Sensor-Based Task-Constrained Motion Planning using Model Predictive Control

摘要

A redundant robotic system must execute a task in a workspace populated by obstacles whose motion is unknown in advance. For this problem setting, we present a sensor-based planner that uses Model Predictive Control (MPC) to generate motion commands for the robot. We also propose a real-time implementation of the planner based on ACADO, an open source toolkit for solving general nonlinear MPC problems. The effectiveness of the proposed algorithm is shown through simulations and experiments carried out on a UR10 manipulator.