Jun Chen#, Mohammed Abugurain, Philip Dames, Shinkyu Park (# corresponding author)
IEEE Transactions on Robotics (Early Access) 2025
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#, Mohammed Abugurain, Philip Dames, Shinkyu Park (# corresponding author)
IEEE Transactions on Robotics (Early Access) 2025
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.
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.