Tilt-Ropter: A Novel Hybrid Aerial and Terrestrial Vehicle with Tilt Rotors and Passive Wheels

• URL: http://arxiv.org/abs/2602.01700v1
• Date: Mon, 02 Feb 2026 06:15:33 GMT
• Title: Tilt-Ropter: A Novel Hybrid Aerial and Terrestrial Vehicle with Tilt Rotors and Passive Wheels
• Abstract summary: We present Tilt-Ropter, a novel hybrid aerial-terrestrial vehicle (HATV)<n>The fully actuated design of Tilt-Ropter enables decoupled force and torque control.<n>Results show a 92.8% reduction in power consumption during ground locomotion.
• Score: 13.687917750845303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present Tilt-Ropter, a novel hybrid aerial-terrestrial vehicle (HATV) that combines tilt rotors with passive wheels to achieve energy-efficient multi-mode locomotion. Unlike existing under-actuated HATVs, the fully actuated design of Tilt-Ropter enables decoupled force and torque control, greatly enhancing its mobility and environmental adaptability. A nonlinear model predictive controller (NMPC) is developed to track reference trajectories and handle contact constraints across locomotion modes, while a dedicated control allocation module exploits actuation redundancy to achieve energy-efficient control of actuators. Additionally, to enhance robustness during ground contact, we introduce an external wrench estimation algorithm that estimates environmental interaction forces and torques in real time. The system is validated through both simulation and real-world experiments, including seamless air-ground transitions and trajectory tracking. Results show low tracking errors in both modes and highlight a 92.8% reduction in power consumption during ground locomotion, demonstrating the system's potential for long-duration missions across large-scale and energy-constrained environments.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• DASH Robot: Minimalistic Design and Optimal Aerial-Terrestrial Locomotion via Contact-Implicit Control [3.07869141026886]
  We present a novel and minimalistic design of an aerial-terrestrial robot DASH: Ducted Aerial Spring Hopper.<n>The goal is to enable both aerial and ground locomotion capabilities on a unified mobile robot.<n>We successfully validated the design and control of DASH through a range of tasks.
  arXiv  Detail & Related papers  (2026-07-20T21:39:25Z)
• SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors [48.60897189520386]
  We introduce the JEPA-style model for real-time quadrotor control.<n>We combine a latent dynamics model with a novel physics-inspired prober that maps frozen latents to interpretable state.<n>To reduce the dependence on expensive and unsafe real-world data collection, we develop a structured pipeline for automated dataset generation.
  arXiv  Detail & Related papers  (2026-06-22T15:00:59Z)
• Friction Characterization of a Cable-Driven Differential Actuation System for Lower-Limb Exoskeletons [42.628421392139]
  Lower-limb exoskeletons require actuation systems that can provide accurate joint torque control.<n> Conventional architectures often rely on independently actuated joints and joint-level torque sensors.<n>This paper presents a novel differential actuation architecture for hip-knee flexion/extension.
  arXiv  Detail & Related papers  (2026-06-14T19:47:32Z)
• Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout [55.19244743715421]
  Driver-WM is a driver-centric latent world model that rolls out in-cabin dynamics causally conditioned on out-cabin traffic context.<n>It unifies physical kinematics forecasting with auxiliary behavioral and emotional semantic recognition.<n>Driver-WM yields robust long-horizon geometric forecasting for reactive high-motion maneuvers.
  arXiv  Detail & Related papers  (2026-05-06T16:30:48Z)
• Trajectory-based actuator identification via differentiable simulation [0.0]
  We present a trajectory-based actuator identification method that uses differentiable simulation to fit system-level actuator models.<n>We optimize actuator and simulator parameters by backpropagating through a simulator without torque sensors, current/voltage measurements, or access to embedded motor-control internals.
  arXiv  Detail & Related papers  (2026-04-11T21:36:34Z)
• 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)
• Receding-Horizon Nullspace Optimization for Actuation-Aware Control Allocation in Omnidirectional UAVs [2.573972255510259]
  Fully actuated omnidirectional UAVs enable independent control of forces and torques along all six degrees of freedom.<n> conventional control allocation methods neglect the asymmetric dynamics of the onboard actuators.<n>This work proposes a receding-horizon, actuation-aware allocation strategy that explicitly incorporates asymmetric motor dynamics.
  arXiv  Detail & Related papers  (2026-03-06T19:47:45Z)
• Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control [46.47619556874003]
  We propose a whole-body controller that combines a model-based admittance control for the manipulator arm with a Reinforcement Learning policy for legged locomotion.<n>The admittance controller maps external wrenches--such as those applied by a human during physical interaction--into desired end-effector velocities, allowing for compliant behavior.<n>We validate our approach in both simulation and hardware using the Unitree Go2 quadruped robot with a 6-DoF arm and wrist-mounted 6-DoF Force/Torque sensor.
  arXiv  Detail & Related papers  (2026-03-02T22:31:12Z)
• 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)
• STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks [58.13757830013997]
  Wireless networks based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications.<n>They face severe signal attenuation in dense environments due to obstructions.<n>To address this critical issue, we consider introducing collaborative beam of UAVs and omni-directional re-altitude beamforming.
  arXiv  Detail & Related papers  (2025-10-25T01:28:37Z)
• RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking [50.200035833530876]
  RobotDancing is a simple, scalable framework that predicts residual joint targets to explicitly correct dynamics discrepancies.<n>It can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality.
  arXiv  Detail & Related papers  (2025-09-25T03:30:34Z)
• GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control [50.67481583744243]
  We introduce GeoDrive, which explicitly integrates robust 3D geometry conditions into driving world models.<n>We propose a dynamic editing module during training to enhance the renderings by editing the positions of the vehicles.<n>Our method significantly outperforms existing models in both action accuracy and 3D spatial awareness.
  arXiv  Detail & Related papers  (2025-05-28T14:46:51Z)
• HDVIO2.0: Wind and Disturbance Estimation with Hybrid Dynamics VIO [25.489747178670125]
  We present HDVIO2.0, which models full 6-DoF, translational and rotational, vehicle dynamics and tightly incorporates them into a VIO.<n>Our system surpasses the performance of state-of-the-art methods in experiments using public and new drone dynamics datasets, as well as real-world flights in winds up to 25 km/h.
  arXiv  Detail & Related papers  (2025-04-01T17:08:27Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• 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)

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.