Cloud RRT ∗: Sampling Cloud based RRT ∗

We present a novel biased sampling technique, Cloud RRT ∗, for efficiently computing high-quality collision-free paths, while maintaining the asymptotic convergence to the optimal solution. Our method uses sampling cloud for allocating samples on promising regions. Our sampling cloud consists of a set of spheres containing a portion of the C-space. In particular, each sphere projects to a collision-free spherical region in the workspace. We initialize our sampling cloud by conducting a workspace analysis based on the generalized Voronoi graph. We then update our sampling cloud to refine the current best solution, while maintaining the global sampling distribution for exploring understudied other homotopy classes. We have applied our method to a 2D motion planning problem with kinematic constraints, i. e. , the Dubins vehicle model, and compared it against the state-of-the-art methods. We achieve better performance, up to three times, over prior methods in a robust manner.

Authors

• Donghyuk Kim
• Junghwan Lee
• Sung-Eui Yoon

Keywords

• Planning
• Convergence
• Trajectory
• Vehicles
• Collision avoidance
• Mobile robots
• Sample Distribution
• Global Distribution
• Path Planning
• Current Solution
• Prior Methods
• Robust Manner
• Kinematic Constraints
• Spherical Region
• Collision-free Path
• Convergence Rate
• Free Space
• Local Optimum
• Probability Sampling
• Constant Speed
• Autonomous Vehicles
• Line Segment
• Start Position
• Optimal Path
• Short Path
• Update Method
• Cost Of Solution
• Pruning Method
• Multiple Spheres
• Improvement In Range
• Visual Point
• Single Query
• Geometric Analysis