4,723 research outputs found
Dynamic Motion Planning for Aerial Surveillance on a Fixed-Wing UAV
We present an efficient path planning algorithm for an Unmanned Aerial
Vehicle surveying a cluttered urban landscape. A special emphasis is on
maximizing area surveyed while adhering to constraints of the UAV and partially
known and updating environment. A Voronoi bias is introduced in the
probabilistic roadmap building phase to identify certain critical milestones
for maximal surveillance of the search space. A kinematically feasible but
coarse tour connecting these milestones is generated by the global path
planner. A local path planner then generates smooth motion primitives between
consecutive nodes of the global path based on UAV as a Dubins vehicle and
taking into account any impending obstacles. A Markov Decision Process (MDP)
models the control policy for the UAV and determines the optimal action to be
undertaken for evading the obstacles in the vicinity with minimal deviation
from current path. The efficacy of the proposed algorithm is evaluated in an
updating simulation environment with dynamic and static obstacles.Comment: Accepted at International Conference on Unmanned Aircraft Systems
201
Approximating Nearest Neighbor Distances
Several researchers proposed using non-Euclidean metrics on point sets in
Euclidean space for clustering noisy data. Almost always, a distance function
is desired that recognizes the closeness of the points in the same cluster,
even if the Euclidean cluster diameter is large. Therefore, it is preferred to
assign smaller costs to the paths that stay close to the input points.
In this paper, we consider the most natural metric with this property, which
we call the nearest neighbor metric. Given a point set P and a path ,
our metric charges each point of with its distance to P. The total
charge along determines its nearest neighbor length, which is formally
defined as the integral of the distance to the input points along the curve. We
describe a -approximation algorithm and a
-approximation algorithm to compute the nearest neighbor
metric. Both approximation algorithms work in near-linear time. The former uses
shortest paths on a sparse graph using only the input points. The latter uses a
sparse sample of the ambient space, to find good approximate geodesic paths.Comment: corrected author nam
Exact Geosedics and Shortest Paths on Polyhedral Surface
We present two algorithms for computing distances along a non-convex polyhedral surface. The first algorithm computes exact minimal-geodesic distances and the second algorithm combines these distances to compute exact shortest-path distances along the surface. Both algorithms have been extended to compute the exact minimalgeodesic paths and shortest paths. These algorithms have been implemented and validated on surfaces for which the correct solutions are known, in order to verify the accuracy and to measure the run-time performance, which is cubic or less for each algorithm. The exact-distance computations carried out by these algorithms are feasible for large-scale surfaces containing tens of thousands of vertices, and are a necessary component of near-isometric surface flattening methods that accurately transform curved manifolds into flat representations.National Institute for Biomedical Imaging and Bioengineering (R01 EB001550
Resilient Backhaul Network Design Using Hybrid Radio/Free-Space Optical Technology
The radio-frequency (RF) technology is a scalable solution for the backhaul
planning. However, its performance is limited in terms of data rate and
latency. Free Space Optical (FSO) backhaul, on the other hand, offers a higher
data rate but is sensitive to weather conditions. To combine the advantages of
RF and FSO backhauls, this paper proposes a cost-efficient backhaul network
using the hybrid RF/FSO technology. To ensure a resilient backhaul, the paper
imposes a given degree of redundancy by connecting each node through
link-disjoint paths so as to cope with potential link failures. Hence, the
network planning problem considered in this paper is the one of minimizing the
total deployment cost by choosing the appropriate link type, i.e., either
hybrid RF/FSO or optical fiber (OF), between each couple of base-stations while
guaranteeing link-disjoint connections, a data rate target, and a
reliability threshold. The paper solves the problem using graph theory
techniques. It reformulates the problem as a maximum weight clique problem in
the planning graph, under a specified realistic assumption about the cost of OF
and hybrid RF/FSO links. Simulation results show the cost of the different
planning and suggest that the proposed heuristic solution has a
close-to-optimal performance for a significant gain in computation complexity
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