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Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks
| Content Provider | MDPI |
|---|---|
| Author | Abdi, Ali Ranjbar, Mohammad Hassan Park, Ju Hong |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 1697 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22051697 |
| Journal | Sensors |
| Issue Number | 5 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-22 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Path Planning Q-learning Neural Network Yolo Algorithm Computer Vision Robot Arm Target Reaching Obstacle Avoidance |
| Content Type | Text |
| Resource Type | Article |
