Collaborative Multi-Robot Formation Control and Global Path Optimization

Liang, Di; Liu, Zhongyi; Bhamara, Ran

doi:10.3390/app12147046

Cited by 12 publications

(6 citation statements)

References 38 publications

(39 reference statements)

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“…Thus, the velocities of the robot 𝑅 𝑖 at the control points, 𝑗 can be found by applying Equation (13):…”

Section: 𝑅′(𝑔) = 𝐴′(𝑔) − 𝑤′(𝑔) 𝑅(𝑔) 𝑤(𝑔) = 𝑤 𝑗 𝐵 𝑗𝑘 ′(𝑔) − 𝑤′(𝑔) 𝑅(𝑔...mentioning

confidence: 99%

NURBs Based Multi-robots Path Planning with Obstacle Avoidance

Belaidi,

Demim

2024

J. Comput. Theor. Appl.

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The primary problem for multi-robot displacement and motion phase solving requires that the robots prevent themselves from colliding with each other as well as stationary obstacles. In certain situations, robot conflict is unavoidable if one robot views its neighbors as immovable obstacles. Hence, this paper proposes a new NURBs (Non-Uniform Rational B-spline) based algorithm for multi-robot path planning in a crowded environment. First, the proposed technique finds each robot's free, smooth, optimal path while avoiding collision with the existing obstacles. Secondly, the prospect of possible collision between the preplanned trajectories will be computed to allow the robots to navigate in the same workspace and coordinate between them. Then, each robot's time to arrive at potential collision sites is computed based on its speed. As a result, the robots involved in the collision must choose whether to use the robot priority technique to prevent the collision. Simulation results under different scenarios and comparisons with previous works are provided to validate the work. The obtained results prove that the proposed approach is accurate (as the robot's instantaneous speed is taken into consideration), fast (as there is no need to broadcast the robots’ positions), the robots’ paths are optimal and smooth (to avoid jerk movements), and the approach ensures that the robots will not be trapped by local minima problem.

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“…Thus, the velocities of the robot 𝑅 𝑖 at the control points, 𝑗 can be found by applying Equation (13):…”

Section: 𝑅′(𝑔) = 𝐴′(𝑔) − 𝑤′(𝑔) 𝑅(𝑔) 𝑤(𝑔) = 𝑤 𝑗 𝐵 𝑗𝑘 ′(𝑔) − 𝑤′(𝑔) 𝑅(𝑔...mentioning

confidence: 99%

NURBs Based Multi-robots Path Planning with Obstacle Avoidance

Belaidi,

Demim

2024

J. Comput. Theor. Appl.

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“…The AUV formation is controlled by the formation clustering algorithm to complete the overall movement of the formation before the threat is perceived [30]. When a threatening obstacle is encountered, the above-mentioned obstacle avoidance algorithm is used to successfully avoid the high-threat obstacle by multi-beam sonar sensing, and the improved artificial potential field algorithm is used to return to the cluster formation control after removing the obstacle.…”

Section: Algorithm Flow Of Cooperative Obstacle Avoidancementioning

confidence: 99%

Coordinated Obstacle Avoidance of Multi-AUV Based on Improved Artificial Potential Field Method and Consistency Protocol

Ning

2023

JMSE

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Formation avoidance is one of the critical technologies for autonomous underwater vehicle (AUV) formations. To this end, a cooperative obstacle avoidance algorithm based on an improved artificial potential field method and a consistency protocol is proposed in this paper for the local obstacle avoidance problem of AUV formation. Firstly, for the disadvantage that the traditional artificial potential field method can easily fall into local minima, an auxiliary potential field perpendicular to the AUV moving direction is designed to solve the problem that AUVs can easily have zero combined force in the potential field and local minima. Secondly, the control law of AUV formation that keeps the speed and position consistent is designed for the problem that the formation will change during the local obstacle avoidance of the formation system. The control conflict problem of the combined algorithm of the artificial potential field law and the consistency protocol is solved by adjusting the desired formation of the consistency protocol through the potential field force. Finally, the bounded energy function demonstrates system convergence stability. The simulation verification confirmed that the AUV formation could achieve the convergence of the formation state under local obstacle avoidance.

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“…These algorithms were mainly used in the path planning of single robots in the early days. Their core advantage lies in their ability to efficiently avoid collisions and find optimal paths (Liang et al, 2022 ). Dijkstra's algorithm is known for its wide applicability and reliability and is widely used in fields such as map navigation and network routing.…”

Section: Introductionmentioning

confidence: 99%

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Wang,

Liu

2024

Front. Neurorobot.

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IntroductionIn the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues. Although deep learning and reinforcement learning methods have made some progress in solving these problems, however, current research still has shortcomings. In particular, research on adaptive sensing and real-time decision-making of multi-robot swarms has not yet received sufficient attention.MethodsTo fill this research gap, we propose a YOLOv5-PPO model based on A3C optimization. This model cleverly combines the target detection capabilities of YOLOv5 and the PPO reinforcement learning algorithm, aiming to improve the efficiency and accuracy of collaborative operations among logistics and warehousing robot groups.ResultsThrough extensive experimental evaluation on multiple datasets and tasks, the results show that in different scenarios, our model can successfully achieve multi-robot collaborative operation, significantly improve task completion efficiency, and maintain target detection and environment High accuracy of understanding.DiscussionIn addition, our model shows excellent robustness and adaptability and can adapt to dynamic changes in the environment and fluctuations in demand, providing an effective method to solve the collaborative operation problem of logistics warehousing robots.

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Collaborative Multi-Robot Formation Control and Global Path Optimization

Cited by 12 publications

References 38 publications

NURBs Based Multi-robots Path Planning with Obstacle Avoidance

NURBs Based Multi-robots Path Planning with Obstacle Avoidance

Coordinated Obstacle Avoidance of Multi-AUV Based on Improved Artificial Potential Field Method and Consistency Protocol

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

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