Low-Cost Control of Robotic Arms in Space

This paper presents a novel approach to controlling robotic arms in space exploration by integrating Kalman Filter techniques with Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The study aims to enhance the autonomy and efficiency of space missions by leveraging these advanced technologies. The proposed methodology is demonstrated through a series of simulations and practical implementations, focusing on the robustness and accuracy of robotic arm control in dynamic space environments. The results show significant improvements in control precision, reducing the margin of error in task execution. Furthermore, this research highlights the potential of AI and ML in advancing space robotics, providing a framework for future developments in this field. The study contributes to the existing literature by offering a comprehensive evaluation of Kalman Filter applications in space robotics, coupled with AI/ML enhancements. It proposes a path forward for more autonomous space exploration missions.