The invention provides a redundant manipulator motion planning method which comprises the following steps that: (1) an upper computer analyzes the inverse kinematics of a manipulator on a velocity layer through quadratic optimization, the designed minimum performance index can be velocity norm, repetitive motion or kinetic energy and is bound by a velocity jacobian equation, an inequation and a joint angular velocity limit, and the angular velocity limit changes with a joint angle; (2) the quadratic optimization of step (1) is optimized into a quadratic programming problem; (3) the quadratic programming problem in step (2) is calculated by a linear variational inequation primal-dual neural network solver or a numerical method; and (4) the calculation result in step (3) is transmitted to a lower computer controller to drive the manipulator to move. The redundant manipulator motion planning method is based on the primal-dual neural network of the linear variational inequation, has global exponential convergence, does not involve matrix inversion and other complicated operations, greatly improves the calculation efficiency, and simultaneously has strong real-time performance, and can adapt to the changes to the joint angular velocity limit.