An improved artificial potential field method (APFM) is proposed for the problem that only the end of manipulator can be guided but the angle of each joint cannot be constrained

and it is difficult to escape from local minimum when traditional APFM is used to avoid obstacles for redundant manipulator. A kinematic model of the manipulator is established and the line segment sphere enveloping box model is established for collision detection. Attract potential field of the end and obstacle repulsive potential field are established in Cartesian

and attract potential field of the target angle is established in joint space

and they work together to guide the manipulator. A virtual target angle is solved in joint space and the virtual potential field is established using Gaussian function to deal with the local minimum problem. The simulations and experiments on the 7 DOF redundant manipulator show that the algorithm can constraint joint pose and guide the manipulator escape from local minimum when trapped

and finally complete obstacle avoidance. At the end of the obstacle avoidance

the maximum error of each joint angle is 0.8°

and the average position error and attitude error are 0.010 m and 2.40°

which are smaller than the traditional algorithm respectively. The motion amplitude of each joint in the obstacle avoidance process is smaller than the traditional algorithm. The improved algorithm can guide the manipulator escape from the local minimum and complete the obstacle avoidance

as well as improve the positioning accuracy of each joint and the end at the end of the obstacle avoidance. The study has certain guiding significance for research and application of obstacle avoidance for redundant manipulators.