Obstacle Aware Sampling for Path Planning

Many path planning algorithms are based on sampling the state space. While this approach is very simple, it can become costly when the obstacles are unknown, since samples hitting these obstacles are wasted. The goal of this paper is to efficiently identify obstacles in a map and remove them from the sampling space. To this end, we propose a pre-processing algorithm for space exploration that enables more efficient sampling. We show that it can boost the performance of other space sampling methods and path planners. Our approach is based on the fact that a convex obstacle can be approximated provably well by its minimum volume enclosing ellipsoid (MVEE), and a non-convex obstacle may be partitioned into convex shapes. Our main contribution is an al-gorithm that strategically finds a small sample, called the active-coreset, that adaptively samples the space via membership-oracle such that the MVEE of the coreset approximates the MVEE of the obstacle. Experimental results confirm the ef-fectiveness of our approach across multiple planners based on rapidly-exploring random trees, showing significant improve-ment in terms of time and path length.

Authors

• Murad Tukan
• Alaa Maalouf
• Dan Feldman
• Roi Poranne

Keywords

• Shape
• Redundancy
• Sampling methods
• Approximation algorithms
• Path planning
• Space exploration
• Ellipsoids
• Path Length
• State Space
• Ellipsoid
• Goal Of This Paper
• Sample Space
• Convex Shape
• Rapidly-exploring Random Tree
• Running Time
• Simplex
• Free Space
• Unit Vector
• Search Space
• Infinite Number
• Convex Optimization
• Convex Hull
• Pathfinding
• Convex Set
• Extreme Points
• Error Parameters
• Delaunay Triangulation
• Starting State
• Convex Hull Of Set
• Binary Search
• Configuration Space