Author name cluster

Ming Cong

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

EAAI Journal 2026 Journal Article

Data-driven slippage detection via tactile sensing for stable enveloping-grasp control under external disturbances

Yongyao Li
Yufei Liu
Xinzhao Zhang
Dongdong Zheng
Xu Song
Ming Cong
Dongchen Liu
Yu Du

Enveloping-grasp control using tactile sensors for slippage detection has garnered significant attention in recent years. However, it remains an open problem in practical applications. This work proposes a data-driven slippage identification framework to address the challenge of enveloping-grasp control. The framework employs a tactile sensor array composed of micro-electro-mechanical barometers covered with soft material, which perceives contact behavior as spatiotemporal data. In the absence of detailed knowledge of interaction dynamics, the slippage information is estimated using a tactile learning model with convolutional computations. The slippage identification output is integrated into an enveloping-grasp controller to resist slippage. Compared to previous studies, our method is cost-effective and does not require large amounts of data or high computational power. Extensive experiments validate the effectiveness and generalization capability of the proposed framework, demonstrating that the slippage identification method exhibits high classification accuracy for grasp status of both known and unknown objects.

Details DOI

EAAI Journal 2025 Journal Article

A fruit fly-inspired path planning algorithm for unmanned aerial vehicle in underground environments based on low-discrepancy sequences

Huageng Zhong
Yu Du
Dong Liu
Minghao Wang
Ming Cong
Xiaojing Tian

Sampling planning algorithms are crucial in high-dimensional path planning for unmanned aerial vehicle (UAV), particularly in underground environments where Global Positioning System (GPS) signals are absent. The Rapidly-exploring Random Tree (RRT) algorithm, however, faces challenges due to the use of pseudo-random sequences, resulting in issues such as under-sampling, over-sampling, and they also suffer from high computational costs and redundant paths. To address these limitations, the (Halton-based Clustering) HBC-RRT algorithm is proposed. This algorithm utilizes the Halton sequence, replacing the pseudo-random sequence, which fundamentally resolves the problems of under-sampling and over-sampling in the RRT method. Additionally, a novel sampler is introduced, inspired by the fruit fly guidance mechanism, which optimizes the dual-tree sampling process by selecting either the optimal sampling candidate strategy or virtual sub-goal points. The optimal sampling candidate strategy conducts global sampling in three-dimensional space, effectively reducing sampling time. The virtual sub-goal points accelerate the dual-tree connection process, enhancing the directional guidance. The fruit fly mechanism reconstructs the path, leading to a reduction in UAV path costs. The performance of the HBC-RRT algorithm is evaluated through simulation experiments, demonstrating superior results in comparison to four baseline algorithms, including reductions in path length, convergence time, and memory usage. The practical applicability of the algorithm is further validated through real-world experiments involving a UAV and radar in an underground environment.

Details DOI

EAAI Journal 2025 Journal Article

A human-robot interaction system for automated chemical experiments based on vision and natural language processing semantics

Zhuang Yang
Yu Du
Dong Liu
Kesong Zhao
Ming Cong

Using collaborative robots to replace researchers in performing repetitive and hazardous chemical experiments can effectively enhance experimental efficiency. However, this technology still faces several challenges, including understanding researchers' natural language instructions, autonomously generating action sequences, and more. Therefore, we developed a general control framework for robots in automated chemical experiments based on visual and natural language semantic information. Firstly, starting with the recognition of keywords within Chinese language instructions, we established a domain dictionary for chemical experiment operations and proposed an instruction understanding model based on the bidirectional long-short-term memory and conditional random field(BiLSTM-CRF), enhancing the robot's cognitive ability towards user instructions. Then, a rule matching method for chemical experimental information and a multimodal information feature matching mechanism were established for command content verification and the automatic generation of multiple types of structured language. At the same time, a robot feedback mechanism was added, enabling human-computer interaction and establishing closed-loop control of the system. Finally, propose a robot action sequence generation mechanism based on hierarchical finite state machines(HFSM), transforming structured language into operational strategies for chemical experiments required by the robot. Experimental results show that on the instruction task comprehension dataset created in this paper, the proposed method improves the F1 score by up to 4. 44% in the instruction keyword extraction task compared to other models. In addition, compared to traditional manual teaching control, this method significantly reduces time costs. This verifies that the method effectively enhances the robot's ability to comprehend Chinese instructions and generates reliable executable action sequences.

Details DOI

EAAI Journal 2024 Journal Article

HB-RRT:A path planning algorithm for mobile robots using Halton sequence-based rapidly-exploring random tree

Huageng Zhong
Ming Cong
Minghao Wang
Yu Du
Dong Liu

Path planning remains crucial for efficient robot operation. A Halton Biased Rapidly-exploring Random Tree (HB-RRT) path planning algorithm is introduced in this study. The Halton sequence, known for its uniform distribution and low discrepancy, is employed for sampling. Issues arising from the pseudo-random sequence in the standard RRT algorithm, leading to uneven distribution of sampling points, are addressed. A mouse-inspired goal-oriented strategy and a candidate sampling pool strategy are incorporated to enhance the sampling point quality, thereby addressing the challenge of insufficient memory during node expansion. Path optimization is further achieved through a multi-level planning approach, which aims to minimize redundancy. A subsequent smoothing of the path is conducted using a cubic B-spline method. Comparisons with the RRT, Bionic Target Bias-RRT, and Informed-RRT* algorithms, through both numerical simulations and real-world testing, confirm the superiority of the HB-RRT algorithm in terms of planning time, path length, and overall path quality.

Details DOI