Graph Search Based Local Path Planning with Adaptive Node Sampling
| Autor: | Tsuyoshi Tasaki, Rie Katsuki, Tomoki Watanabe |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science Approximation algorithm 02 engineering and technology Planner Computer Science::Robotics Piecewise linear function 020901 industrial engineering & automation Obstacle 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Motion planning Algorithm computer Dijkstra's algorithm Sampling interval computer.programming_language |
| Zdroj: | Intelligent Vehicles Symposium |
| DOI: | 10.1109/ivs.2018.8500396 |
| Popis: | This paper describes a graph search-based local path planner with an adaptive node sampling according to positions of obstacles. Randomly sampled nodes of a graph in a traversable region for finding a local path can generate a winding path due to connection between the randomly sampling nodes. Node sampling with constant intervals can fail to find a proper path in a case that an interval between adjacent obstacles are smaller than the sampling interval. To solve these problems, our path planner changes node sampling strategy according to obstacle positions; it samples nodes along a reference path, e.g., the center line of the lane, if there are no obstacles, but densely samples nodes around obstacles. After sampling, the planner searches the graph using Dijkstra's algorithm for finding an optimal trajectory. For efficient search for the optimal trajectory, the planner firstly generates a trajectory approximated by piecewise linear lines with minimum cost, and fine-tunes it by adjusting node positions with curved edges having smaller cost. We test the planner through simulations in which an ego vehicle drive along a reference path when there are no obstacles, and densely sample around obstacles to improve robustness against obstacle locations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|