Dynamic Planning for Sequential Whole-body Mobile Manipulation

TL;DR

This paper introduces a comprehensive dynamic planning framework for mobile manipulators that integrates long-term task planning, reactive motion generation, and obstacle avoidance, validated through simulations and real-world tests.

Contribution

It extends existing SMMP methods by automating long-term task planning and reactive motion control, enabling more robust and adaptable mobile manipulation in dynamic environments.

Findings

Effective long-term task planning responding to environment changes

Reactive obstacle avoidance during execution

Validated performance in real-world scenarios with human interference

Abstract

The dynamic Sequential Mobile Manipulation Planning (SMMP) framework is essential for the safe and robust operation of mobile manipulators in dynamic environments. Previous research has primarily focused on either motion-level or task-level dynamic planning, with limitations in handling state changes that have long-term effects or in generating responsive motions for diverse tasks, respectively. This paper presents a holistic dynamic planning framework that extends the Virtual Kinematic Chain (VKC)-based SMMP method, automating dynamic long-term task planning and reactive whole-body motion generation for SMMP problems. The framework consists of an online task planning module designed to respond to environment changes with long-term effects, a VKC-based whole-body motion planning module for manipulating both rigid and articulated objects, alongside a reactive Model Predictive Control (MPC) module for obstacle avoidance during execution. Simulations and real-world experiments validate the framework, demonstrating its efficacy and validity across sequential mobile manipulation tasks, even in scenarios involving human interference.