DC Field | Value | Language |
---|---|---|
dc.contributor.author | Abdi, Ali | - |
dc.contributor.author | Ranjbar, Mohammad Hassan | - |
dc.contributor.author | PARK, JUHONG | - |
dc.date.accessioned | 2022-03-02T09:00:07Z | - |
dc.date.available | 2022-03-02T09:00:07Z | - |
dc.date.created | 2022-03-02 | - |
dc.date.issued | 2022-03-01 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/110081 | - |
dc.description.abstract | Computer vision-based path planning can play a crucial role in numerous technologically driven smart applications. Although various path planning methods have been proposed, limitations, such as unreliable three-dimensional (3D) localization of objects in a workspace, time-consuming computational processes, and limited two-dimensional workspaces, remain. Studies to address these problems have achieved some success, but many of these problems persist. Therefore, in this study, which is an extension of our previous paper, a novel path planning approach that combined computer vision, Q-learning, and neural networks was developed to overcome these limitations. The proposed computer vision-neural network algorithm was fed by two images from two views to obtain accurate spatial coordinates of objects in real time. Next, Q-learning was used to determine a sequence of simple actions: up, down, left, right, backward, and forward, from the start point to the target point in a 3D workspace. Finally, a trained neural network was used to determine a sequence of joint angles according to the identified actions. Simulation and experimental test results revealed that the proposed combination of 3D object detection, an agent-environment interaction in the Q-learning phase, and simple joint angle computation by trained neural networks considerably alleviated the limitations of previous studies | - |
dc.language | English | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.relation.isPartOf | Sensors | - |
dc.title | Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/s22051697 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Sensors, v.22, no.5 | - |
dc.identifier.wosid | 000768253500001 | - |
dc.citation.number | 5 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 22 | - |
dc.contributor.affiliatedAuthor | PARK, JUHONG | - |
dc.identifier.scopusid | 2-s2.0-85125097617 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | path planning | - |
dc.subject.keywordAuthor | Q-learning | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | YOLO algorithm | - |
dc.subject.keywordAuthor | computer vision | - |
dc.subject.keywordAuthor | robot arm | - |
dc.subject.keywordAuthor | target reaching | - |
dc.subject.keywordAuthor | obstacle avoidance | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
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