Author name cluster

Jianming Wang

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.

7 papers

2 author rows

EAAI Journal 2026 Journal Article

Lightweight method of foreign matter detection in coal conveying based on improved you only look once version 8 and embedded equipment

Guanfeng Du
Hongzheng Zhang
Yupeng Luo
Zhibo Bao
Zhiwei Li
Mingxin Zhou
Zhelin Liu
Shengxian Cao

During the process of conveying pulverized coal, the mixed foreign matter will not only affect the combustion efficiency of pulverized coal, but also cause safety accidents in coal conveying equipment. Therefore, it is very important to monitor the foreign matter in the process of conveying coal. Due to the limited scope of the actual conveying site, embedded equipment is needed for inspection. Aiming at the computing and memory challenges of embedded equipment, an improved lightweight YOLOv8 (you only look once version 8) algorithm is proposed. In the backbone of the algorithm, cross stage partial with 2 convolutions and lightweight PoolFormer (C2f_LPF) module is used to extract lightweight features, and foreign matter information is extracted by using multi-scale concerns in cross stage partial with deformable convolution (CSPDC) module. Then the part of the feature aggregation (PFA) module of the neck is used for lightweight feature fusion. The proposed C2f_LPF+CSPDC+PFA combination realizes a more balanced optimization of lightweight performance and detection accuracy and provides a solution to the contradiction between the limitation of computing resources of embedded equipment and the demand for real-time detection of accuracy in coal conveying. A self-made datasets contain scrap iron, stones, wooden stick and branch are trained and compared with faster region-based convolutional neural network (Faster R-CNN) and YOLO (you only look once) series algorithm on computer and embedded equipment. The datasets consist of 612 images with 4413 examples of foreign matter, which are collected on a lab-scale self-made coal conveying platform. The mean average precision (mAP), Giga floating-point operations per second (GFLOPS), parameters and frame per second (FPS) on embedded equipment are 0. 963, 6. 1, 2. 44 million and 37. 04, respectively. Compared with the original YOLOv8, the computation and parameters are reduced by 24. 7 % and 18. 9 % respectively, and the FPS is improved by 29. 6 %. By contrast, it also has better results than other algorithms, which is well compatible with the configuration requirements of embedded equipment and achieves a good balance between precision and speed. This shows a promising performance on a lab-scale platform and may be extended to real industrial lines after further validation.

Details DOI

IROS Conference 2025 Conference Paper

JiAo: A Versatile Snake Robot with Elliptical Wheels for Multimodal Locomotion

Zizhu Zhao
Jianming Wang
Michael Albert Sumantri
Chenghui Zhang
Sihan Feng
Xuan Xiao
Shiyong Meng

This paper presents a novel snake robot, JiAo, equipped with elliptical wheels that enable both wheeled and body-based locomotion. First, the design of each module of the snake robot is described, which consists of the body link and the transmission system of the elliptical wheels. Second, distinct control systems for wheeled and body-based locomotion are proposed. Finally, the prototype has been successfully developed and various experiments have been conducted, including crossing grasslands, crossing gaps, climbing slopes, navigating pipelines and climbing cylinders. In conclusion, JiAo demonstrates its versatility by effectively performing a wide range of tasks in various challenging scenarios.

Details

IROS Conference 2025 Conference Paper

Lywal-X: A Novel Wheel-claw Quadruped Robot

Hao Shen
Yuxuan Yang
Yiliang Wang
Xintian Zuo
Hongwei Zhu
Jianming Wang
Xuan Xiao

This paper introduces a wheel-claw quadruped robot named Lywal-X, which is capable of omnidirectional movement as well as grasping actions. Firstly, the mechanical structure of Lywal-X is designed with a three-degree-of-freedom leg transformation mechanism and a two-degree-of-freedom wheel-claw structure. Then, movement strategies for different modes such as climbing and grasping are developed. Finally, the mobility performance of Lywal-X is analyzed, and physical experiments are conducted to verify the robot’s ability to pick up and transport target objects in both single-claw and double-claw modes.

Details

AAAI Conference 2024 Conference Paper

Direct May Not Be the Best: An Incremental Evolution View of Pose Generation

Yuelong Li
Tengfei Xiao
Lei Geng
Jianming Wang

Pose diversity is an inherent representative characteristic of 2D images. Due to the 3D to 2D projection mechanism, there is evident content discrepancy among distinct pose images. This is the main obstacle bothering pose transformation related researches. To deal with this challenge, we propose a fine-grained incremental evolution centered pose generation framework, rather than traditional direct one-to-one in a rush. Since proposed approach actually bypasses the theoretical difficulty of directly modeling dramatic non-linear variation, the incurred content distortion and blurring could be effectively constrained, at the same time the various individual pose details, especially clothes texture, could be precisely maintained. In order to systematically guide the evolution course, both global and incremental evolution constraints are elaborately designed and merged into the overall framework. And a novel triple-path knowledge fusion structure is worked out to take full advantage of all available valuable knowledge to conduct high-quality pose synthesis. In addition, our framework could generate a series of valuable by-products, namely the various intermediate poses. Extensive experiments have been conducted to verify the effectiveness of the proposed approach. Code is available at https://github.com/Xiaofei-CN/Incremental-Evolution-Pose-Generation.

PDF Details DOI

IROS Conference 2022 Conference Paper

Design and Experiments of Snake Robots with Docking Function

Fatao Qin
Xiaojie Duan
Shihao Ma
Jinglun Yuan
Xiangyu Wang
Jianming Wang
Xuan Xiao

This paper presents a novel snake robot with the docking function, which can help the snake robots to connect with each other to achieve a stronger one with double length and double degrees of freedom. First, the mechanical design of the snake robot with docking function is introduced, including the body link and the head-tail passive docking mechanical structure. Second, the control system is built, and the control strategies of locomotion and docking are separately proposed. Then, the visual perception function is implemented for the target recognition during the docking process. Finally, the prototype is developed. The mobility and the docking function are fully verified and analyzed through the physical experiments.

Details

ICRA Conference 2021 Conference Paper

Lywal: a Leg-Wheel Transformable Quadruped Robot with Picking up and Transport Functions

Yongjiang Xue
Xichen Yuan
Yuhai Wang
Yang Yang
Siyu Lu
Bo Zhang
Juezhu Lai
Jianming Wang

This paper introduces a leg-wheel transformable quadruped robot named Lywal which can switch to the leg-mode and the wheel-mode for locomotion, and the claw-mode for picking up and transport functions. First, the mechanical structure of Lywal is designed by using an innovative 2-DoF transformable mechanism. Second, the calculation of kinematics is analyzed in detail. Then, the switching-mode strategy and the mobile control strategies in different modes are designed. Finally, the prototype of Lywal is built. The properties of the mobile modes are analyzed, and the picking-up and transport functions of the claw-mode are verified through physical experiments.

Details

EAAI Journal 2020 Journal Article

Genetic programming based feature construction methods for foreground object segmentation

Jiayu Liang
Yu Xue
Jianming Wang

Foreground object segmentation is a crucial preprocessing step for many high-level computer vision tasks, e. g. object recognition. It is still challenging to achieve accurate segmentation, especially for complex images (e. g. with high variations). Feature construction can help to improve the segmentation performance by extracting more distinctive features for foreground/background regions from the original features. However, commonly-used feature construction methods (e. g. principle component analysis) often involve certain assumptions/constraints, and the constructed features cannot be interpreted. To address these problems, genetic programming (GP) is employed in this paper, which is a well-suited feature construction technique. The aim of this work is to design new feature construction methods using GP, and analyse/compare popular GP-based feature construction methods for foreground object segmentation, especially on complex image datasets with high variations. Specifically, one new feature construction method that incorporates the subtree technique in GP is designed, which can construct multiple features simultaneously (called SubtMFC, Subtree Multiple Feature Construction). Moreover, a parsimony pressure technique is introduced to improve SubtMFC for bloat control (a common issue for GP-based methods), which forms the method, PSubtMFC (Parsimony SubtMFC). In addition, comparison of popular GP-based feature construction methods for foreground object segmentation is conducted for the first time. Results show that SubtMFC achieves better or similar performance compared with three reference methods. In addition, compared with SubtMFC that does not control bloat, PSubtMFC can significantly reduce the solution size while maintain similar performance in the segmentation accuracy. The GP-based feature construction framework is further extended for feature representation based knowledge transfer, which can handle the problem of the scare labelled training data. Moreover, after GP is thoroughly investigated on benchmark datasets with one type of foreground objects (i. e. the Weizmann horse dataset and Pascal aeroplane dataset), it is considered whether the GP methods can perform well on datasets containing multiple types of foreground objects. Compared with three other well-performing GP-based feature construction methods, the proposed method achieves better or comparable results for the given segmentation tasks. In addition, this paper thoroughly compares/analyses popular GP-based feature construction methods for complex figure-ground segmentation for the first time. Moreover, further analyses on the input features frequently used by the GP-evolved feature construction functions reflect the effectiveness of the extracted high-level features.

Details DOI