Autonomous robotic systems are increasingly deployed across manufacturing, logistics, and healthcare due to their ability to efficiently complete difficult and dangerous tasks. Motion planning, which generates a safe and feasible motion from an initial state to a goal state, is fundamental to these systems. Many applications also require optimizing performance criteria such as time or energy while satisfying kinematic and dynamic constraints, motivating the study of optimal motion planning. This paper provides a comprehensive review of optimal motion planning algorithms for autonomous robotic systems. The fundamental concepts of optimal motion planning are first introduced. Planners are then categorized into three classes: graph-based, tree-based, and trajectory optimization-based methods, and state-of-the-art algorithms within each category are reviewed. Key properties of each class are compared, and guidance on selecting an appropriate planner for a given application is provided. Finally, challenges and future directions in this research area are discussed.
Xie et al. (Fri,) studied this question.
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