Paper Info
Title
Task partitioning via ant colony optimization for distributed assembly
Abstract
We address the distributed assembly of a structure by a team of homogeneous robots. We present an ant-colony-optimization (ACO) based algorithm to partition general 2- and 3-D assembly tasks into N separate subtasks. The objective is to determine an allocation or partitioning strategy that minimizes the workload imbalance between the robots that allow for maximum assembly parallelization. This objective is achieved by extending ACO to apply to a team of ants dividing a set of tasks, with pheromone marking connections between tasks guiding decisions on task allocation. We present simulation results for various 2-D and 3-D structures and discuss the advantages of the ACO formulation in the context of other existing approaches.
Year
DOI
Venue
2012
10.1007/978-3-642-32650-9_13
ANTS
Keywords
Field
DocType
ant colony optimization,partitioning strategy,n separate subtasks,task allocation,aco formulation,existing approach,3-d assembly task,simulation result,3-d structure,maximum assembly parallelization,homogeneous robot
Ant colony optimization algorithms,Mathematical optimization,Workload,Computer science,Homogeneous,Deterministic algorithm,Robot,Distributed computing
Conference
Volume
ISSN
Citations 
7461
0302-9743
0
PageRank 
References 
Authors
0.34
14
2
Name
Order
Citations
PageRank
James Worcester191.87
M. Ani Hsieh238234.69