Distributed Static and Dynamic Circumnavigation Control with Arbitrary Spacings for a Heterogeneous Multi-robot System

Yao, Weijia; Lu, Huimin; Zeng, Zhiwen; Xiao, Jing; Zhang, Zhiqiang

doi:10.1007/s10846-018-0906-5

Cited by 29 publications

(13 citation statements)

References 20 publications

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“…The CFP could be static or dynamic. In the static case, the radius of the circular trajectory, angular speed, and spacing between the agents are assumed static [77]. If at least one of the quantities varies (speed, position, spacing, etc.…”

Section: Classification Of the Cfpmentioning

confidence: 99%

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A Survey on Techniques in the Circular Formation of Multi-Agent Systems

2021

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Distributed control solutions to the multi-agent systems in terms of coordination, formation, and consensus problems are extensively studied in the literature. The circular formation control of multi-agent systems becomes one of the most challenging and active research topics in distributed cooperative control. The research potential of circular formation control has grown especially in satellite formation and control. The circular formation control aims to drive a group of agents to move on a circular trajectory around a center with spacing adjustment to avoid the collision. This paper covers the fundamental research in consensus and formation control and the latest developments in the applications of formation control especially in circular formation control and stability analysis. Literature related to various control streams to achieve circular formation is discussed. Algebraic graph theory is used as a building block in problem definition, problem formulation, and solutions. A review of bearing, range, and received signal strength measurements with respect to circular formation control is presented. Leader following, behavior-based, artificial potential function, virtual structure, and cyclic pursuit are the methods discussed most in this context. The stabilization of the collective circular motion with the unicycles approach is also discussed. Further results on circular formation, several applications, and future research directions are presented.

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Section: Classification Of the Cfpmentioning

confidence: 99%

“…If at least one of the quantities varies (speed, position, spacing, etc. ), the problem will be considered as a dynamic CFP [77][78][79]. The characteristics of CFP with reference to control and stability are further classified with respect to the center as follows [80]:…”

Section: Classification Of the Cfpmentioning

confidence: 99%

A Survey on Techniques in the Circular Formation of Multi-Agent Systems

2021

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“…Recently, computer games have been used for AI research, which helps the agent to grow since its birth, including the Atari video games [4], the imperfect information game and so on [5]. The Multi-Robot Confrontation [6], as a platform to imitate real battlefields, provides convenience for military command, situation assessment, and intelligent decision-making, and is also an effective platform to develop AI applications.…”

Section: The Robot Confrontation Systemmentioning

confidence: 99%

Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Meng

2019

Information

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Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.

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“…H ETEROGENEOUS multi-robot System is a new research hot spot in the robotic field [1]- [4]. Heterogeneous multi-robot system means that the types or the capabilities of the robots in a multi-robot system are different.…”

Section: Introductionmentioning

confidence: 99%

An Improved Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System

Wang

Tang

et al. 2020

IEEE Access

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Heterogeneous multi-robot system is one of the most important research directions in the robotic field. Real-time path planning for heterogeneous multi-robot system under unknown 3D environment is a new challenging research and a hot spot in this field. In this paper, an improved real-time path planning method is proposed for a heterogeneous multi-robot system, which is composed of many unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In the proposed method, the 3D environment is modelled as a neuron topology map, based on the grid method combined with the bio-inspired neural network. Then a new 3D dynamic movement model for multi-robots is established based on an improved Dragonfly Algorithm (DA). Thus, the movements of the robots are optimized according to the activities of the neurons in the bio-inspired neural network to realize the real-time path planning. Furthermore, some simulations have been carried out. The results show that the proposed method can effectively guide the heterogeneous UAV/UGV system to the target, and has better performance than traditional methods in the real-time path planning tasks.

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Distributed Static and Dynamic Circumnavigation Control with Arbitrary Spacings for a Heterogeneous Multi-robot System

Cited by 29 publications

References 20 publications

A Survey on Techniques in the Circular Formation of Multi-Agent Systems

A Survey on Techniques in the Circular Formation of Multi-Agent Systems

Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

An Improved Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System

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