An MPC-Based Framework for Dynamic Trajectory Re-Planning in Uncertain Environments

Abstract

Online motion re-planning is an important feature for introducing robots into unstructured environments where the close presence of humans at any time can challenge the operation of the robot from the human safety perspective. This work introduces a novel re-planning framework for robotic manipulators operating in dynamic environments where the interactions with humans may occur either in an anticipated or unexpected manner. The contribution of the proposed framework lies in the fact that it allows to account for the uncertainty of human pose and challenges associated with human motion estimation during occlusion phases of the human with respect to the perception system on the robot. To this aim the proposed framework is comprised of an uncertainty estimation component and a model predictive control (MPC) component, the combination of which enables to efficiently and dynamically track a task-space trajectory by the robot while limiting the probability of potential collisions with a moving human obstacle entering the workspace of the robot. Simulations and experimental trials on a robotic platform show the effectiveness of the proposed framework in re-planning the trajectory of the robotic arm under the presence of a human detected by the perception system installed on the robot.