Tension Tells the Tail: Cable-Obstacle Interaction Sensing for Tethered Mobile Robot Navigation
Authors: Ryuki Shimada, Koki Suzuki, Genya Ishigami
Organizations: The authors are with the Space Robotics Group, Department of Mechanical Engineering, Keio University, Yokohama, Kanagawa 223-8522, Japan.
Tethered mobile robots can sustain long-duration operation through continuous power supply and stable communication via their cables. However, cable-environment interaction causes unintended obstacle displacement, cable snagging or entanglement, and even cable damage, leading to mission failure. To assess the risk of individual cable-obstacle contacts, this study investigates cable tension as a sensing modality for estimating whether a contacted obstacle is movable or non-movable. We conducted experiments with a tethered mobile robot and collected tension data while varying robot velocity, contact angle between the obstacle and cable, obstacle mass, and obstacle side-surface material. The results show that obstacle movability is characterized by the initial tension rise and, more importantly, by whether the tension relaxes or remains high after its peak. Based on these observations, we select six features from the tension profiles and train a logistic-regression classifier using 117 labeled trials. The proposed method distinguishes movable and non-movable cases with a balanced accuracy of 95.5% after 30 cm of post-contact travel. These results demonstrate the potential of cable tension for risk assessment of cable-obstacle interactions and provide a step toward online, cable-aware autonomous navigation.
Figures & tables
Figure 1: Overview of the cable–obstacle interaction sensing task. As the robot moves, the cable contacts the obstacle and generates tension; the goal is to estimate whether the obstacle is movable or non-movable from the tension measured within a short travel distance after contact.
Figure 2: Cable friction around a cylindrical obstacle. Capstan equation governs the tension distribution along the cable–obstacle contact region.
Figure 3: Experimental setup. (A) Jackal robot with a force/torque sensor at its rear; the sensor z -axis is aligned with the robot’s longitudinal axis. (B) Test field with the cable base, whose constant-force spring (close-up in the red box) keeps the cable pretension constant; the red arrow shows the robot’s direction of travel. (C) Steel cans with three side-surface materials (bare steel, sandpaper #400, and rubber sheet), loaded with weights (top right) to set the obstacle mass.
Category
Model
Size
Additional specifications
Robot
Clearpath Robotics Jackal
580 × 430 × 250 mm
Mass 17 kg, Max speed 2.0 m/s
Cable
Blue Robotics Fathom ROV Tether
∅ 4 mm × 3 m
—
Obstacle
20 L steel can
∅ 306 mm × 367 mm
Mass 1.25 kg (empty)
Force/torque sensor
Leptrino PFS080YS102U6
∅ 80 mm × 35 mm
Max ∗ ± 1000 N ( Fz ), Resolution 0.25 N, Frequency ≤ 1.2 kHz
Figure 5: Representative examples of cable–obstacle interaction behaviors. (A) Rotation and translation (movable): the obstacle rotates (red arrow) and translates (green arrow) on the ground. (B) Rotation only (non-movable): the obstacle rotates in place without translating. (C) Stationary (non-movable): the obstacle stays in place while the cable slides along its side surface.
Figure 6: Behaviors and tension profiles on the bare surface. (A) Interaction behavior in each condition; numbers denote trial counts. Green thick lines mark the movable/non-movable boundary, and red dashed boxes mark the conditions in which velocity changes the behavior. (B) Representative ΔFz profile of the majority behavior in each condition. Time axes cover similar robot travel distances, and green arrows at θ=45∘ and v=0.10 m/s mark the three phases used for feature design.
Figure 7: Mean tension profiles over three trials for different obstacle side surfaces at v=0.10 m/s and θ=45∘ . (A) 16.25 kg. (B) 31.25 kg.
Figure 8: Pipeline for movability classification. For each tension signal, we detect the change point tcp and extract three features from each of two post-contact windows with equal duration. The resulting six features are used in a logistic regression classifier to classify the contacted obstacle as movable or non-movable.
Window
No.
Feature name
Notation
Definition
Unit
Early
1
Peak force rise
rise_to_peak
fpeak−fbase
N
2
Normalized peak timing
peak_time_ratio
tpeak/Δt
–
3
Post-peak force drop
drop_ratio
(fpeak−fend)/(fpeak−fbase)
–
Late
4
Late-to-peak force ratio
late_mean_over_peak
(Fˉzlate−fbase)/(fpeak−fbase)
–
5
Late-phase coefficient of variation
wlate_cv
σzlate/(Fˉzlate−fbase)
–
6
Late-phase zero-crossing density
wlate_zc_detrended
Sign changes per meter after linear detrending
1/m
Table II : Six features extracted from cable tension Fz . Definitions are relative to the contact onset tcp , where early and late denote two consecutive windows after tcp , each of length Δt=D/v . The baseline force fbase is the mean of Fz over [tcp−Δt,tcp) ; the peak force fpeak and the peak time tpeak are the maximum of Fz in the early window and its time from tcp ; and the end force fend is the last sample of the early window.
Dropped Feature
D=0.05 m
D=0.10 m
D=0.15 m
D=0.20 m
D=0.25 m
D=0.30 m
(post-contact travel 2D )
(0.10 m)
(0.20 m)
(0.30 m)
(0.40 m)
(0.50 m)
(0.60 m)
rise_to_peak
−0.006
−0.061
−0.053
−0.080
−0.044
−0.024
peak_time_ratio
+0.000
−0.011
+0.007
+0.000
+0.004
+0.000
drop_ratio
+0.000
+0.000
+0.007
−0.004
+0.000
−0.010
late_mean_over_peak
+0.000
−0.083
−0.076
−0.046
−0.021
+0.000
wlate_cv
−0.068
−0.007
−0.014
+0.007
+0.000
+0.000
Table III : Drop-one feature ablation across prediction distances D (stratified 5-fold cross-validation). Each value is the change in mean balanced accuracy when that feature is dropped from the full six-feature set, ΔBA=BAdrop−BAfull ; more negative values indicate greater importance. Bold marks the largest negative ΔBA in each column.
Figure 9: Early and late feature-extraction windows for different travel distances D at v=0.10 m/s, where each window has duration Δt=D/v . Bold marks the selected D .
Figure 10: Logistic-regression decision slice at D=0.15 m, with the remaining four features fixed at their dataset medians. The solid line is the decision boundary, and circles denote misclassified trials, numbered as in Section V-D .
Figure 11: Distributions of the six features for movable (Mov., n=71 ), rotation-only (Rot., n=20 ), and stationary (Stat., n=26 ) interactions at D=0.15 m. The green dashed line marks a late-phase tension equal to the peak rise.
Safe robot navigation develops methods for reaching goals while avoiding collisions and managing uncertainty, dynamic obstacles, and environmental hazards.
In motion planning algorithms for tethered mobile robots, the entanglement state of the tether is a critical aspect to consider during the planning phase. This is particularly important in case of a slack tether, where the shape of the tether is not determined solely by the geometry of the environment and the location of the obstacles, but also by the dynamics of the tether, by the trajectory followed by the robot, and possibly by exogenous forces. In this scenario, preventing entanglement requires planning a robot trajectory that accounts for the entanglement definition and for the dynamics of the robot and of the tether. In this work, we propose a motion planning algorithm for tethered mobile robots with a slack tether that computes dynamically feasible entanglement-free trajectories to navigate through an environment with static obstacles. By considering the entanglement state during all the stages of the planning pipeline, we are able to compute safer trajectories that avoid entanglement during the motion of the robot. We achieve this through a three-step pipeline, which includes (i) the construction of a topological model of the entanglement-free configuration space of the tethered robot, (ii) the generation of a set of candidate paths using this model, and (iii) the computation of a dynamically feasible entanglement-free trajectory by solving a homotopy-constrained trajectory generation problem. The resulting trajectory can then be executed to lead the robot to its target location, while maintaining the tether in an entanglement-free configuration. We demonstrate the benefits of this algorithm in simulations, where we show how the planning algorithm avoids violations of the entanglement constraints, resulting in safer and more reliable trajectories.
Gianpietro Battocletti, Dimitris Boskos, Bart De Schutter
Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
In extreme environments such as underwater exploration and post-disaster rescue, tethered robots require continuous navigation while avoiding cable entanglement. Traditional planners struggle in these lifelong planning scenarios due to topological unawareness, while topology-augmented graph-search methods face computational bottlenecks in obstacle-rich environments where the number of candidate topological classes increases. To address these challenges, we propose Topological Motion Planning Diffusion (TMPD), a novel generative planning framework that integrates lifelong topological memory. Instead of relying on sequential graph search, TMPD leverages a diffusion model to propose a multimodal front-end of kinematically feasible trajectory candidates across various homotopy classes. A tether-aware topological back-end then filters and optimizes these candidates by computing generalized winding numbers to evaluate their topological energy against the accumulated tether configuration. Benchmarking in obstacle-rich simulated environments demonstrates that TMPD achieves a collision-free reach of 100% and a tangle-free rate of 97.0%, outperforming traditional topological search and purely kinematic diffusion baselines in both geometric smoothness and computational efficiency. Simulation with realistic cable dynamics further validates the practicality of the proposed approach.
Cable/rope elements are pervasive in deformable-object manipulation, often serving as a deformable force-transmission medium whose routing and contact determine how wrenches are delivered. In cable-towed manipulation, transmission is unilateral and hybrid: the tether can pull only when taut and becomes force-free when slack; in practice, the tether may also contact the object boundary and self-wrap around edges, which is not merely collision avoidance but a change of the wrench transmission channel by shifting the effective application point and moment arm, thereby coupling routing geometry with rigid-body motion and tensioning. We formulate self-wrap towing as a routing-aware, tensioning-implicit trajectory optimization (TITO) problem that couples (i) a tensioning-implicit taut/slack constraint and (ii) routing-conditioned transmission maps for effective length and wrench, and we build a relaxation hierarchy from a strict mode-conditioned reference to three tractable relaxations: Full-Mode Relaxation (FMR), Binary-Mode Relaxation (BMR), and Implicit-Mode Relaxation (IMR). Across planar towing tasks, we find that making routing an explicit decision often yields conservative solutions that stay near switching boundaries, whereas IMR induces self-wrap through state evolution and exploits the redirected torque channel whenever turning requires it.
Yu Li, Amin Fakhari, Hamid Sadeghian
Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, Germany. · The author is with the Department of Mechanical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.