Qi Mao, Jun Chen#, Fei Xie, Liqian Dou, Bailing Tian, Qun Zong (# corresponding author)
Systems & Control Letters 2025
In this paper, we investigate how a regulator can be implemented for gaining the optimal robustness margin of second-order plants in the presence of uncertain perturbations
Qi Mao, Jun Chen#, Fei Xie, Liqian Dou, Bailing Tian, Qun Zong (# corresponding author)
Systems & Control Letters 2025
In this paper, we investigate how a regulator can be implemented for gaining the optimal robustness margin of second-order plants in the presence of uncertain perturbations
Wenbin Ding, Jun Chen#, Mingjia Chen, Fei Xie, Qi Mao, Philip Dames (# corresponding author)
arXiv preprint arXiv:2510.24109 2025
This paper explores the implementation of intelligent robotic manipulating agents based on Vision-Language Models (VLMs) in the physical world
Wenbin Ding, Jun Chen#, Mingjia Chen, Fei Xie, Qi Mao, Philip Dames (# corresponding author)
arXiv preprint arXiv:2510.24109 2025
This paper explores the implementation of intelligent robotic manipulating agents based on Vision-Language Models (VLMs) in the physical world
Jun Chen#, Mingjia Chen, Qi Mao, Fei Xie, Philip M Dames (# corresponding author)
Frontiers in Plant Science 2025
In this paper, we reformulate the information gathering problem as a multi-armed bandit (MAB) problem and propose a novel distributed Bernoulli Thompson Sampling algorithm
Jun Chen#, Mingjia Chen, Qi Mao, Fei Xie, Philip M Dames (# corresponding author)
Frontiers in Plant Science 2025
In this paper, we reformulate the information gathering problem as a multi-armed bandit (MAB) problem and propose a novel distributed Bernoulli Thompson Sampling algorithm
Jun Chen#, Mingjia Chen, Shinkyu Park (# corresponding author)
arXiv preprint arXiv:2509.23705 2025
We introduce a novel Multi-robot Dynamic Coverage Path Planning (MDCPP) algorithm for complete coverage in two-dimensional environments
Jun Chen#, Mingjia Chen, Shinkyu Park (# corresponding author)
arXiv preprint arXiv:2509.23705 2025
We introduce a novel Multi-robot Dynamic Coverage Path Planning (MDCPP) algorithm for complete coverage in two-dimensional environments
Jun Chen#, Jiaqing Ma, Philip Dames (# corresponding author)
arXiv preprint arXiv:2509.23308 2025
In this paper, we propose a novel motion planning algorithm framework that integrates three control strategies
Jun Chen#, Jiaqing Ma, Philip Dames (# corresponding author)
arXiv preprint arXiv:2509.23308 2025
In this paper, we propose a novel motion planning algorithm framework that integrates three control strategies
Zeyan Wang, Fei Xie#, Zhiyuan Wang#, Yijian Liu, Qi Mao, Jun Chen (# corresponding author)
Applied Sciences 2025
This study develops a digital twin-driven intelligent sorting system for 3D-printed agricultural tools
Zeyan Wang, Fei Xie#, Zhiyuan Wang#, Yijian Liu, Qi Mao, Jun Chen (# corresponding author)
Applied Sciences 2025
This study develops a digital twin-driven intelligent sorting system for 3D-printed agricultural tools
Jun Chen, Philip Dames#, Shinkyu Park (# corresponding author)
Autonomous Robots 2025
This paper develops a novel distributed multi-robot multi-target tracking algorithm to effectively search for and track clustered targets
Jun Chen, Philip Dames#, Shinkyu Park (# corresponding author)
Autonomous Robots 2025
This paper develops a novel distributed multi-robot multi-target tracking algorithm to effectively search for and track clustered targets
Wei Zhang, Yong Chen, Qianqian Wang, Jun Chen# (# corresponding author)
Journal of the Science of Food and Agriculture 2025
In this study, a tender leaf identification algorithm and a mechanically perceptible plucking finger have been proposed
Wei Zhang, Yong Chen, Qianqian Wang, Jun Chen# (# corresponding author)
Journal of the Science of Food and Agriculture 2025
In this study, a tender leaf identification algorithm and a mechanically perceptible plucking finger have been proposed
Jun Chen#, Mohammed Abugurain, Philip Dames, Shinkyu Park (# corresponding author)
IEEE Transactions on Robotics 2025
This paper presents a cooperative multi-robot multi-target search and tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network and, consequently, improving overall target tracking accuracy
Jun Chen#, Mohammed Abugurain, Philip Dames, Shinkyu Park (# corresponding author)
IEEE Transactions on Robotics 2025
This paper presents a cooperative multi-robot multi-target search and tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network and, consequently, improving overall target tracking accuracy
Wei Zhang, Yong Chen, Qianqian Wang, Jun Chen# (# corresponding author)
Journal of the Science of Food and Agriculture 2024
In this study, a tender leaf identification algorithm and a mechanically perceptible plucking finger have been proposed, which are effective for identification of tender leaves and plucking.
Wei Zhang, Yong Chen, Qianqian Wang, Jun Chen# (# corresponding author)
Journal of the Science of Food and Agriculture 2024
In this study, a tender leaf identification algorithm and a mechanically perceptible plucking finger have been proposed, which are effective for identification of tender leaves and plucking.
Yihan Huang, Fei Xie#, Jing Zhao, Zhilin Gao, Jun Chen, Fei Zhao, Xixiang Liu (# corresponding author)
Remote Sensing 2024
This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM.
Yihan Huang, Fei Xie#, Jing Zhao, Zhilin Gao, Jun Chen, Fei Zhao, Xixiang Liu (# corresponding author)
Remote Sensing 2024
This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM.
Jun Chen, Philip Dames# (# corresponding author)
Journal of Intelligent & Robotic Systems 2023
In this paper, we introduce the convex uncertain Voronoi (CUV) diagram, a generalization of the standard Voronoi diagram that accounts for the uncertain pose of each individual robot. We then use the CUV diagram to develop distributed multi-target tracking and coverage control algorithms that enable teams of mobile robots to account for bounded uncertainty in the location of each robot.
Jun Chen, Philip Dames# (# corresponding author)
Journal of Intelligent & Robotic Systems 2023
In this paper, we introduce the convex uncertain Voronoi (CUV) diagram, a generalization of the standard Voronoi diagram that accounts for the uncertain pose of each individual robot. We then use the CUV diagram to develop distributed multi-target tracking and coverage control algorithms that enable teams of mobile robots to account for bounded uncertainty in the location of each robot.
Jun Chen#, Philip Dames, Shinkyu Park (# corresponding author)
International Symposium on Distributed Autonomous Robotic Systems 2024
In this paper, we develop a novel distributed multi-robot multi-target tracking algorithm for effectively tracking clustered targets from noisy measurements.
Jun Chen#, Philip Dames, Shinkyu Park (# corresponding author)
International Symposium on Distributed Autonomous Robotic Systems 2024
In this paper, we develop a novel distributed multi-robot multi-target tracking algorithm for effectively tracking clustered targets from noisy measurements.
Jun Chen, Zhanteng Xie, Philip Dames# (# corresponding author)
Robotics and Autonomous Systems 2022
In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty.
Jun Chen, Zhanteng Xie, Philip Dames# (# corresponding author)
Robotics and Autonomous Systems 2022
In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty.
Jun Chen, Philip Dames# (# corresponding author)
2021 IEEE International Conference on Robotics and Automation (ICRA) 2021
This paper introduces the normalized unused sensing capacity to measure the amount of information that a sensor is currently gathering relative to its theoretical maximum. This is then used to develop a distributed coverage control strategy for a team of heterogeneous sensors that automatically balances the load based on the current unused capacity of each team member.
Jun Chen, Philip Dames# (# corresponding author)
2021 IEEE International Conference on Robotics and Automation (ICRA) 2021
This paper introduces the normalized unused sensing capacity to measure the amount of information that a sensor is currently gathering relative to its theoretical maximum. This is then used to develop a distributed coverage control strategy for a team of heterogeneous sensors that automatically balances the load based on the current unused capacity of each team member.
Jun Chen, Philip Dames# (# corresponding author)
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
In this paper we address this problem by introducing four new distributed algorithms that allow large teams of robots to: i) run the prediction and ii) update steps of a distributed recursive Bayesian multi- target tracker, iii) determine the set of local neighbors that must exchange data, and iv) exchange data in a consistent manner.
Jun Chen, Philip Dames# (# corresponding author)
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
In this paper we address this problem by introducing four new distributed algorithms that allow large teams of robots to: i) run the prediction and ii) update steps of a distributed recursive Bayesian multi- target tracker, iii) determine the set of local neighbors that must exchange data, and iv) exchange data in a consistent manner.
Jun Chen, Philip Dames# (# corresponding author)
2020 American Control Conference (ACC) 2020
In this paper, we propose a distributed coverage control algorithm for mobile sensing networks that can account for bounded uncertainty in the location of each sensor.
Jun Chen, Philip Dames# (# corresponding author)
2020 American Control Conference (ACC) 2020
In this paper, we propose a distributed coverage control algorithm for mobile sensing networks that can account for bounded uncertainty in the location of each sensor.
Jun Chen, Philip Dames# (# corresponding author)
Robotics science and systems Workshop on Heterogeneous Multi-Robot Task Allocation and Coordination 2020
In this paper we allow a heterogeneous team of groundand aerial robots to perform the search and tracking tasks in a coordinated manner.
Jun Chen, Philip Dames# (# corresponding author)
Robotics science and systems Workshop on Heterogeneous Multi-Robot Task Allocation and Coordination 2020
In this paper we allow a heterogeneous team of groundand aerial robots to perform the search and tracking tasks in a coordinated manner.
Jun Chen, Philip Dames# (# corresponding author)
The International Symposium of Robotics Research 2022
In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty.
Jun Chen, Philip Dames# (# corresponding author)
The International Symposium of Robotics Research 2022
In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty.