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.
Graph Search Based Local Path Planning with Adaptive Node Sampling
2018 IEEE Intelligent Vehicles Symposium (IV) ; 2084-2089
2018-06-01
1674988 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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