cs.ROApr 9, 2026

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

Authors: Yifei Wang, Hao Zhang, Jidong Huang, Shuohang Fang, Haoyao Chen

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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.

Figures & tables

Fig. 1 : The concept of mobile manipulation for explosive ordnance disposal in rescue scenarios.
Fig. 2 : General framework of EMMa.
Fig. 3 : The typical mechanism of a tracked mobile manipulator and its key reference frame and state definition.
Fig. 4 : The typical arm span schematic description of a tracked mobile manipulator.
Fig. 5 : Manipulator configuration selection near obstacles. In the color-coded ESDF map, red squares indicate obstacles in the environment. The green arrow nk\mathbf{n}_{k} denotes the midline vector pointing toward the elbow, and the red arrow no\mathbf{n}_{o} represents the ESDF gradient direction at the midpoint of the line from end-effector to base.
Fig. 6 : Joint configuration and base path interpolation considering end-effector stability.
Fig. 7 : Four typical simulated rescue scenarios with distinct end-effector operation requirements: (a) obstacle-aware grasping with configuration adjustment; (b) grasping during locomotion; (c) environment inspection during locomotion; and (d) transportation of a force-sensitive payload over rough terrain.
ParametersDescriptionValue
ϵed,ϵbd,ϵad\epsilon_{ed},\epsilon_{bd},\epsilon_{ad}clearances of EE/Base/Arm0.05/0.2/0.1 m
ϵew\epsilon_{ew}arm workspace limitation0.85 m
δJ\delta_{J}Frobenius norm threshold3.0
Ωea,Ωeκ,Ωew\Omega_{ea},\Omega_{e\kappa},\Omega_{ew}weight of the EE costs102,102,10310^{2},10^{2},10^{3}
Ωbk\Omega_{bk}weight of the Base cost10210^{2}
Ωm\Omega_{m}weight of the Manipulability5050
TABLE I : Typical parameter settings
Fig. 8 : The typical performance of different algorithms in grasping near obstacles.
Fig. 9 : The typical end-effector executed trajectories of all compared methods across the four simulated tasks. The coordinate frames in (b-c) indicates the optimal grasp/inspection pose, respectively.
Fig. 10 : The typical executed trajectories of the end-effector in grasping during locomotion.
 Grasp during locomotionInspect during locomotion
 σla↓\sigma_{la}\downarrowσaa↓\sigma_{aa}\downarrowκmax⁡↓\kappa_{\max}\downarrowσla↓\sigma_{la}\downarrowσaa↓\sigma_{aa}\downarrowκmax⁡↓\kappa_{\max}\downarrow
Ours1.0901.658142.21.7261.50044.8
ReDyn1.3582.837349.41.5551.642133.2
GP1.4341.81488.62.9143.838177.6
TABLE II : Standard deviations of the end-effector linear acceleration σla(m/s2)\sigma_{la}\mathrm{(m/s^{2})} , angular acceleration σaa(rad/s2)\sigma_{aa}\mathrm{(rad/s^{2})} , and the maximum curvature κmax⁡(m−1)\kappa_{\max}(\mathrm{m^{-1}}) of the executed end-effector trajectories for the compared methods performing rescue tasks with end-effector fixed-pose constraints.
Fig. 11 : Raigor: a self-developed tracked mobile manipulator.
Fig. 12 : Real-world experiment setups.
Fig. 13 : The typical robot performance during the tasks: (a-b) the end-effector states in grasping during locomotion, captured either 0.1s0.1\mathrm{s} before gripper closure or at the moment when the end-effector is closest to the grasping pose; (c) the end-effector states near the objects during the object inspection task on rugged terrain.
Fig. 14 : The typical end-effector executed trajectories during the experiments. The coordinate frames in (a) and (b) indicate the optimal grasping poses.
 Easy SceneHard Scene
bvmaxx{}_{b}v_{max}^{x}≤0.2\leq 0.2≤0.3\leq 0.3≤0.5\leq 0.5≤0.2\leq 0.2≤0.3\leq 0.3≤0.5\leq 0.5
Ours100%100%100%100%60%40%
ReDyn100%80%0%0%0%0%
GP100%40%20%0%0%0%
TABLE III : Success rates of the different algorithms under varying chassis velocity limitations bvmaxx(m/s){}_{b}v_{max}^{x}(\mathrm{m/s}) and task difficulty setups.
Fig. 15 : The typical end-effector and payload motion traces in the ablation experiment ( 15s15\mathrm{s} in total).
Fig. 16 : The typical end-effector executed trajectories in the ablation experiment (top-down view).
Fig. 17 : The typical end-effector linear velocity and acceleration patterns in the ablation experiment.
 pmax↓p_{\mathrm{max}}\downarrowvmean↓v_{\mathrm{mean}}\downarrowvmax↓v_{\mathrm{max}}\downarrowamean↓a_{\mathrm{mean}}\downarrowamax↓a_{\mathrm{max}}\downarrow
F3B-P30.01690.00970.03260.05250.2382
F3B-P10.02360.01200.04070.05960.3041
FB-P30.05210.01470.06510.07470.5472
FB-P10.20520.04240.12100.09320.3130
TABLE IV : Quantitative performance of different control strategies in real-world experiments: pmax (m)p_{\mathrm{max}}\,(\mathrm{m}) denotes the Euclidean distance between the farthest waypoints along the executed end-effector trajectory, vmean (m/s)v_{\mathrm{mean}}\,(\mathrm{m/s}) denotes the mean linear velocity of the end-effector in the global coordinate frame, vmax (m/s)v_{\mathrm{max}}\,(\mathrm{m/s}) denotes the maximum linear velocity of the end-effector in the global coordinate frame, amean (m/s2)a_{\mathrm{mean}}\,(\mathrm{m/s^{2}}) denotes the mean linear acceleration of the end-effector in the global coordinate frame, and amax (m/s2)a_{\mathrm{max}}\,(\mathrm{m/s^{2}}) denotes the maximum linear acceleration of the end-effector in the global coordinate frame.
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