Robot Path Planning in Complex Environments: Methods, Challenges and Future Directions

Abstract

Robot path planning is a fundamental problem in robotics enabling autonomous robots to navigate safely, efficiently and naturally from a start position to a target position. In real robotic systems, path planning is not only about finding a collision-free path, but also about generating motions that satisfy the robot's physical, sensory, and task constraints. In this essay, the main robot path planning methods, including graph search, artificial potential field methods, sampling-based planning, local obstacle avoidance, and trajectory optimization are reviewed. Their respective strengths, limitations, and applicable scenarios are examined, with particular attention to how these approaches address issues such as computational complexity, environmental structure, and real-time responsiveness. It also addresses present day issues such as uncertainty, dynamic environments, computational efficiency and physical feasibility, emphasizing that no single method is sufficient for all robotic applications. Practical systems increasingly rely on hierarchical integration to balance global navigation with local reactivity. Finally, the essay argues that the future of robot path planning will be dominated by hybrid systems that combine global planning, local replanning, optimization, and learning-based prediction, enabling robots to operate more safely, intelligently, and adaptively in complex real-world environments.