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Dense-Joint-Based Obstacle-Aided Locomotion with a Joint-Repositionable Snake Robot
Kyosuke Minomo, Ryo Takahashi, Kotaro Yasui, Yasutaka Nakashima, Motoji Yamamoto, Ayato Kanada
TL;DR
Sparse-joint snake robots can make discontinuous obstacle contact and suffer stagnation, while biological snakes maintain contact with many vertebrae. The paper compares equal-length high- and low-density joint-repositionable robots in obstacle environments; higher density smooths contact transitions and reduces power consumption during successful propulsion.
Problem
Snake robots typically have tens of joints rather than the hundreds of vertebrae in biological snakes, and their low joint density is associated with discontinuous obstacle contact, jamming, unstable propulsion, and power loss.
Source: §I · p. 1Method
Results
In the 160 mm environment, the High-Density model averaged approximately 5.4 W and the Low-Density model approximately 8.5 W in power consumption increment; higher density also suppressed abrupt contact and reaction-force transitions.
Source: §I · p. 2, C. Results · p. 6Takeaways & Limitations
Within these obstacle experiments, higher joint density maintained smoother obstacle contact and supported more stable, efficient propulsion, particularly in challenging small-obstacle environments.
Source: §V · p. 7
Abstract
from arXiv · showhide
Obstacle-aided locomotion is a fundamental capability for snake robots to traverse complex environments. However, conventional rigid-link snake robots often suffer from stagnation or jamming caused by their low joint density (i.e., the number of joints per unit length). This results in discontinuous contact with obstacles, unlike the continuous adaptation of biological snakes. To investigate the effect of joint density on obstacle-aided locomotion performance, we utilized a joint-repositionable snake robot mechanism that decouples actuators from joints, enabling a high-density architecture. We developed two experimental models with identical total lengths but different joint densities (high-density and low-density) and conducted comparative propulsion experiments in obstacle environments with varying obstacle diameters. The experimental results demonstrate that the high-density model substantially suppresses the abrupt shifts in reaction forces that cause stagnation in the low-density model. By maintaining smooth contact points, the high-density configuration reduces power consumption and achieves stable, continuous propulsion. These results highlight high joint density as a key factor in improving the environmental adaptability of snake robots in complex terrains.
I. INTRODUCTION
Snake robots have far fewer joints per unit length than biological snakes, and sparse joints can make obstacle contact discontinuous and propulsion unstable. This study uses a joint-repositionable robot to compare joint densities and finds that higher density supports smoother contact and more stable, lower-energy propulsion.
I. INTRODUCTION
- Biological snakes have hundreds of vertebrae, whereas typical snake robots use tens of joints, leaving a substantial joint-density gap.Dense articulation is presented as relevant to search-and-rescue in complex terrains.Source: §I · p. 1
- The conceptual comparison depicts smooth obstacle conformity for the high-density configuration and coarse bending with abrupt reaction-force shifts for the low-density configuration.The low-density shifts are linked to stagnation, while the high-density configuration is depicted as supporting continuous propulsion.Source: Figure 1 · p. 1
- Low joint density can force coarse bending and discontinuous obstacle interaction, which is associated with geometric locking or jamming, unstable propulsion, and power loss.The paper positions joint density as a distinct mechanical factor alongside prior approaches using active contact feedback or passive compliance.Source: §I · p. 1
- The study compares high- and low-density robots with identical overall dimensions in obstacle environments with varying diameters to examine the effect of body discretization on locomotion.The joint-repositionable mechanism decouples actuators from joints, enabling a high-density articulated body while preserving independent local shape control.Source: §I · p. 2
- Higher joint density smooths body-wave propagation and contact transitions, suppressing reaction-force shifts associated with stagnation and lowering energy consumption.The improvement is attributed primarily to finer spatial discretization of the body curve, rather than rack-gear compliance.Source: §I · p. 2
II. ROBOT MECHANISM
The joint-repositionable robot uses movable and fixed joint units mounted on flexible racks. Its movable units can translate along the body or bend it, decoupling joint count from actuator count.
II. ROBOT MECHANISM
- Movable joint units travel along flexible rack gears, while passive links connect through rotational joints; each movable unit has two servomotors and passive gears for posture stabilization.The overall mechanism combines movable and fixed joint units mounted on the racks.Source: A. Joint-Repositionable System · p. 2, Figure 2 · p. 2
- Opposite motor rotations translate a movable joint unit along the body, allowing the actuator position to change dynamically.The movable unit travels back and forth inside the robot along the rack gears.Source: A. Joint-Repositionable System · p. 2, Figure 4 · p. 3
- Same-direction motor rotations push one rack forward and pull the other backward, bending the body in an S shape around the joint unit.Rack tips are fixed, so the difference in rack length produces a bending moment.Source: A. Joint-Repositionable System · p. 2, Figure 4 · p. 3
- A fixed rear joint unit bends the trailing section without translating, while the movable and fixed units together decouple joint number from actuator number.The fixed unit uses one motor to drive both rack gears.Source: A. Joint-Repositionable System · pp. 2–3
B. Design for Joint Density Comparison
The comparison models share a 1500 mm total body length but differ in link length and joint density. The high-density model has twice the joint density of the low-density model.
B. Design for Joint Density Comparison
- Both experimental models have a total body length of 1500 mm, controlling overall length while joint density varies.The models were built to test whether joint density affects obstacle-aided locomotion performance.Source: B. Design for Joint Density Comparison · p. 3
- The High-Density Model has 48 links of 30 mm, while the Low-Density Model has 24 links of 60 mm, giving the former twice the joint density.Both models include two common 30 mm rear links for the fixed joint unit, keeping total length at 1500 mm.Source: B. Design for Joint Density Comparison · p. 3
- Spherical-tipped legs provide isotropic, low-friction contact with the floor to remove floor-friction propulsion from the experiment.The experiment therefore tests propulsion using reaction forces from obstacles.Source: B. Design for Joint Density Comparison · p. 3
III. OBSTACLE-AIDED LOCOMOTION WITH THE JOINT-REPOSITIONABLE SNAKE ROBOT
The robot is designed to propel itself by pushing against obstacles and converting their reaction forces into forward thrust. The experiment uses alternating obstacle contacts to support stable propulsion while limiting sideslip and rotation.
III. OBSTACLE-AIDED LOCOMOTION WITH THE JOINT-REPOSITIONABLE SNAKE ROBOT
- The locomotion method uses forces exerted on environmental objects and converts their reaction forces into thrust in the direction of travel.The section introduces the motion-generation method for the fabricated experimental robot.Source: A. Propulsion Principle and Contact… · p. 3, §III · p. 3
A. Propulsion Principle and Contact Conditions
- Spherical legs on the links reduce ground friction and make it isotropic, removing floor-friction propulsion from the test.This isolates propulsion based on obstacle reaction forces.Source: Figure 6 · p. 3, A. Propulsion Principle and Contact… · p. 3
- The experimental contact condition maintains simultaneous contact with three obstacles arranged alternately on either side of the robot.The setup aims to synthesize reaction forces for stable propulsion while suppressing sideslip and rotation; the cited principle states that efficient obstacle-force propulsion requires at least three contact points.Source: A. Propulsion Principle and Contact… · p. 3
B. Motion Generation with Joint Units
The robot uses movable joint units to translate a shaped body profile backward while preserving its bends, producing forward propulsion from obstacle contacts.
B. Motion Generation with Joint Units
- Four joint units—three movable and one fixed—control four continuous arc segments, corresponding to the leading line, S-shaped curve, and trailing line.One joint unit determines the shape of each arc segment.Source: B. Motion Generation with Joint Units · pp. 3–4
- Fig. 7 depicts a four-segment target body shape pressing against three alternating contact points.The target profile is modeled as four continuous arc segments.Source: Figure 7 · p. 4, B. Motion Generation with Joint Units · p. 4
- Movable joint units travel backward along rack gears while maintaining the bend, shifting the S-shaped profile posteriorly and generating forward propulsion through obstacle reactions.The units move at a uniform velocity under constant motor speeds.Source: B. Motion Generation with Joint Units · p. 4
C. Control Parameters and Experimental Conditions
The models used identical propulsion control parameters apart from joint density, and the comparative experiments evaluated the propulsion phase rather than complete locomotion cycles.
C. Control Parameters and Experimental Conditions
- Both models received the same target shape, 16 rpm motor speed, and 16 s operating time; other control parameters were held identical.The target S-bend used two connected arcs with radius 420 mm and central angle 25° in opposite directions.Source: C. Control Parameters and Experimental… · p. 4
- Evaluation excluded reset motion and covered only the propulsion phase, because movable units eventually reach the robot’s rear end and must return forward for continued locomotion.Reset temporarily halts propulsion while the body shape relaxes.Source: C. Control Parameters and Experimental… · p. 4
IV. EXPERIMENTAL EVALUATION
- The experiments compared the High-Density and Low-Density models to assess joint-density effects on obstacle-aided locomotion performance.Source: §IV · p. 4
A. Experimental Objectives
The experiments examine whether increasing joint density can mitigate stagnation associated with discrete, link-by-link obstacle contact, across environments of differing geometric difficulty.
A. Experimental Objectives
- The experiment tests whether higher joint density can mitigate stagnation caused by discrete link-by-link contact and abrupt reaction-force fluctuations.The high-density structure is intended to maintain continuous contact and detachment across multiple links.Source: A. Experimental Objectives · p. 4
1) Obstacle Configurations:
- Fig. 9 marks successful propulsion in green when contact is maintained over time and stagnation in red when geometric constraints fail and the robot slips backward.The snapshots cover obstacle diameters from 160 mm to 40 mm.Source: Figure 9 · p. 5
- Obstacle diameters of 160, 120, 80, and 40 mm span relatively easy to highly challenging conditions; smaller obstacles make stagnation more likely.Smaller radii shift the surface normal and reaction-force vector more abruptly as contact moves along a link.Source: 1) Obstacle Configurations · pp. 4–5
- Three obstacles were placed alternately on each side, with their centers adjusted so initial-shape contact points matched across environments.This arrangement kept the initial contact geometry consistent for comparisons.Source: 1) Obstacle Configurations · p. 5
- A smooth steel floor plate removed leg-ground friction so propulsion arose solely from obstacle reaction forces.The fixed obstacles were secured to the plate with magnets and adhesive tape.Source: 1) Obstacle Configurations · p. 5
2) Evaluation Metrics:
Propulsion performance was evaluated through travel distance, incremental power consumption, and energy efficiency during active propulsion.
2) Evaluation Metrics:
- Travel distance was computed from motion-capture tracking of a marker on the robot’s head.Source: 2) Evaluation Metrics · p. 5
- Pinc isolates obstacle-condition power above the flat-ground baseline: Pinc = Ptotal − Pbase, with each term summed across seven motors.Ptotal is measured under each condition; Pbase is measured during flat-ground locomotion without obstacles.Source: 2) Evaluation Metrics · p. 5
- CoT is the dimensionless ratio of average total power to mg times average forward velocity, evaluated only during active propulsion.Stagnation begins when forward velocity remains below 0.5 mm/s for 0.5 s; later data are excluded to avoid underestimating transport cost.Source: 2) Evaluation Metrics · p. 5
C. Results
The High-Density model maintained propulsion in all but the 40 mm environment, while the Low-Density model stagnated in smaller-obstacle environments. It generally used less power and had lower CoT in successful environments.
C. Results
- In successful propulsion, the S-shaped body maintained obstacle contact points, whereas in stagnating trials its bending section slipped backward and lost forward propulsion.Fig. 10 reports means and standard deviations across four trials.Source: C. Results · p. 5, Figure 10 · p. 6
- In 160 mm obstacles, the Low-Density model traveled approximately 54 mm less than the High-Density model; it stagnated in the other small-diameter environments, while the High-Density model moved continuously except at 40 mm.Fig. 10 plots travel distance over time; stagnation occurred when joints continued actuating but the body stopped advancing.Source: C. Results · p. 6, Figure 10 · p. 6
- In the successful 160 mm environment, average Pinc was approximately 5.4 W for High-Density and 8.5 W for Low-Density.Across periods when both models moved, High-Density generally used less power; at 40 mm, both peaked at stagnation onset before power decreased as posture collapsed.Source: C. Results · p. 6, Figure 10 · p. 6
- Across successful 160–80 mm environments, High-Density CoT remained around 47–52, while Low-Density CoT rose from 91.1 at 160 mm to 197.6 at 80 mm.Lower CoT indicates higher efficiency; at 40 mm, both models had extremely high CoT due to near-zero velocity.Source: C. Results · p. 6, Figure 11 · p. 6
D. Discussion
The discussion links propulsion efficiency to seamless reaction-force handoffs between links, which high joint density supports by approximating a continuous body curve. Both models nevertheless stagnated at 40 mm because obstacles could lodge in inter-link gaps.
D. Discussion
- Low-Density links undergo larger angular deviations before detachment, so the next link contacts at a suboptimal angle and redirects reaction forces inward as lateral loads; High-Density smooths these transitions across 160–80 mm environments.The inward loads deform the body, waste motor power, and increase CoT.Source: D. Discussion · p. 7
- At 40 mm, obstacles can lodge in the gaps required for joint rotation, causing geometric locking, posture collapse, sharp power peaks, and extreme CoT in both models.In larger environments, the same gaps caused only minor periodic velocity drops.Source: D. Discussion · p. 7
V. CONCLUSION
The study compares equal-length snake robot models with different joint densities in obstacle environments. It concludes that high-density joints support smooth contact and stable, efficient propulsion, while identifying long-distance locomotion, adaptive control, and gap elimination as future work.
V. CONCLUSION
- The study addresses stagnation from discrete rigid-link contact by comparing two joint-density models across environments with different obstacle diameters.The proposed structural approach is a high-density joint configuration.Source: §V · p. 7
- High-density joints maintained smooth obstacle contact and stable, efficient propulsion, with the effect particularly pronounced for small-diameter obstacles.The Low-Density model suffered postural deformation and stagnation attributed to geometric incompatibility.Source: §V · p. 7
- Future work targets continuous long-distance locomotion, reaction-force sensing for autonomous adaptive control, and flexible skin to eliminate inter-link gaps.The proposed skin is intended to prevent geometric locking and improve traversal in extreme environments.Source: §V · p. 7