Paper Info
Title
Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment.
Abstract
Navigation or path planning is the basic need for movement of robots. Navigation consists of two foremost concerns, target tracking and hindrance avoidance. Hindrance avoidance is the way to accomplish the task without clashing with intermediate hindrances. In this paper, an evolutionary scheme to solve the multi-agent, multi-target navigation problem in an unknown dynamic environment is proposed. The strategy is a combination of modified artificial bee colony for neighborhood search planner and evolutionary programming to smoothen the resulting intermediate feasible path. The proposed strategy has been tested against navigation performances on a collection of benchmark maps for A* algorithm, particle swarm optimization with clustering-based distribution factor, genetic algorithm and rapidly-exploring random trees for path planning. Navigation effectiveness has been measured by smoothness of feasible paths, path length, number of nodes traversed and algorithm execution time. Results show that the proposed method gives good results in comparison to others.
Year
DOI
Venue
2018
10.1007/s11370-017-0244-7
Intelligent Service Robotics
Keywords
Field
DocType
Artificial bee colony (ABC), Evolutionary programming (EP), Multi-robot path planner system, Dynamic obstacle collision avoidance, Centralized coordination, Dynamic unknown environment
Particle swarm optimization,Motion planning,Computer vision,Mathematical optimization,Path length,Computer science,Artificial intelligence,Smoothness,Evolutionary programming,Robot,Cluster analysis,Genetic algorithm
Journal
Volume
Issue
ISSN
11
2
1861-2776
Citations 
PageRank 
References 
0
0.34
15
Authors
5
Name
Order
Citations
PageRank
Abdul Qadir Faridi100.34
Sanjeev Sharma223630.20
Anupam Shukla3295.42
Ritu Tiwari422229.33
Joydip Dhar53712.11