Deep Q Networks (DQN), in addressing these challenges. These algorithms enable robots, and by extension, satellite systems, to learn optimal behaviors through interaction with their environment and feedback, making them well-suited for managing non-linear, dynamic, and stochastic environments. The study investigates the methodology and implementation of these RL algorithms within simulations designed to model satellite and robotic systems in aerospace conditions. These simulations cover complex tasks such as attitude control, path optimization, and maneuver planning, all under noisy and dynamic conditions typical in space environments. The results show that RL algorithms significantly enhance the precision and adaptability of movement control, reducing collision rates and optimizing energy efficiency. The findings demonstrate the potential of RL in advancing the performance of robotic systems and satellite operations in dynamic environments, accelerating the development of autonomous space systems, and contributing to innovations in aerospace and satellite technology.

Keywords:

Robot , DQN , Q-learning , Algorithm , Learning