Decoupled Sampling Based Planning Method for Multiple Autonomous Vehicles
This addresses collision avoidance in autonomous vehicle control, but it appears incremental as it builds on existing RRT methods.
The paper tackles the problem of planning for multiple autonomous vehicles by proposing a sampling-based algorithm that uses an improved Rapidly-exploring Random Tree with K-nearest points and a two-stage sampling strategy to perform maneuvers while avoiding collisions, with simulation results showing the algorithm's success.
This paper proposes a sampling based planning algorithm to control autonomous vehicles. We propose an improved Rapidly-exploring Random Tree which includes the definition of K- nearest points and propose a two-stage sampling strategy to adjust RRT in other to perform maneuver while avoiding collision. The simulation results show the success of the algorithm.