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...

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Main Authors: Yu-Cheng Chang, Anna Dostovalova, Chin-Teng Lin, Jijoong Kim
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
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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
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AT annadostovalova intelligentmultirobotnavigationandarrivaltimecontrolusingascalablepsooptimizedhierarchicalcontroller
AT chintenglin intelligentmultirobotnavigationandarrivaltimecontrolusingascalablepsooptimizedhierarchicalcontroller
AT jijoongkim intelligentmultirobotnavigationandarrivaltimecontrolusingascalablepsooptimizedhierarchicalcontroller
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