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Analysis and Evaluation of Path Planning Algorithms for Autonomous Driving of Electromagnetically Actuated Microrobot
- Source :
- International Journal of Control, Automation and Systems. 18:2943-2954
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- In order to overcome the limitations of the A* algorithm in the autonomous control of electromagnetically actuated microrobots, this study introduces three modified path planning algorithms (A*-WAPP, A*-waypoints, A*-WAPP-waypoints) using the concept of Wall Avoiding Path Planning (WAPP) and waypoints. Through the autonomous driving experiment of an electromagnetically actuated microrobot, the three modified path planning algorithms based on A* and the original A* algorithm were evaluated using four performance measures. As a result, it was confirmed whether significant changes exist between the A* algorithm and the A*-based modified algorithms about the fitness for the autonomous driving environment of the electromagnetically actuated microrobot. First, compared to the path of the A* algorithm, A*-WAPP algorithm generated a stable path that dramatically reduced the collision between the microrobot and the obstacle. However, in the autonomous driving of the microrobot, A*-WAPP algorithm increased the driving distance and driving time. On the other hand, A*-waypoints algorithm showed a tendency in reducing the driving distance and driving time of the autonomous driving microrobot by simplifying the generated path, but still showed the collision problem between the microrobot and the obstacle. Finally, the path generated by the A*-WAPP-waypoints algorithm greatly increased the stability of the autonomous driving microrobot and showed great advantages of the decreases in the driving distance and driving time. In conclusion, it was confirmed that the proposed A*-WAPP-waypoints algorithm showed the best path generation results in the autonomous driving microrobot among the three A*-based algorithms.
- Subjects :
- 0209 industrial biotechnology
business.industry
Computer science
Stability (learning theory)
Robotics
02 engineering and technology
Mechatronics
Collision
Computer Science Applications
020901 industrial engineering & automation
Control and Systems Engineering
Obstacle
Path (graph theory)
Collision problem
Artificial intelligence
Motion planning
business
Algorithm
Subjects
Details
- ISSN :
- 20054092 and 15986446
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- International Journal of Control, Automation and Systems
- Accession number :
- edsair.doi...........54864b761d3a583b0ab954b82b17936b
- Full Text :
- https://doi.org/10.1007/s12555-019-0637-9