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Shaoxun Liu

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Resilient UAV Swarm with Fast Connectivity Recovery and Extensive Coverage

Yabin Peng
Chenyu Zhou
Hainan Cui
Tong Duan
Haoyang Chen
Fan Zhang
Shaoxun Liu

To address partial node failures in unmanned aerial vehicle swarms, self-healing communication techniques are commonly employed to restore backbone connectivity while preserving area coverage. However, existing heuristic methods struggle to scale under large-scale failures and dynamic conditions, while learning-based approaches often suffer from spatial collapse, resulting in significant coverage loss. To overcome these limitations, we propose a resilient self-healing framework that enables rapid connectivity recovery and wide-area coverage through a divide-and-conquer strategy. First, we introduce a buffered dynamic virtual force expansion mechanism that categorizes pairwise distances into repulsive, neutral, and attractive zones, allowing nodes to disperse appropriately while preserving communication links and maintaining safety buffers. Subsequently, we design a multipartite graph convolution module to reason over subnetwork-level interactions and facilitate cross-subnetwork reconnection with global structural awareness. Finally, we develop an adaptive fusion strategy that combines both outputs with time-aware weighting to generate the final motion decisions. Experimental results in both random and uniform deployment scenarios demonstrate that our approach outperforms many state-of-the-art methods in terms of connectivity restoration speed and communication coverage.

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IROS Conference 2025 Conference Paper

Learning Robust and Flexible Locomotion of Wheel-Legged Quadruped Robots in Complex Terrains

Shiyu Zhou
Shaoxun Liu
Rongrong Wang

The wheel-legged quadruped robot, equipped with leg and end-wheel structures, possesses the capability to traverse continuous surfaces at relatively high speeds while also being able to navigate unstructured terrains. However, designing its controller using traditional methods presents significant challenges, particularly under conditions of limited or lost external environmental perception and highly variable terrain complexity. In light of this issue, this paper proposes a novel, concise, and effective reinforcement learning framework. The framework employs an asymmetric actor-critic structure incorporating a velocity estimation network and leverages multi-contact states generated by a central pattern generator for fusion, thereby training a single control policy to address the robust and flexible traversal of complex terrains by wheel-legged robots relying solely on an inertial measurement unit and joint sensors. Our method enables the modified Unitree Go1-based wheel-legged robot to traverse various challenging terrains, such as steps, high obstacles, rough terrain, and low-adhesion surfaces, while ensuring efficient locomotion performance on smooth and continuous surfaces. The effectiveness of the framework’s training results has been validated through testing in both simulation and real-world environments.

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IROS Conference 2023 Conference Paper

Load Awareness: Sensorless Body Payload Sensing and Localization for Heavy Quadruped Robot

Shaoxun Liu
Shiyu Zhou
Zheng Pan
Zhihua Niu
Rongrong Wang

Heavy quadrupedal drives have great potential for overcoming obstacles, showing great possibilities for transportation industries in complex environments. Ground reaction force (GRF) is a crucial state variable for quadrupedal control. Most GRF observations are implemented in lightweight quadrupeds, with little consideration of the loading being static or slippery on the body. However, the load information is vital to the heavy-duty quadruped applied in transportation tasks. In this paper, we disassembled the whole-body dynamics into the body dynamics combined with the individual floating single-leg dynamics and completed observing the virtual coupling effects between the body and legs. Based on the observed coupling force and centroidal dynamics (CD), the GRF of a stance leg is obtained without the awareness of body weight, movement, and load information. Furthermore, we utilized the body dynamics and the observed virtual force to obtain the body's unknown payload. By reconstructing the moment balance equation, we obtained the payload's position concerning the body coordinate. Compared to conventional quadrupedal GRF observation methods, this framework achieves higher observation accuracy in heavy quadrupeds without load and body information. Additionally, it enables real-time calculation of load magnitude and position.

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