5 research outputs found
Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps
This is the final version. Available from SPIE via the DOI in this record.Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018, 10 - 13 September 2018, Berlin, GermanyUnmanned Aerial Vehicles (UAVs) have shown great capability for disaster management due to their fast speed,
automated deployment and low maintenance requirements. In recent years, disasters such as flooding are having
increasingly damaging societal and environmental effects. To reduce their impact, real-time and reliable flood
monitoring and prevention strategies are required. The limited battery life of small lightweight UAVs imposes
efficient strategies to subsample the sensing field. This paper proposes a novel solution to maximise the number of
inspected flooded cells while keeping the travelled distance bounded. Our proposal solves the so-called continuous
Travelling Salesman Problem (TSP), where the costs of travelling from one cell to another depend not only on
the distance, but also on the presence of water. To determine the optimal path between checkpoints, we employ
the fast sweeping algorithm using a cost function defined from hyperspectral satellite maps identifying flooded
regions. Preliminary results using MODIS flood maps show that our UAV planning strategy achieves a covered
flooded surface approximately 4 times greater for the same travelled distance when compared to the conventional
TSP solution. These results show new insights on the use of hyperspectral imagery acquired from UAVs to
monitor water resourcesThis work was funded by the Royal Society of Edinburgh and National Science Foundation of China within the
international project “Flood Detection and Monitoring using Hyperspectral Remote Sensing from Unmanned
Aerial Vehicles” (project NNS/INT 15-16 Casaseca)
Continuous relaxations for the traveling salesman problem
In this work, we aim to explore connections between dynamical systems
techniques and combinatorial optimization problems. In particular, we construct
heuristic approaches for the traveling salesman problem (TSP) based on
embedding the relaxed discrete optimization problem into appropriate manifolds.
We explore multiple embedding techniques -- namely, the construction of new
dynamical systems on the manifold of orthogonal matrices and associated
Procrustes approximations of the TSP cost function. Using these dynamical
systems, we analyze the local neighborhood around the optimal TSP solutions
(which are equilibria) using computations to approximate the associated
\emph{stable manifolds}. We find that these flows frequently converge to
undesirable equilibria. However, the solutions of the dynamical systems and the
associated Procrustes approximation provide an interesting biasing approach for
the popular Lin--Kernighan heuristic which yields fast convergence. The
Lin--Kernighan heuristic is typically based on the computation of edges that
have a `high probability' of being in the shortest tour, thereby effectively
pruning the search space. Our new approach, instead, relies on a natural
relaxation of the combinatorial optimization problem to the manifold of
orthogonal matrices and the subsequent use of this solution to bias the
Lin--Kernighan heuristic. Although the initial cost of computing these edges
using the Procrustes solution is higher than existing methods, we find that the
Procrustes solution, when coupled with a homotopy computation, contains
valuable information regarding the optimal edges. We explore the Procrustes
based approach on several TSP instances and find that our approach often
requires fewer -opt moves than existing approaches. Broadly, we hope that
this work initiates more work in the intersection of dynamical systems theory
and combinatorial optimization