Improved hopping control on slopes for small robots using spring mass modeling
- URL: http://arxiv.org/abs/2603.05902v1
- Date: Fri, 06 Mar 2026 04:42:34 GMT
- Title: Improved hopping control on slopes for small robots using spring mass modeling
- Abstract summary: Hopping robots often lose balance on slopes because the tilted ground creates unwanted rotation at landing.<n>This work analyzes that effect using a simple spring mass model and identifies how slope induced impulses destabilize the robot.<n>We introduce two straightforward fixes, adjusting the bodys touchdown angle based on the slope and applying a small corrective torque before takeoff.
- Score: 0.9857968274865204
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hopping robots often lose balance on slopes because the tilted ground creates unwanted rotation at landing. This work analyzes that effect using a simple spring mass model and identifies how slope induced impulses destabilize the robot. To address this, we introduce two straightforward fixes, adjusting the bodys touchdown angle based on the slope and applying a small corrective torque before takeoff. Together, these steps effectively cancel the unwanted rotation caused by inclined terrain, allowing the robot to land smoothly and maintain stable hopping even on steep slopes. Moreover, the proposed method remains simple enough to implement on low cost robotic platforms without requiring complex sensing or computation. By combining this analytical model with minimal control actions, this approach provides a practical path toward reliable hopping on uneven terrain. The results from simulation confirm that even small slope aware adjustments can dramatically improve landing stability, making the technique suitable for future autonomous field robots that must navigate natural environments such as hills, rubble, and irregular outdoor landscapes.
Related papers
- Robust bipedal locomotion on flowable slopes via foot-driven terrain manipulation [9.438718890087712]
Bipedal robots are challenging to control because they operate close to instability.<n>Small variations in foot-induced contact can rapidly destabilize locomotion.<n>We study how controlling terrain response can improve bipedal locomotion on granular slopes.
arXiv Detail & Related papers (2026-07-13T17:42:03Z) - Physics-Guided Biomechanical Gait Adaptation for Humanoid Locomotion on Extreme Sloped Terrains [8.87337310412389]
We propose a novel two-stage physics-guided framework, dubbed HumoSlope, dedicated to robust humanoid locomotion on sloped terrains.<n>Our framework effectively mitigates posture degeneration and enables blind, continuoustraversal of outdoor grass slopes up to 62.7%.
arXiv Detail & Related papers (2026-07-08T18:12:18Z) - Dynamic Modeling and Attitude Control of a Reaction-Wheel-Based Low-Gravity Bipedal Hopper [0.0]
hopping locomotion offers high energy efficiency but is prone to mid-flight attitude instability.<n>This paper presents an underactuated bipedal hopping robot that employs an internal reaction wheel to regulate body posture.<n>The proposed framework is evaluated in MuJoCo-based simulations under lunar gravity conditions.
arXiv Detail & Related papers (2026-03-11T11:35:21Z) - Failure Mechanisms and Risk Estimation for Legged Robot Locomotion on Granular Slopes [1.6267479602370543]
Locomotion on granular slopes is a challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media.<n>Using a hexapedal robot on a tiltable granular bed, we measure locomotion speed together with slope-dependent normal and shear granular resistive forces.<n>We develop a simple robot-terrain interaction model that predicts anchoring timing, step length, and resulting robot speed, as functions of terrain strength and slope angle.
arXiv Detail & Related papers (2026-03-06T22:58:45Z) - A 26-Gram Butterfly-Inspired Robot Achieving Autonomous Tailless Flight [61.21669716392821]
textitAirPulse is a 26-gram butterfly-inspired robot that achieves the first onboard, closed-loop controlled flight for a tailless two-winged platform at this scale.<n>It replicates key biomechanical traits of butterfly flight, utilizing low-aspect-ratio, compliant carbon-fiber-reinforced wings and low-frequency flapping that reproduces characteristic biological body undulations.
arXiv Detail & Related papers (2026-02-06T15:58:50Z) - Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction [86.53248703859718]
We introduce a perceptive locomotion framework that merges terrain sensing, gait regulation, and whole-body control into a single reinforcement learning policy.<n>Experiments conducted on a 31-DoF, 1.65 m humanoid robot demonstrate robust locomotion in both simulation and real-world settings.
arXiv Detail & Related papers (2025-12-08T11:42:41Z) - Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
arXiv Detail & Related papers (2025-02-24T14:53:45Z) - Learning to enhance multi-legged robot on rugged landscapes [7.956679144631909]
Multi-legged robots offer a promising solution forNavigating rugged landscapes.
Recent studies have shown that a linear controller can ensure reliable mobility on challenging terrains.
We develop a MuJoCo-based simulator tailored to this robotic platform and use the simulation to develop a reinforcement learning-based control framework.
arXiv Detail & Related papers (2024-09-14T15:53:08Z) - Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and
Skid Compensation using Sliding-Mode Control and Deep Learning [0.0]
Compensating for slip and skid is crucial for mobile robots navigating outdoor terrains.
This paper proposes a novel trajectory tracking technique featuring real-world feasible online slip and skid compensation.
Experimental results demonstrate a significant improvement, enhancing the trajectory tracking system's performance by over 27%.
arXiv Detail & Related papers (2023-09-16T03:58:03Z) - VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait
Representation [78.92147339883137]
We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait.
We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration.
The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
arXiv Detail & Related papers (2022-05-02T19:49:53Z) - Next Steps: Learning a Disentangled Gait Representation for Versatile
Quadruped Locomotion [69.87112582900363]
Current planners are unable to vary key gait parameters continuously while the robot is in motion.
In this work we address this limitation by learning a latent space capturing the key stance phases constituting a particular gait.
We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, foot step height and full stance duration.
arXiv Detail & Related papers (2021-12-09T10:02:02Z) - Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator.
A cost-sensitive active learning approach is used to select optimal joint configurations.
The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
arXiv Detail & Related papers (2021-01-26T16:01:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.