Coordination of movement of multiagent robotic systems

Kyzyrkanov, Abzal; Atanov, Sabyrzhan; Aljawarneh, Shadi

doi:10.1109/icecco53203.2021.9663796

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…By changing the parameters of cohesion and repulsion in the algorithm, we can achieve a simulation scenario with continuous obstacle avoidance and maximum coverage of space. At the same time, we introduce virtual leaders to facilitate stable coordination among intelligent agents and trigger the expansion and contraction of formations [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…It allows the algorithm to effectively explore the solution space in the early stages, while gradually transitioning towards exploiting the acquired knowledge in later stages. This approach maximizes the learning efficiency of the -Greedy algorithm, leading to an improved performance and convergence towards optimal solutions, = 1 − 0.1 num 10 (24) where • denotes the floor function. It is defined by x = max{m ∈ Z|m ≤ x}, where x is a real number and Z denotes a set of integers.…”

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confidence: 99%

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Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms

Dong,

Wu,

Chen

2023

Drones

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Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, and repulsion, to imitate natural flocking movements. However, traditional models of Boids often lack pinning and the adaptability to quickly adapt to the dynamic environment. To address this limitation, we introduce reinforcement learning into the framework of Boids to solve the problem of disorder and the lack of pinning. The aim of this approach is to enable drone swarms to quickly and effectively adapt to dynamic external environments. We propose a method based on the Q-learning network to improve the cohesion and repulsion parameters in the Boids model to achieve continuous obstacle avoidance and maximize spatial coverage in the simulation scenario. Additionally, we introduce a virtual leader to provide pinning and coordination stability, reflecting the leadership and coordination seen in drone swarms. To validate the effectiveness of this method, we demonstrate the model’s capabilities through empirical experiments with drone swarms, and show the practicality of the RL-Boids framework.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms

Dong,

Wu,

Chen

2023

Drones

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“…The coordination and control of motion of the group is a major task of group robotics. In practice, to coordinate the motion of the robots in the system, the robots form some geometric structure while maintaining a distance between them [15].…”

Section: Introductionmentioning

confidence: 99%

Method of Coordination of Motion of Swarm Robotic Systems

Kyzyrkanov,

Atanov,

Aljawarneh

et al. 2023

sjaitu

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Maintaining a specific geometric pattern is essential in various applications where groups of autonomous robots must follow a given path. Proper organization of the geometric pattern can lead to several benefits such as cost reduction, increased system reliability, and efficiency while providing a reconfigurable and flexible structure of the system. Military missions and traffic systems are examples where maintaining certain geometric patterns are widely used. However, little is known about how to develop an effective algorithm that guarantees collision avoidance and obstacle avoidance while maintaining the geometric pattern. This paper presents an algorithm for movement with a certain geometric structure of a group of autonomous mobile robots that maintains the required geometric pattern and ensures the avoidance of collisions and obstacles. The proposed algorithm is behavior-based and utilizes a set of rules that allow the robots to navigate around obstacles and avoid collisions. The algorithm's performance is demonstrated through simulations in a variety of scenarios with different numbers of robots and geometric patterns. The algorithm proposed in this paper provides an effective solution for controlling a group of autonomous mobile robots to maintain a certain geometric pattern. The proposed algorithm has the potential to be utilized in numerous applications where multiple robots must work together to achieve a common goal while maintaining a specific formation. The use of behavior-based approach and obstacle avoidance rules ensures that the robots avoid collisions and obstacles while maintaining the required pattern

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Service Quality in eLearning

Al-Jawarneh

Mohammed

2022

2022 International Conference on Engineering &Amp; MIS (ICEMIS)

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Coordination of movement of multiagent robotic systems

Cited by 5 publications

References 11 publications

Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms

Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms

Method of Coordination of Motion of Swarm Robotic Systems

Service Quality in eLearning

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