Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller
We present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents such that they achieve simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning Lidar to make movement decisions bas...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2020-08-01
|
Series: | Frontiers in Artificial Intelligence |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/frai.2020.00050/full |
id |
doaj-7e0105f2c3cd4e85ad42c4b87d254530 |
---|---|
record_format |
Article |
spelling |
doaj-7e0105f2c3cd4e85ad42c4b87d2545302020-11-25T03:20:10ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122020-08-01310.3389/frai.2020.00050502843Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical ControllerYu-Cheng Chang0Anna Dostovalova1Chin-Teng Lin2Jijoong Kim3Computational Intelligence and Brain Computer Interface (CIBCI) Lab, Centre for Artificial Intelligence (CAI), University of Technology, Sydney, NSW, AustraliaDefence Science & Technology Group, Adelaide, SA, AustraliaComputational Intelligence and Brain Computer Interface (CIBCI) Lab, Centre for Artificial Intelligence (CAI), University of Technology, Sydney, NSW, AustraliaDefence Science & Technology Group, Adelaide, SA, AustraliaWe present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents such that they achieve simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning Lidar to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of control modules to enable synchronization of the agents' arrival times while avoiding collision with obstacles. The proposed control module controls both moving speeds and directions of the robots to achieve the simultaneous target-reaching task. The control system consists of two levels: the lower-level individual navigation control for obstacle avoidance and the higher-level coordination control to ensure the same time of arrival for all robots at their target. The first approach is based on cascading fuzzy logic controllers, and the second approach considers the use of a Long Short-Term Memory recurrent neural network module alongside fuzzy logic controllers. The parameters of all the controllers are optimized using the particle swarm optimization algorithm. To increase the scalability of the proposed control modules, an interpolation method is introduced to determine the velocity scaling factors and the searching directions of the robots. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware, which will be conducted in the next phase of our research.https://www.frontiersin.org/article/10.3389/frai.2020.00050/fullhierarchical fuzzy systemfuzzy logic controlmulti-agent controlnavigationarrival-time control |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yu-Cheng Chang Anna Dostovalova Chin-Teng Lin Jijoong Kim |
spellingShingle |
Yu-Cheng Chang Anna Dostovalova Chin-Teng Lin Jijoong Kim Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller Frontiers in Artificial Intelligence hierarchical fuzzy system fuzzy logic control multi-agent control navigation arrival-time control |
author_facet |
Yu-Cheng Chang Anna Dostovalova Chin-Teng Lin Jijoong Kim |
author_sort |
Yu-Cheng Chang |
title |
Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller |
title_short |
Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller |
title_full |
Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller |
title_fullStr |
Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller |
title_full_unstemmed |
Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller |
title_sort |
intelligent multirobot navigation and arrival-time control using a scalable pso-optimized hierarchical controller |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Artificial Intelligence |
issn |
2624-8212 |
publishDate |
2020-08-01 |
description |
We present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents such that they achieve simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning Lidar to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of control modules to enable synchronization of the agents' arrival times while avoiding collision with obstacles. The proposed control module controls both moving speeds and directions of the robots to achieve the simultaneous target-reaching task. The control system consists of two levels: the lower-level individual navigation control for obstacle avoidance and the higher-level coordination control to ensure the same time of arrival for all robots at their target. The first approach is based on cascading fuzzy logic controllers, and the second approach considers the use of a Long Short-Term Memory recurrent neural network module alongside fuzzy logic controllers. The parameters of all the controllers are optimized using the particle swarm optimization algorithm. To increase the scalability of the proposed control modules, an interpolation method is introduced to determine the velocity scaling factors and the searching directions of the robots. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware, which will be conducted in the next phase of our research. |
topic |
hierarchical fuzzy system fuzzy logic control multi-agent control navigation arrival-time control |
url |
https://www.frontiersin.org/article/10.3389/frai.2020.00050/full |
work_keys_str_mv |
AT yuchengchang intelligentmultirobotnavigationandarrivaltimecontrolusingascalablepsooptimizedhierarchicalcontroller AT annadostovalova intelligentmultirobotnavigationandarrivaltimecontrolusingascalablepsooptimizedhierarchicalcontroller AT chintenglin intelligentmultirobotnavigationandarrivaltimecontrolusingascalablepsooptimizedhierarchicalcontroller AT jijoongkim intelligentmultirobotnavigationandarrivaltimecontrolusingascalablepsooptimizedhierarchicalcontroller |
_version_ |
1724619104587874304 |