Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Multi-robot task allocation based on the modified particle swarm optimization algorithm

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jianping Chen ; Sch. of Autom., Guangdong Univ. of Technol., Guangzhou, China ; Yimin Yang ; Yunbiao Wu

Task allocation is one of the research focuses of multi-robot system. On the base of presenting the utility values matrix of robots relative to tasks and analyzing the characteristics of multi-robot task allocation, we build the multi-robot task allocation model based on robotic utility value. In order to prevent the basic particle swarm optimization (PSO) algorithm from converging on local optimum, this paper proposes a modified particle swarm optimization (MPSO) algorithm by introducing the linear decrease mechanism of inertia weight and the concept of adjustment operator and adjustment sequence. With the evolution of velocity in the MPSO algorithm, particle not only studies from the historical optimum individual of itself and population, but also studies from the other stochastic individuals with some probability. Finally, the MPSO algorithm is used to solve the task allocation problem of RoboCup 2D soccer robot system, the efficiency of this modified algorithm is proved through simulation results.

Published in:

Natural Computation (ICNC), 2011 Seventh International Conference on  (Volume:3 )

Date of Conference:

26-28 July 2011