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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…
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…
Near Optimal Bayesian Active Learning for Decision Making
Shervin Javdani, Yuxin Chen, Amin Karbasi +3
How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce unc…