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Dongming Han

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3 papers

2 author rows

IROS Conference 2025 Conference Paper

Neural network control method for target tracking of magnetically actuated capsule endoscopic robots with obstacle avoidance and noise-resistant capabilities

Zhiwei Cui
Yichong Sun
Dongming Han
Philip Wai Yan Chiu
Zheng Li 0012

Magnetically actuated capsule endoscopic robots (MACERs) are becoming increasingly popular because they can reach deep diseased regions inside the body that are difficult or inaccessible to traditional endoscopes without the restriction of mechanical transmission medium. However, MACERs are highly nonlinear, hence achieving obstacle avoidance, safe, and stable target tracking control of MACERs remains a challenging research topic. Therefore, to satisfy the diagnosis and treatment needs of the deep diseased regions inside the body, this paper designs a MACER target tracking neural network control method with obstacle avoidance and noise-resistant capabilities. Firstly, the kinematics and obstacle avoidance model of the MACER are established, and then a moving target tracking control scheme of robot with joint motion constraints and obstacle avoidance capabilities is designed. Next, a noise-resistant neural network is designed to quickly solve the MACER’s control scheme, thereby achieving safe, obstacle avoidance, and stable target tracking control of the MACER. Finally, the effectiveness and practicability of the proposed method are checked by simulation analysis and experiment on MACER, and compared with the existing methods. The experimental results indicate that the neural network method proposed can effectively control the MACER to track the target motion along the gastric wall curve. Compared with existing methods, the designed method has stronger anti-noise interference ability, the convergence accuracy of the proposed method is improved by 1. 3 times, and the computational burden is reduced by 26. 7 times.

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ICRA Conference 2024 Conference Paper

Bayesian-Guided Evolutionary Strategy with RRT for Multi-Robot Exploration

Shuge Wu
Chunzheng Wang
Jiayi Pan
Dongming Han
Zhongliang Zhao

With the increasing demand for multi-robot exploration of unknown environments, how to accomplish this problem efficiently has become a focus of research. However, in this kind of task, the formulation of strategies for frontier point detection and task allocation largely determines the overall efficiency of the system. In the task of multi-robot exploration of unknown environments, the strategies of frontier point detection and task assignment determine the overall efficiency of the system. Most of the existing methods implement frontier point detection based on the Rapidly-Exploring Random Tree (RRT) and use greedy algorithms for task allocation. However, the classical RRT algorithm is a fixed growth step, which leads to the difficulty of growing branches in narrow environments, making the efficiency and correctness of detecting frontier points lower. Meanwhile, the allocation strategy of the greedy algorithm causes each robot to consider only the exploration area with the largest gain for itself, which easily leads to repeated exploration and reduces the overall efficiency of the system. To solve these problems, we propose an adaptive RRT tree growth strategy for frontier point detection, which can adjust the step size according to the known map information and thus improve the efficiency and accuracy of detection; and introduce a Bayesian-guided evolutionary strategy(BGE) for efficient task allocation, which can utilize the current and historical information to find the optimal allocation scheme in a global perspective. We conduct a comprehensive test of the proposed strategy in the ROS system as well as in the real world, which proves the efficiency of our strategy. Our code is open-sourced and can be provided under request.

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TIST Journal 2022 Journal Article

Federated Multi-task Graph Learning

Yijing Liu
Dongming Han
Jianwei Zhang
Haiyang Zhu
Mingliang Xu
Wei Chen

Distributed processing and analysis of large-scale graph data remain challenging because of the high-level discrepancy among graphs. This study investigates a novel subproblem: the distributed multi-task learning on the graph, which jointly learns multiple analysis tasks from decentralized graphs. We propose a federated multi-task graph learning (FMTGL) framework to solve the problem within a privacy-preserving and scalable scheme. Its core is an innovative data-fusion mechanism and a low-latency distributed optimization method. The former captures multi-source data relatedness and generates universal task representation for local task analysis. The latter enables the quick update of our framework with gradients sparsification and tree-based aggregation. As a theoretical result, the proposed optimization method has a convergence rate interpolates between \( \mathcal {O}(1/T) \) and \( \mathcal {O}(1/\sqrt {T}) \), up to logarithmic terms. Unlike previous studies, our work analyzes the convergence behavior with adaptive stepsize selection and non-convex assumption. Experimental results on three graph datasets verify the effectiveness and scalability of FMTGL.

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