Redundancy Utilization and Motion Planning of Redundant Manipulators

Abstract

Redundant manipulators provide extra degrees of freedom that can be exploited to improve motion flexibility, obstacle avoidance, singularity avoidance, and joint-limit handling. However, redundancy also introduces multiple inverse kinematics solutions and increases the complexity of motion planning and control. This paper reviews methods for utilizing redundant degrees of freedom and solving inverse kinematics, with emphasis on the development from null-space and task-priority methods to optimization-based approaches. Techniques including gradient projection, flexible task priorities, task-transition control, set-based control, and performance-based multi-objective optimization are discussed. The review also examines motion planning under multiple constraints, including environmental obstacles, self-collision, joint limits, singularities, path length, and motion smoothness. Particular attention is given to the interaction between inverse kinematics and path planning, including methods that jointly optimize target configurations and motion paths. Particle swarm optimization and the PASO framework are introduced as examples of intelligent optimization for high-dimensional redundant manipulators. Overall, research is shifting from finding feasible inverse kinematics solutions toward integrated optimization of safety, motion performance, and the complete motion process. Future work should improve computational efficiency and the coordination of multiple constraints and objectives for high-degree-of-freedom robots.