Planning and Control for Wheel-Leg Hybrid Locomotion in Wheeled Biped Robots for Obstacle Traversal

This paper presents a wheel-leg hybrid gait planning and whole-body motion generation method for wheeled biped robots (WBR). This method utilizes momentum to evaluate the robot’s balance, and employs direct collocation to optimized generation the wheel-leg hybrid gait in real-time based on the environment elevation information. The trajectory optimization (TO) is decoupled into the forward and lateral motion to reduce the solution time for each subproblem, thereby enhancing the efficiency of the overall planning, enabling online trajectory generation at 5 Hz. An obstacle search algorithm is proposed to identify the obstacles along the robot’s reference path and construct terrain and safety constraints, which are incorporated into the optimization problem through exponential control barrier function. This facilitates the online generation of the robot’s contact sequence, ensuring that wheels movement remain within a safe region. Finally, a balance and trajectory tracking controller based on nonlinear model predictive control (NMPC) is proposed to generate the CoM’s spatial motion and contact trajectory at a higher update frequency (200Hz), which are then mapped into the joint space using inverse kinematic, enabling the robot to track the reference gait trajectory while maintaining balance. Experimental validation on a hydraulically driven WBR demonstrates that the method enables periodic hybrid gait generation and real-time obstacle search and traversal.

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Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tase.2025.3587848
Citations
1
Primary Topic
Robotic Locomotion and Control
Type
article
Field-Weighted Citation Impact
0.67
Controls
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article

Planning and Control for Wheel-Leg Hybrid Locomotion in Wheeled Biped Robots for Obstacle Traversal

1 citations
IEEE Transactions on Automation Science and Engineering
Robotic Locomotion and Control
0.67
article

Planning and Control for Wheel-Leg Hybrid Locomotion in Wheeled Biped Robots for Obstacle Traversal

article en
1 citations

Abstract

This paper presents a wheel-leg hybrid gait planning and whole-body motion generation method for wheeled biped robots (WBR). This method utilizes momentum to evaluate the robot’s balance, and employs direct collocation to optimized generation the wheel-leg hybrid gait in real-time based on the environment elevation information. The trajectory optimization (TO) is decoupled into the forward and lateral motion to reduce the solution time for each subproblem, thereby enhancing the efficiency of the overall planning, enabling online trajectory generation at 5 Hz. An obstacle search algorithm is proposed to identify the obstacles along the robot’s reference path and construct terrain and safety constraints, which are incorporated into the optimization problem through exponential control barrier function. This facilitates the online generation of the robot’s contact sequence, ensuring that wheels movement remain within a safe region. Finally, a balance and trajectory tracking controller based on nonlinear model predictive control (NMPC) is proposed to generate the CoM’s spatial motion and contact trajectory at a higher update frequency (200Hz), which are then mapped into the joint space using inverse kinematic, enabling the robot to track the reference gait trajectory while maintaining balance. Experimental validation on a hydraulically driven WBR demonstrates that the method enables periodic hybrid gait generation and real-time obstacle search and traversal.

IEEE Transactions on Automation Science and EngineeringVol. 22
Harbin Institute of Technology (CN), State Key Laboratory of Robotics and Systems (CN)
Sustainable cities and communities
Openalex Percentile: Top 24%
Robotic Locomotion and Control
0.67
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Planning and Control for Wheel-Leg Hybrid Locomotion in Wheeled Biped Robots for Obstacle Traversal · IEEE Transactions on Automation Science and Engineering (2025) | TGRS Research Map | TGRS