Paper Info
Title
Adaptive Potential Fields Model for Solving Distributed Area Coverage Problem in Swarm Robotics.
Abstract
Complete coverage of a given region has become a fundamental problem addressed in the field of swarm robots. Currently available approaches to the coverage problem are typically of computational complexity, and are manually specified with different map settings, which are not scalable and flexible. To address these shortcomings, this paper describes an efficient distributed approach based on potential fields method and self-adaptive control. It makes no assumptions about prior knowledge on global map, and need few manual intervention during execution. Although the motion policy of each robot is very simple, efficient coverage behavior is achieved at team level. We evaluate the approach against a traditional rule-based method and pheromone method under different target area scenarios. It shows state-of-the-art performance, both in the percentage of coverage and the degree of connectivity.
Year
DOI
Venue
2017
10.1007/978-3-319-61833-3_16
ADVANCES IN SWARM INTELLIGENCE, ICSI 2017, PT II
Keywords
Field
DocType
Swarm robotics,Distributed area coverage problem,Potential fields,Adaptive control
Global Map,Computer science,Artificial intelligence,Adaptive control,Robot,Machine learning,Swarm robotics,Scalability,Area coverage,Computational complexity theory
Conference
Volume
ISSN
Citations 
10386
0302-9743
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Xiangyu Liu15114.10
Ying Tan2128695.40