Multi-Agent Area Coverage Using a Single Query Roadmap: A Swarm Intelligence Approach

2010 ◽  
pp. 95-112 ◽  
Author(s):  
Ali Nasri Nazif ◽  
Alireza Davoodi ◽  
Philippe Pasquier
Keyword(s):  
Swarm Intelligence ◽  
Area Coverage ◽  
Multi Agent ◽  
Intelligence Approach

scholarly journals Development of Self-Synchronized Drones’ Network Using Cluster-Based Swarm Intelligence Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 48010-48022
Author(s):  
Fawaz Alsolami ◽  
Fahad A. Alqurashi ◽  
Mohammad Kamrul Hasan ◽  
Rashid A. Saeed ◽  
S. Abdel-Khalek ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Intelligence Approach

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.

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

2021 ◽  
Author(s):  
Arvind R. Singh ◽  
Lei Ding ◽  
Dhenuvakonda Koteswara Raju ◽  
Lolla Phani Raghav ◽  
Rangu Seshu Kumar
Keyword(s):  
Swarm Intelligence ◽  
Energy Management ◽  
Demand Response ◽  
Load Demand ◽  
Flexible Load ◽  
Intelligence Approach
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