EMMa: End-Effector Stability-Oriented Mobile Manipulation for Tracked Rescue Robots

TL;DR

This paper introduces EMMa, a motion generation framework for tracked rescue robots that enhances end-effector stability and coordination during complex rescue operations, validated through extensive experiments.

Contribution

It proposes a novel coordinated path optimization and control scheme specifically designed for stable end-effector operation in rescue scenarios.

Findings

Outperforms state-of-the-art methods in success rate and stability.

Effective in both simulated and real-world rescue tasks.

Reduces computational complexity of motion planning.

Abstract

The autonomous operation of tracked mobile manipulators in rescue missions requires not only ensuring the reachability and safety of robot motion but also maintaining stable end-effector manipulation under diverse task demands. However, existing studies have overlooked many end-effector motion properties at both the planning and control levels. This paper presents a motion generation framework for tracked mobile manipulators to achieve stable end-effector operation in complex rescue scenarios. The framework formulates a coordinated path optimization model that couples end-effector and mobile base states and designs compact cost/constraint representations to mitigate nonlinearities and reduce computational complexity. Furthermore, an isolated control scheme with feedforward compensation and feedback regulation is developed to enable coordinated path tracking for the robot. Extensive simulated and real-world experiments on rescue scenarios demonstrate that the proposed framework consistently outperforms SOTA methods across key metrics, including task success rate and end-effector motion stability, validating its effectiveness and robustness in complex mobile manipulation tasks.