Using DCT Neural Networks for Human-Aware Trajectory Optimization of Robotic Manipulators

This paper addresses the integration of current and predicted human poses into the trajectory optimization problem of a robotic manipulator operating in a shared workspace, to achieve proactive collision avoidance. We propose a two-step approach. In the first step, a Feedforward Neural Network with Discrete Cosine Transform (DCT) preprocessing is trained to predict human poses over a finite prediction horizon, given a sequence of past observed poses. In the second step, we formulate a nonlinear trajectory optimization problem that minimizes control effort and torque variations, subject to dynamic, actuation, and joint-velocity constraints, and includes a collision-avoidance term in the objective function based on both current and predicted human poses. Numerical experiments on a scenario involving a 6-degree-of-freedom UR5 manipulator and a human operator demonstrate that the DCT-based approach limits the pose prediction error and that the proposed collision-avoidance term effectively maintains a safe human-manipulator distance throughout the planned trajectory.