Coverage control of unicycle multi‐agent network in dynamic environment

2021 ◽  
Author(s):  
Qihai Sun ◽  
Zhi‐Wei Liu ◽  
Ming Chi ◽  
Yangming Dou ◽  
Dingxin He ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Coverage Control ◽  
Multi Agent

Coverage Control for Mobile Sensor Networks on a Circle

2014 ◽  
Vol 02 (03) ◽  
pp. 243-248 ◽  
Author(s):  
Cheng Song ◽  
Gang Feng
Keyword(s):  
Sensor Networks ◽  
Mobile Agents ◽  
Mobile Sensor Networks ◽  
Mobile Sensors ◽  
Mobile Sensor ◽  
Coverage Problem ◽  
Control Laws ◽  
Coverage Control ◽  
Order Preservation ◽  
Multi Agent

This paper investigates the coverage problem for mobile sensor networks on a circle. The goal is to minimize the largest distance from any point on the circle to its nearest sensor while preserving the mobile sensors' order. The coverage problem is translated into a multi-agent consensus problem by showing that the largest distance from any point to its nearest sensor is minimized if the counterclockwise distance between each sensor and its right neighbor reaches a consensus. Distributed control laws are also developed to drive the mobile agents to the optimal configuration with order preservation. Simulation results illustrate the effectiveness of the proposed control laws.

AGENT-BASED SYSTEM ARCHITECTURE FOR DYNAMIC AND OPEN ENVIRONMENTS

2003 ◽  
Vol 02 (01) ◽  
pp. 105-133 ◽  
Author(s):  
ELHADI SHAKSHUKI ◽  
HAMADA GHENNIWA ◽  
MAHAMED KAMEL
Keyword(s):  
System Architecture ◽  
Rapid Growth ◽  
Dynamic Environment ◽  
Information Gathering ◽  
Information Resources ◽  
Architecture Design ◽  
Agent Based ◽  
Interface Agents ◽  
Multi Agent ◽  
Computing Environments

The rapid growth of the network-centered (Internet and Intranet) computing environments requires new architectures for information gathering systems. Typically, in these environments, the information resources are dynamic, heterogeneous and distributed. In addition, these computing environments are open, where information resources may be connected or disconnected at any time. This paper presents an architecture for a multi-agent information gathering system. The architecture includes three types of agents: interface, broker and resource agents. The interface agents interact with the users to fulfill their interests and preferences. The resource agents access and capture the content of the information resources. The broker agents facilitate cooperation among the information and the resource agents to achieve their desired goals. This paper provides the agents' architecture, design and implementations that enable them to cooperate, coordinate and communicate with each other to gather information in an open and dynamic environment.

scholarly journals Scalarized Q Multi-Objective Reinforcement Learning for Area Coverage Control and Light Control Implementation

2018 ◽  
Vol 16 (2) ◽  
pp. 72-82
Author(s):  
Akkhachai Phuphanin ◽  
Wipawee Usaha
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Area Coverage ◽  
Coverage Area ◽  
Lighting Control ◽  
Multi Objective ◽  
Coverage Control ◽  
Control Schemes ◽  
Multi Agent ◽  
Testbed Experiments

Coverage control is crucial for the deployment of wireless sensor networks (WSNs). However, most coverage control schemes are based on single objective optimization such as coverage area only, which do not consider other contradicting objectives such as energy consumption, the number of working nodes, wasteful overlapping areas. This paper proposes on a Multi-Objective Optimization (MOO) coverage control called Scalarized Q Multi-Objective Reinforcement Learning (SQMORL). The two objectives are to achieve the maximize area coverage and to minimize the overlapping area to reduce energy consumption. Performance evaluation is conducted for both simulation and multi-agent lighting control testbed experiments. Simulation results show that SQMORL can obtain more efficient area coverage with fewer working nodes than other existing schemes.  The hardware testbed results show that SQMORL algorithm can find the optimal policy with good accuracy from the repeated runs.

Introduction to Multi-agent Cooperative Coverage Control

2021 ◽  
pp. 1-11
Author(s):  
Chao Zhai ◽  
Hai-Tao Zhang ◽  
Gaoxi Xiao
Keyword(s):  
Coverage Control ◽  
Multi Agent

A Multi-Agent System for Optimal Supply Chain Management

2011 ◽  
pp. 281-304
Author(s):  
Hyung Rim Choi ◽  
Hyun Soo Kim
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Environmental Changes ◽  
Scheduling Algorithm ◽  
Dynamic Environment ◽  
Multi Agent System ◽  
Production Environment ◽  
Agent System ◽  
Chain Management ◽  
Multi Agent

Supply chain management recently has been developing into a dynamic environment that has to accept the changes in the formation of the supply chain. In other words, the supply chain is not static but varies dynamically according to the environmental changes. Therefore, under this dynamic supply chain environment, the priority is given not to the management of the existing supply chain but to the selection of new suppliers and outsourcing companies in order to organize an optimal supply chain. The objective of this research is to develop a multi-agent system that enables the effective formation and management of an optimal supply chain. The multi agent system for optimal supply chain management developed in this research is a multi agent system based on the scheduling algorithm, a cooperative scheduling methodology, which enables the formation of an optimal supply chain and its management. By means of active communications among internal agents, a multi-agent system for optimal supply chain management makes it possible to quickly respond to the production environment changes such as the machine failure or outage of outsourcing companies and the delivery delay of suppliers. This research has tried to suggest a new direction and new approach to the optimal supply chain management by means of a multi-agent system in dynamic supply chain environment

Distributed persistent coverage control and performance evaluation of multi-agent system

2019 ◽  
Vol 123 (1268) ◽  
pp. 1701-1723 ◽  
Author(s):  
S. Lee ◽  
Y. Kim
Keyword(s):  
Evaluation Model ◽  
Probabilistic Method ◽  
Voronoi Tessellation ◽  
Coverage Level ◽  
Detection Model ◽  
Coverage Control ◽  
Space And Time ◽  
Model And Simulation ◽  
Multi Agent ◽  
And Performance

ABSTRACTThe persistent coverage control problem is formulated based on cell discretisation of two-dimensional mission space and time-increasing cell ages. A new performance function is defined to represent the coverage level of the mission space, and time behaviour is evaluated by the probabilistic method based on the detection model of agents. For comparison, persistent coverage controllers are designed by a target-based approach and a reactive approach. Both controllers are designed in a distributed manner using Voronoi tessellation and Delaunay graph-based local information sharing. Numerical simulation is performed to analyse the evaluated mean age of cells and evaluated coverage level over time for the designed persistent coverage controllers. The differences between the evaluation model and simulation situation are discussed.

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