Minimum-jerk trajectory planning in robotic manipulators with kinematic redundancy

Abstract

Minimizing induced mechanical vibrations represents a challenging and critical issue in robotics, as they can degrade motion accuracy, reduce achievable performance, and accelerate mechanical wear. This paper addresses the problem of minimum-jerk trajectory planning for redundant robotic manipulators by introducing an optimization approach that simultaneously exploits the structural redundancy of the robotic system and the functional redundancy of the assigned task. Unlike conventional redundancy-based approaches that typically rely on either kinematic redundancy alone or task redundancy properties, the proposed strategy leverages both types of redundancy to optimally plan the robot motion for the purpose of minimizing the end-effector jerk and, consequently, achieving smoother trajectories. In more detail, both the position of a selected redundant joint of the robot and one or more angles of the end-effector orientation are considered as optimization variables for each of the prescribed way points of the given path. The proposed strategy is verified on a robotic manipulator with seven degrees of freedom executing a pick-and-place operation. Extensive simulations and experimental tests demonstrate the effectiveness of the proposed strategy in reducing the end-effector jerk by more than 90% compared to a reference case.