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

arXiv cs.RO / 4/10/2026

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Key Points

  • The paper proposes “EMMa,” a motion generation framework for tracked rescue robots that jointly considers end-effector stability and safe, reachable mobile manipulation in complex rescue tasks.
  • It couples end-effector and mobile base states in a coordinated path optimization model while using compact cost/constraint representations to reduce computational complexity and handle nonlinearities.
  • The method includes a control strategy with feedforward compensation and feedback regulation to support coordinated path tracking between the base and the end-effector.
  • Experiments in both simulated and real rescue scenarios show EMMa improves over state-of-the-art approaches on task success rate and end-effector motion stability.
  • By addressing end-effector motion properties at both planning and control levels, the work targets more robust autonomous manipulation for tracked robots under diverse task demands.

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.