|
" A Dynamic Territorializing Approach for Workload Balancing and Resource Management in a Distributed Multiagent System "
Islam, Mohammad Mahmudul
Zargarzadeh, Hassan
| Document Type
|
:
|
Latin Dissertation
|
| Language of Document
|
:
|
English
|
| Record Number
|
:
|
1108636
|
| Doc. No
|
:
|
TLpq2467587774
|
| Main Entry
|
:
|
Islam, Mohammad Mahmudul
|
|
|
:
|
Zargarzadeh, Hassan
|
| Title & Author
|
:
|
A Dynamic Territorializing Approach for Workload Balancing and Resource Management in a Distributed Multiagent System\ Islam, Mohammad MahmudulZargarzadeh, Hassan
|
| College
|
:
|
Lamar University - Beaumont
|
| Date
|
:
|
2020
|
| student score
|
:
|
2020
|
| Degree
|
:
|
D.E.
|
| Page No
|
:
|
123
|
| Abstract
|
:
|
In this work, a dynamic territorializing approach for the problem of distributing tasks among a group of robots, and a resource optimization algorithm for determining an appropriate number of agents for a distributed multiagent system has been proposed. Consider the scenario in which a task is comprised of two subtasks – detection and completion; two complementary teams of agents - hunters and gatherers, are assigned for the subtasks. Hunters are assigned with the task of exploring the environment, i.e., detection, whereas gatherers are assigned with the latter subtask. To minimize the workload among the gatherers, the proposed algorithm utilizes the center of mass of the known targets to form territories among the gatherers. The concept of center of mass has been adopted because it simplifies the task of territorial optimization and allows the system to dynamically adapt to changes in the environment by adjusting the assigned partitions as more targets are discovered. In addition, a game-theoretic analysis to justify the agents’ reasoning mechanism to stay within their territory while completing the tasks has been presented. Moreover, simulation results are presented to analyze the performance of the proposed algorithm. First, an investigation on how the performance of the proposed algorithm varies as the frequency of territorializing is varied is conducted. Then, the effect of density of tasks on the performance of the algorithm is examined. Finally, the effectiveness of the proposed algorithm is verified by comparing its performance against an alternative approach. For resource optimization, where the hunters and gatherers are referred to as the resources, a more practical scenario is considered where some information about the environment is given a priori. The task assignment problem for the gatherers is modelled as a Travelling Salesman Problem (TSP) which in turn enables the development of the resource optimization algorithm. Statistical Analysis is conducted to illustrate the effectiveness of the proposed algorithm against several alternative approaches.
|
| Subject
|
:
|
Artificial intelligence
|
|
|
:
|
Robotics
|
| |