A non-potential orthogonal vector field method for more efficient robot navigation and control

Gao, Yan; Bai, Chenggang; Fu, Rao; Quan, Quan

doi:10.1016/j.robot.2022.104291

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…With the separation principle, the safety radius in the High traffic efficiency is usually a control objective inside the virtual tube. In [131], the authors propose a non-potential orthogonal vector field method, which is the modification of the classic attractive/repulsive potential fields approach so as to improve its efficiency while retaining the Lyapunov stability analysis from traditional potential fields. The improvement strategy aims at making the overall repulsive vector field orthogonal to the attractive vector field in some conditions.…”

Section: Other Work and Applications Related To Virtual Tubementioning

confidence: 99%

Distributed Control for a Multiagent System to Pass Through a Connected Quadrangle Virtual Tube

Gao

Bai

Quan

2023

IEEE Trans. Control Netw. Syst.

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This survey presents a comprehensive review of various methods and algorithms related to passing-through control of multi-robot systems in cluttered environments. Numerous studies have investigated this area, and we identify several avenues for enhancing existing methods. This survey describes some models of robots and commonly considered control objectives, followed by an in-depth analysis of four types of algorithms that can be employed for passing-through control: leader-follower formation control, multi-robot trajectory planning, control-based methods, and virtual tube planning and control. Furthermore, we conduct a comparative analysis of these techniques and provide some subjective and general evaluations.

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Section: Other Work and Applications Related To Virtual Tubementioning

confidence: 99%

Distributed Control for a Multiagent System to Pass Through a Connected Quadrangle Virtual Tube

Gao

Bai

Quan

2023

IEEE Trans. Control Netw. Syst.

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“…In recent years, due to their agility, maneuverability, and ability to be deployed in many complex missions, mobile robot has attracted many researchers, particularly in respect of autonomous navigation in the warehouse or restricted area [1][2][3][4][5][6][7][8][9][10]. However, methods using fixed line have many drawbacks:…”

Section: Introductionmentioning

confidence: 99%

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Hanh

Cong

2023

Journal of Robotics and Control

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Obstacle avoidance for mobile robot to reach the desired target from a start location is one of the most interesting research topics. However, until now, few works discuss about working of mobile robot in the dynamic and continuously changing environment. So, this issue is still the research challenge for mobile robots. Traditional algorithm for obstacle avoidance in the dynamic, complex environment had many drawbacks. As known that Q-learning, the type of reinforcement learning, has been successfully applied in computer games. However, it is still rarely used in real world applications. This research presents an effectively method for real time dynamic obstacle avoidance based on Q-learning in the real world by using three-wheeled mobile robot. The position of obstacles including many static and dynamic obstacles and the mobile robot are recognized by fixed camera installed above the working space. The input for the robot is the 2D data from the camera. The output is an action for the robot (velocities, linear and angular parameters). Firstly, the simulation is performed for Q-learning algorithm then based on trained data, The Q-table value is implemented to the real mobile robot to perform the task in the real scene. The results are compared with intelligent control method for both static and dynamic obstacles cases. Through implement experiments, the results show that, after training in dynamic environments and testing in a new environment, the mobile robot is able to reach the target position successfully and have better performance comparing with fuzzy controller.

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“…With the development of robot technology, HRC-based robots are widely used in autonomous navigation, inspection and exploration, agricultural machinery services, logistics and warehousing, and other fields [1][2][3][4]. Map creation and localization, path planning, and controlled movement are the three main components of autonomous navigation for mobile robots [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm

Sun,

Wang,

et al. 2023

Sensors

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Due to the increased employment of robots in modern society, path planning methods based on human–robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function’s sub-functions and enhance the algorithm’s performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique’s ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.

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A non-potential orthogonal vector field method for more efficient robot navigation and control

Cited by 14 publications

References 24 publications

Distributed Control for a Multiagent System to Pass Through a Connected Quadrangle Virtual Tube

Distributed Control for a Multiagent System to Pass Through a Connected Quadrangle Virtual Tube

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm

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