1,490 research outputs found
Path Planning for Cooperative Routing of Air-Ground Vehicles
We consider a cooperative vehicle routing problem for surveillance and
reconnaissance missions with communication constraints between the vehicles. We
propose a framework which involves a ground vehicle and an aerial vehicle; the
vehicles travel cooperatively satisfying the communication limits, and visit a
set of targets. We present a mixed integer linear programming (MILP)
formulation and develop a branch-and-cut algorithm to solve the path planning
problem for the ground and air vehicles. The effectiveness of the proposed
approach is corroborated through extensive computational experiments on several
randomly generated instances
Routing Unmanned Vehicles in GPS-Denied Environments
Most of the routing algorithms for unmanned vehicles, that arise in data
gathering and monitoring applications in the literature, rely on the Global
Positioning System (GPS) information for localization. However, disruption of
GPS signals either intentionally or unintentionally could potentially render
these algorithms not applicable. In this article, we present a novel method to
address this difficulty by combining methods from cooperative localization and
routing. In particular, the article formulates a fundamental combinatorial
optimization problem to plan routes for an unmanned vehicle in a GPS-restricted
environment while enabling localization for the vehicle. We also develop
algorithms to compute optimal paths for the vehicle using the proposed
formulation. Extensive simulation results are also presented to corroborate the
effectiveness and performance of the proposed formulation and algorithms.Comment: Publised in International Conference on Umanned Aerial System
Optimal Routing of Energy-aware Vehicles in Networks with Inhomogeneous Charging Nodes
We study the routing problem for vehicles with limited energy through a
network of inhomogeneous charging nodes. This is substantially more complicated
than the homogeneous node case studied in [1]. We seek to minimize the total
elapsed time for vehicles to reach their destinations considering both
traveling and recharging times at nodes when the vehicles do not have adequate
energy for the entire journey. We study two versions of the problem. In the
single vehicle routing problem, we formulate a mixed-integer nonlinear
programming (MINLP) problem and show that it can be reduced to a lower
dimensionality problem by exploiting properties of an optimal solution. We also
obtain a Linear Programming (LP) formulation allowing us to decompose it into
two simpler problems yielding near-optimal solutions. For a multi-vehicle
problem, where traffic congestion effects are included, we use a similar
approach by grouping vehicles into "subflows". We also provide an alternative
flow optimization formulation leading to a computationally simpler problem
solution with minimal loss in accuracy. Numerical results are included to
illustrate these approaches.Comment: To appear in proceeding of 22nd Mediterranean Conference on Control
and Automation, MED'1
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