Obstacle Avoidance and Trajectory Tracking of Redundant Manipulators with Unknown Physical Parameters Under Multiple Constraints

Abstract

Redundant manipulators are broadly used in safety tasks that require precise execution. However, some actual factors, such as assembly defects and mechanical wear, inevitably introduce uncertainties in their physical parameters. Due to the insufficient excitation, existing methods fail to achieve high-accuracy physical parameter identification, which seriously affects the accurate operation of multiple safety tasks. Considering that quadratic programming (QP) can integrate desired behaviors with constraints into a unified optimization framework and can be solved online in real time, this paper formulates the collision-free trajectory tracking of redundant manipulators with uncertain structure as a QP problem, which describes the trajectory tracking, obstacle avoidance and joint motion limits simultaneously. To tackle the above issues, we first develop an online physical parameters identification method called regularization and zeroing neural dynamics (RZND) by using the state information of the manipulator, which realizes highly accurate physical parameters identification and real-time Jacobian matrix updates. On the basis of this, a multi-task quadruple projection neural network (MT-QPNN) solver is proposed by applying projection operators to handle the joint constraints, and further the trajectory tracking and obstacle avoidance tasks of the redundant manipulator are well achieved. Relative experiments verify that the presented RZND method and MT-QPNN solver can accurately identify physical parameters and enable collision-free tracking with superior performance.