Online task-space trajectory planning using real-time estimations of robot motion capabilities

Planning a robot's task-space movement according to its actual capacity is a difficult problem because this capacity depends on its state and thus can evolve significantly during the execution of the movement. This paper proposes a method for real-time trajectory planning based on time-optimal Trapezoidal acceleration profile (TAP) trajectories, that adapts to the real-time evolution of the robot's capacity. The method is based on an efficient approach for projecting the robot's kinematic limits in the trajectory direction, based on the convex polytope algebra. The method is experimentally validated on a Franka Emika Panda collaborative robot and compared with the classical approach considering fixed robot's Cartesian space motion capacity. The results show that the proposed method is able to better exploit true robot's motion capacity by generating faster trajectories for the same level of tracking accuracy