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Development of collision avoidance system for multiple autonomous mobile robots

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dc.citedby10
dc.contributor.authorHou Y.C.en_US
dc.contributor.authorMohamed Sahari K.S.en_US
dc.contributor.authorWeng L.Y.en_US
dc.contributor.authorFoo H.K.en_US
dc.contributor.authorAbd Rahman N.A.en_US
dc.contributor.authorAtikah N.A.en_US
dc.contributor.authorHomod R.Z.en_US
dc.contributor.authorid37067465000en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid26326032700en_US
dc.contributor.authorid57218559153en_US
dc.contributor.authorid56342466200en_US
dc.contributor.authorid57188930241en_US
dc.contributor.authorid36994633500en_US
dc.date.accessioned2023-05-29T08:08:54Z
dc.date.available2023-05-29T08:08:54Z
dc.date.issued2020
dc.descriptionAgricultural robots; Collision avoidance; Computer software; Data communication equipment; Highway accidents; Robots; Safety devices; Vehicle to vehicle communications; Autonomous Mobile Robot; Collision avoidance systems; Collision scenarios; Comprehensive comparisons; Crossing intersections; Experimental testing; Inter-robot communication; Rear-end collisions; Autonomous vehiclesen_US
dc.description.abstractThis article presents a collision avoidance system for multiple robots based on the current autonomous car collision avoidance system. The purpose of the system is to improve the current autonomous car collision avoidance system by including data input of other vehicles� velocity and positioning via vehicle-to-vehicle communication into the current autonomous car collision avoidance system. There are two TurtleBots used in experimental testing. TurtleBot is used as the robot agent while Google Lightweight Communication and Marshalling is used for inter-robot communication. Additionally, Gazebo software is used to run the simulation. There are two types of collision avoidance system algorithm (collision avoidance system without inter-robot communication and collision avoidance system with inter-robot communication) that are developed and tested in two main road crash scenarios, rear end collision scenario and junction crossing intersection collision scenario. Both algorithms are tested and run both in simulation and experiment setup, each with 10 repetitions for Lead TurtleBot sudden stop, Lead TurtleBot decelerate, Lead TurtleBot slower speed, and straight crossing path conditions. Simulation and experimental results data for each algorithm are recorded and tabulated. A comprehensive comparison of performance between the proposed algorithms is analyzed. The results showed that the proposed system is able to prevent collision between vehicles with an acceptable success rate. � The Author(s) 2020.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1177/1729881420923967
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85089473036
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089473036&doi=10.1177%2f1729881420923967&partnerID=40&md5=1f311adf07f632d3455f17cdec9174e3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25395
dc.identifier.volume17
dc.publisherSAGE Publications Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Robotic Systems
dc.titleDevelopment of collision avoidance system for multiple autonomous mobile robotsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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