9,397 research outputs found
Large-scale V/STOL testing
Several facets of large-scale testing of V/STOL aircraft configurations are discussed with particular emphasis on test experience in the Ames 40- by 80-Foot Wind Tunnel. Examples of powered-lift test programs are presented in order to illustrate tradeoffs confronting the planner of V/STOL test programs. Large-scale V/STOL wind-tunnel testing can sometimes compete with small-scale testing in the effort required (overall test time) and program costs because of the possibility of conducting a number of different tests with a single large-scale model where several small-scale models would be required. The benefits of both high- or full-scale Reynolds numbers, more detailed configuration simulation, and number and type of onboard measurements are studied
The FastMap Algorithm for Shortest Path Computations
We present a new preprocessing algorithm for embedding the nodes of a given
edge-weighted undirected graph into a Euclidean space. The Euclidean distance
between any two nodes in this space approximates the length of the shortest
path between them in the given graph. Later, at runtime, a shortest path
between any two nodes can be computed with A* search using the Euclidean
distances as heuristic. Our preprocessing algorithm, called FastMap, is
inspired by the data mining algorithm of the same name and runs in near-linear
time. Hence, FastMap is orders of magnitude faster than competing approaches
that produce a Euclidean embedding using Semidefinite Programming. FastMap also
produces admissible and consistent heuristics and therefore guarantees the
generation of shortest paths. Moreover, FastMap applies to general undirected
graphs for which many traditional heuristics, such as the Manhattan Distance
heuristic, are not well defined. Empirically, we demonstrate that A* search
using the FastMap heuristic is competitive with A* search using other
state-of-the-art heuristics, such as the Differential heuristic
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to
their goal locations without collisions. In this paper, we study the lifelong
variant of MAPF, where agents are constantly engaged with new goal locations,
such as in large-scale automated warehouses. We propose a new framework
Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by
decomposing the problem into a sequence of Windowed MAPF instances, where a
Windowed MAPF solver resolves collisions among the paths of the agents only
within a bounded time horizon and ignores collisions beyond it. RHCR is
particularly well suited to generating pliable plans that adapt to continually
arriving new goal locations. We empirically evaluate RHCR with a variety of
MAPF solvers and show that it can produce high-quality solutions for up to
1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse
instances, significantly outperforming existing work.Comment: Published at AAAI 202
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