FORMI: A Fast Holonomic Path Planning and Obstacle Representation Method Based on Interval Analysis
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems and Robotics, Automation and Mechatronics, CIS and RAM 2019 |
| Publisher | IEEE |
| Pages | 398-403 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728134581 |
| DOIs | |
| Publication status | Published - 1 Nov 2019 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Cybernetics and Intelligent Systems and Robotics, Automation and Mechatronics - Bangkok, Thailand Duration: 18 Nov 2019 → 20 Nov 2019 |
Conference
| Conference | IEEE International Conference on Cybernetics and Intelligent Systems and Robotics, Automation and Mechatronics |
|---|---|
| Country | Thailand |
| City | Bangkok |
| Period | 18/11/19 → 20/11/19 |
Abstract
This paper presents a path planning approach for mobile robots in 2D spaces. The algorithm uses a quadtree decomposition where the discretization precision is improved until a path to the goal is found if one exists. The algorithm uses interval analysis-based methods to categorize the quadtree decomposition to occupied, free and partly occupied cells. The proposed algorithm is compared against other concurrent path planning algorithms, A on an ordinary quadtree, A for shortest path on a binary occupancy grid, and a Dijkstra's algorithm for lowest collision probability in a continuous-valued occupancy grid, in five different scenarios.Compared to the other methods, the main advantage of our method is achieving a compromise between driving distance, safety, and computation time. The proposed algorithm was found to require significantly fewer collision checks in all scenarios while providing sub-optimum results, based on the obstacle distance and path length criteria. The algorithm is suitable for further extension to include non-euclidean measures and for higher dimensions of configuration spaces. The proposed algorithm will be publicly available on GitHub repository.