Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks
SCIE
SCOPUS
- Title
- Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks
- Authors
- Abdi, Ali; Ranjbar, Mohammad Hassan; PARK, JUHONG
- Date Issued
- 2022-03-01
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- 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
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/110081
- DOI
- 10.3390/s22051697
- ISSN
- 1424-8220
- Article Type
- Article
- Citation
- Sensors, vol. 22, no. 5, 2022-03-01
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- There are no files associated with this item.
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