Bounded Suboptimal Path Planning with Compressed Path Databases

Abstract

Compressed Path Databases (CPDs) are a state-of-the-art method for path planning. They record, for each start position, an optimal first move to reach any target position. Computing an optimal path with CPDs is extremely fast and requires no state-space search. The main disadvantages are overhead related: building a CPD usually involves an all-pairs precomputation, and storing the result often incurs prohibitive space overheads. Previous research has focused on reducing the size of CPDs and/or improving their online performance. In this paper we consider a new type of CPD, which can also dramatically reduce preprocessing times. Our idea involves computing first-move data for only selected target nodes; chosen in such a way as to guarantee that the cost of any extracted path is within a fixed bound of the optimal solution. Empirical results demonstrate that our new bounded suboptimal CPDs improve preprocessing times by orders of magnitude. They further reduce storage costs, and compute paths more quickly – all in exchange for only a small amount of suboptimality.

More Flexible Proximity Wildcards Path Planning with Compressed Path Databases

“…The above works are all approaches to reduce the memory overhead of CPDs in finding optimal solutions, often accompanied by a significant loss of preprocessing time. In 2020, a centroid-based bounded suboptimal method (Zhao et al 2020) reduces the storage cost by computing only the first-move array of selected nodes, which ensures that the path costs are within the fixed bound of the optimal solution. This approach innovatively reduces preprocessing time and storage cost by trading some suboptimality.…”

Customised Shortest Paths Using a Distributed Reverse Oracle

“…A main consequence is that a reverse CPD can be many times larger than an equivalent forward CPD. We refer the interested reader to Zhao et al (2020) In this work we omit the compression step entirely and propose a new type of reverse oracle that operates directly on first-move tables. As we will see, this seemingly naïve approach actually has several strong advantages: 1.…”