The Maverick planner: An efficient hierarchical planner for autonomous vehicles in unstructured environments
| Autor: | Elliot R. Johnson, Jerry Towler, Jason Gassaway, Neal Seegmiller |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Speedup Computer science 020208 electrical & electronic engineering Real-time computing Sampling (statistics) ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Directed graph Planner Waypoint 020901 industrial engineering & automation Local optimum Path (graph theory) 0202 electrical engineering electronic engineering information engineering computer computer.programming_language |
| Zdroj: | IROS |
| DOI: | 10.1109/iros.2017.8206021 |
| Popis: | Planning kinodynamically feasible trajectories for autonomous vehicles is computationally expensive, especially when planning over long distances in unstructured environments. This paper presents a hierarchical planner, called the Maverick planner, which can find such trajectories efficiently. It comprises two parts: a waypoint planner that uses a simplified vehicle model and an RRT∗ planner that respects full kinodynamic constraints. The waypoint planner quickly finds a directed graph of waypoints from start to goal, which is then used to bias sampling and speed up computation in RRT∗. The Maverick planner is capable of anytime planning and continuous replanning. It has been tested extensively in simulation and on real vehicles. When planning on a sensor-generated map of the SwRI test track it can find a feasible path over 0.5 km in under 16 ms, and refine that path to within 1% of the local optimum in 0.5 seconds. |
| Databáze: | OpenAIRE |
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