Real-time Whole-body Motion Planning Based on Optimized NMPC in Static and Dynamic Environments for Mobile Manipulator

Abstract

Recently, the research on mobile manipulators has attracted increasing attention. Ensuring that mobile manipulators can meet obstacle avoidance constraints and efficiently accomplish assigned tasks in dynamic environments remains a significant challenge. To address this issue, this paper proposes an integrated framework for environment perception, real-time planning, and control optimization. Firstly, we develop a fusion map that combines euclidean signed distance field (ESDF) with clustered point clouds occupying cubes, enabling robots to perceive more precise environmental information in complex and changing conditions. Secondly, we introduce a novel rapid generation strategy for 6-DOF guide point sequences, which directs the mobile manipulator to follow the most efficient path to the target location while making real-time adjustments to avoid dynamic obstacles. Additionally, utilizing optimized nonlinear model predictive control (NMPC), we design a whole-body motion controller for the mobile manipulator to prevent the system from becoming trapped in local optima, thereby allowing the manipulator to adjust its state tracking guide points promptly in complex indoor environments. Finally, the proposed algorithm was implemented on a mobile manipulator with an Ackerman base and tested through both simulations and real-world experiments.