Minimum jerk norm scheme applied to obstacle avoidance of redundant robot arm with jerk bounded and feedback control

In this study, a minimum jerk norm (MJN) scheme with an obstacle avoidance constraint is proposed and applied to a redundant robot arm, of which the joint jerks keep bounded for a human‐friendly robot control. To achieve superior tracking performances of the redundant robot arm, the proposed jerk bounded MJN scheme is improved by the feedback control. More importantly, the effectiveness on obstacle avoidance of the proposed scheme is guaranteed by the variable‐magnitude escape jerk theorem. Besides, for the purpose of implementation on the practical robot system, the corresponding discrete formulas with their theoretical analyses are presented. Then the proposed scheme is reformulated as a dynamical quadratic program which is solved by a piecewise‐linear projection equation neural network. Furthermore, the path‐tracking simulation and comparison substantiate the effectiveness and accuracy of such a scheme with the smooth and human‐friendly joint variables applied to the obstacle avoidance of a six degrees of freedom jerk bounded robot arm. At last, the experimental application conducted on a practical redundant robot arm system further shows the physical realisability and the safety of the proposed scheme.