Convolutional neural network based tracking for human following mobile robot with LQG based control system

Gupta, Sudip Chandra; Majumdar, Jharna

doi:10.1145/3339311.3339325

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…In contrast, deep learning methods offer innovative approaches in the realm of people detection and following. Gupta et al proposed the use of the mask region-based convolutional neural network (mask RCNN) and YOLO v2-based CNN architectures for personnel localization, along with speed-controlled tracking algorithms [26]. Boudjit et al introduced a target-detecting unmanned aerial vehicle (UAV) following a method based on the YOLO-v2 architecture and achieved UAV target tracking by combining detection algorithms with proportional-integral-derivative control [27].…”

Section: Related Workmentioning

confidence: 99%

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Huang,

Ou,

Guo

et al. 2024

Horticulturae

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Autonomous mobile robots play a vital role in the mechanized production of orchards, where human-following is a crucial collaborative function. In unstructured orchard environments, obstacles often obscure the path, and personnel may overlap, leading to significant disruptions to human-following. This paper introduces the KCF-YOLO fusion visual tracking method to ensure stable tracking in interference environments. The YOLO algorithm provides the main framework, and the KCF algorithm intervenes in assistant tracking. A three-dimensional binocular-vision reconstruction method was used to acquire personnel positions, achieving stabilized visual tracking in disturbed environments. The robot was guided by fitting the personnel’s trajectory using an unscented Kalman filter algorithm. The experimental results show that, with 30 trials in multi-person scenarios, the average tracking success rate is 96.66%, with an average frame rate of 8 FPS. Additionally, the mobile robot is capable of maintaining a stable following speed with the target individuals. Across three human-following experiments, the horizontal offset Error Y does not exceed 1.03 m. The proposed KCF-YOLO tracking method significantly bolsters the stability and robustness of the mobile robot for human-following in intricate orchard scenarios, offering an effective solution for tracking tasks.

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Section: Related Workmentioning

confidence: 99%

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Huang,

Ou,

Guo

et al. 2024

Horticulturae

View full text Add to dashboard Cite

show abstract

“…Arias et al (2019) presented a facial gesture recognition system by a convolutional neural network algorithm for gesticulation of an interactive social robot with humanoid appearance. Gupta and Majumdar (2019) investigated the tracking control a human following robot, where the convolutional neural network is used to identify the followed human and the linear quadratic Gaussian controller is used for velocity control. Gu et al (2020) applied an improved convolutional neural network model to path planning for a robot moving in corn fields.…”

Section: Introductionmentioning

confidence: 99%

Deep regression of convolutional neural network applied to resolved acceleration control for a robot manipulator

Kuo

Tang

2021

Transactions of the Institute of Measurement and Control

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This paper presents a modified resolved acceleration control scheme based on deep regression of the convolutional neural network. The resolved acceleration control scheme can achieve precise motion control of robot manipulators by regulating the accelerations of the end-effector, and the conventional scheme needs the position and orientation of the end-effector, which are obtained through the direct kinematics of the robot manipulator. This scheme increases the computational loads and might obtain inaccurate position and orientation due to mechanical errors. To overcome the drawbacks, a camera is used to capture the images of the robot manipulator, and then a deep regression of convolutional neural network is imposed into the resolved acceleration control to obtain the position and orientation of the end-effector. The proposed approach aims to enhance the positioning accuracy, to reduce the computational loads, and to facilitate the deep regression in real-time control. In this study, the proposed approach is applied to a 3-DOF planar parallel robot manipulator, and the results are compared with those by the conventional resolved acceleration control and a visual servo-based control. The results show that those objectives are achieved. Furthermore, the robustness of the proposed approach is tested through only the partial image of the end-effector available, and the proposed approach still works functionally and effectively.

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Convolutional neural network based tracking for human following mobile robot with LQG based control system

Cited by 2 publications

References 7 publications

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Human-Following Strategy for Orchard Mobile Robot Based on the KCF-YOLO Algorithm

Deep regression of convolutional neural network applied to resolved acceleration control for a robot manipulator

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