Real-time Whole-Body Motion Planning for Mobile Manipulators Carrying Arbitrarily Shaped Payloads via Kinematically-Coupled SVSDF

2026-08-07

AI Summary

This work addresses the challenge that existing mobile manipulators struggle to simultaneously model complex geometries of large, arbitrarily shaped payloads and satisfy kinematic coupling constraints during transport, often leading to motion planning failure. To overcome this, the authors propose a real-time whole-body motion planning framework: the front-end employs a chain-decomposition kernel function to accurately represent robot-payload geometry; the mid-tier generates smooth, executable trajectories; and the back-end introduces a kinematically coupled Signed Voronoi Signed Distance Field (KC-SVSDF) to enable efficient gradient propagation along the kinematic chain and facilitate coordinated collision avoidance. This approach uniquely integrates chain-based decomposition for collision detection with KC-SVSDF, preserving true payload geometry while significantly enhancing traversability of non-convex objects in narrow, cluttered environments. Simulations and real-world experiments on a differential-drive mobile manipulator demonstrate superior performance over state-of-the-art methods.

Abstract

Mobile manipulators are increasingly tasked with transporting large, non-convex payloads through cluttered environments, yet existing planners either oversimplify the payload geometry or fail to handle the kinematic coupling between manipulator links, leading to lost feasible space or stalled optimization. This letter presents a real-time whole-body motion planning framework for mobile manipulators carrying arbitrarily shaped payloads. The front-end employs a chain-decomposed kernel-based collision check that preserves the true geometry of the robot and payload, with compact storage and fast bit-level queries. A mid-end preprocessing stage converts the front-end path into a continuous trajectory enforcing smoothness and feasibility, and executes it directly when collision-free to bypass the costly back-end. When refinement is required, the back-end performs trajectory optimization built on a Kinematically-Coupled SVSDF (KC-SVSDF), which propagates collision-avoidance gradients along the kinematic chain to produce coherent whole-body escape directions. Ablation studies, comparative benchmarks against state-of-the-art baselines, and real-world experiments on a differential-drive mobile manipulator demonstrate that the proposed framework reliably transports large, non-convex payloads through tight passages and cluttered environments.

Problem

• mobile manipulators
• arbitrarily shaped payloads
• kinematic coupling
• whole-body motion planning
• collision avoidance

Innovation

• mobile manipulator
• arbitrarily shaped payload
• kinematically-coupled SVSDF
• real-time motion planning
• whole-body trajectory optimization