Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges
Aditya Mandalika, Oren Salzman, Siddhartha Srinivasa

TL;DR
This paper introduces Lazy Receding-Horizon A* (LRA*), a new graph search algorithm that balances edge evaluations and graph operations to optimize total planning time in motion planning problems with expensive edge evaluations.
Contribution
LRA* generalizes existing lazy shortest-path algorithms by incorporating a lazy lookahead, and demonstrates that an intermediate lookahead often minimizes total planning time.
Findings
LRA* outperforms LWA* and LazySP in many scenarios.
An intermediate lazy lookahead minimizes total planning time.
Empirical validation in simulated and manipulation tasks.
Abstract
Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the computational cost of evaluating whether an edge of the roadmap graph is collision-free dominates the running time of search algorithms. Algorithms such as Lazy Weighted A* (LWA*) and LazySP have been proposed to reduce the number of edge evaluations by employing a lazy lookahead (one-step lookahead and infinite-step lookahead, respectively). However, this comes at the expense of additional graph operations: the larger the lookahead, the more the graph operations that are typically required. We propose Lazy Receding-Horizon A* (LRA*) to minimize the total planning time by balancing edge evaluations and graph operations. Endowed with a lazy lookahead, LRA* represents a family of lazy shortest-path graph-search…
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