Robotics & Automation: Advances in Motion Control, Path Planning, and Autonomous System Design with Integrated Perception

Abstract

This paper provides a comprehensive analysis of robotics and automation, focusing on motion control, path planning, and autonomous system design with integrated perception. Motion control strategies, from classical PID to advanced adaptive and model predictive control, are examined for their use in industrial robots, mobile platforms, and humanoid systems. Path planning techniques, including traditional graph-based and modern learning-driven approaches, are evaluated in dynamic environments requiring real-time obstacle detection and reconfiguration. The role of integrated perception—through sensor fusion, computer vision, and LiDAR—is emphasized for enhancing autonomy in unstructured environments. Case studies in industrial automation, autonomous vehicles, and service robotics demonstrate practical implementations. Challenges such as real-time processing, robustness to noise, and ethical issues are discussed, alongside future trends like AI and edge computing integration. This work bridges theoretical advancements and practical applications, contributing to the development of intelligent robotic systems.