Improved Acceleration-Level Motion Planning Method for Robot Manipulators Corrupted by Noise

Motion planning (MP) is one of fundamental issues in robot manipulators. Various MP schemes at joint velocity and acceleration levels are reported, but the noise impact is usually neglected. This paper presents an enhanced version of the acceleration-level MP (ALMP) method, incorporating additive noise considerations, specifically designed for robotic manipulators. Then, such an improved method, which is based on the pseudoinverse of robots’ Jacobian matrix, is theoretically proven to be robust against different types of noise. As case studies of the improved method, the noise-tolerance repetitive motion planning (NT-RMP) and the noise-tolerance minimum acceleration norm (NT-MAN) scheme are established for robot manipulators. Simulation and experiment results under the UR5 and Panda robot manipulators corrupted by different noises further validate the effectiveness and practicality of such two schemes and the improved ALMP method.