Linear Quadratic Regulator Method in Vision-Based Laser Beam Tracking for a Mobile Target Robot

Ling, Yun; Wu, Jian; Zhou, Weijian; Wang, Yubiao; Wu, Changcheng

doi:10.1017/s0263574720000545

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…A regressor-free Fourier series-based tracking controller [1] was designed to improve the controlling accuracy when the dynamics model is uncertain. For mobile robots, many tracking methods have been developed, such as the agoraphilic navigation algorithm which was proposed to solve navigation problem in dynamic environment [2], the laser beam tracking mechanism [3], the formation tracking control method based on fast terminal sliding mode controller [4], the obstacle-avoidance method based on improved artificial potential field and PID adaptive tracking control algorithm [5]. These methods are mainly used in the field of navigation and obstacle avoidance.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A novel method of shuttlecock trajectory tracking and prediction for a badminton robot

Zhi

Luo

et al. 2022

Robotica

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This paper proposes an aerodynamic analysis of the shuttlecock and a novel method for predicting shuttlecock trajectory. First, we have established a shuttlecock track data set by an infrared-based binocular vision system. Then the unscented Kalman filter algorithm is designed to further filter the noise and visual recognition algorithm errors. Third, the radial basis function (RBF)-based track prediction model is designed. This method offers a concept to obtain the neural network model of different kinds of flying or moving objects. The experimental results show that the proposed method can predict the shuttlecock trajectory in real time at high accuracy and can be used for implementing the algorithm of return strategies in the near future.

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Section: Introduction and Related Workmentioning

confidence: 99%

A novel method of shuttlecock trajectory tracking and prediction for a badminton robot

Zhi

Luo

et al. 2022

Robotica

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A deep learning optimized LQR method for enhanced formation control with embedded systems

Wang,

Ling,

2024

Eng. Res. Express

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To achieve higher accuracy throughout the formation control processes and enhance precision in dynamic environments, particularly for the formation control of follower vehicles with embedded systems, this paper proposes a method and framework for vehicle formation control. An Ackermann-model based LQR controller is developed for lateral distance control and a PD controller for longitudinal distance control. To enhance the efficacy of the LQR controller, the Deep Deterministic Policy Gradient Derivative (DDPG) method is introduced into the control system. The DDPG networks are trained in a simulation environment and can subsequently predict LQR parameters in real-time experiments. The practical application of the methodology is showcased, and the concluding remarks emphasize the potential and superior performance of our proposed formation control approach by experimental comparison with other controllers. This methodology can be implemented in small vehicles that possess limited computational resources and is also suitable for scenarios requiring dynamic motion control with higher tracking accuracy and stability.

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A dynamic target tracking framework of UGV for UAV recovery under random disturbances

Li,

Wang,

et al. 2024

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Purpose Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control. Design/methodology/approach To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV. Findings To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances. Originality/value This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

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Linear Quadratic Regulator Method in Vision-Based Laser Beam Tracking for a Mobile Target Robot

Cited by 5 publications

References 16 publications

A novel method of shuttlecock trajectory tracking and prediction for a badminton robot

A novel method of shuttlecock trajectory tracking and prediction for a badminton robot

A deep learning optimized LQR method for enhanced formation control with embedded systems

A dynamic target tracking framework of UGV for UAV recovery under random disturbances

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