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Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples de…
Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot
Rapidly-exploring random trees (RRTs) are popular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the probl…
The Probability Density Function of a Transformation-based Hyperellipsoid Sampling Technique
Jonathan D. Gammell, Timothy D. Barfoot
Sun and Farooq [2] showed that random samples can be efficiently drawn from an arbitrary n-dimensional hyperellipsoid by transforming samples drawn randomly from the unit n-ball. T…
On Recursive Random Prolate Hyperspheroids
Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot
This technical note analyzes the properties of a random sequence of prolate hyperspheroids with common foci. Each prolate hyperspheroid in the sequence is defined by a sample drawn…