3 research outputs found
Algorithms for Routing of Unmanned Aerial Vehicles with Mobile Recharging Stations
We study the problem of planning a tour for an energy-limited Unmanned Aerial
Vehicle (UAV) to visit a set of sites in the least amount of time. We envision
scenarios where the UAV can be recharged along the way either by landing on
stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as
mobile recharging stations. This leads to a new variant of the Traveling
Salesperson Problem (TSP) with mobile recharging stations. We present an
algorithm that finds not only the order in which to visit the sites but also
when and where to land on the charging stations to recharge. Our algorithm
plans tours for the UGVs as well as determines best locations to place
stationary charging stations. While the problems we study are NP-Hard, we
present a practical solution using Generalized TSP that finds the optimal
solution. If the UGVs are slower, the algorithm also finds the minimum number
of UGVs required to support the UAV mission such that the UAV is not required
to wait for the UGV. Our simulation results show that the running time is
acceptable for reasonably sized instances in practice.Comment: 7 pages, 14 figures, ICRA2018 under revie
Visual Monitoring for Multiple Points of Interest on a 2.5D Terrain using a UAV with Limited Field-of-View Constraint
Varying terrain conditions and limited field-of-view restricts the visibility
of aerial robots while performing visual monitoring operations. In this paper,
we study the multi-point monitoring problem on a 2.5D terrain using an unmanned
aerial vehicle (UAV) with limited camera field-of-view. This problem is NP-Hard
and hence we develop a two phase strategy to compute an approximate tour for
the UAV. In the first phase, visibility regions on the flight plane are
determined for each point of interest. In the second phase, a tour for the UAV
to visit each visibility region is computed by casting the problem as an
instance of the Traveling Salesman Problem with Neighbourhoods (TSPN). We
design a constant-factor approximation algorithm for the TSPN instance.
Further, we reduce the TSPN instance to an instance of the Generalized
Traveling Salesman Problem (GTSP) and devise an ILP formulation to solve it. We
present a comparative evaluation of solutions computed using a branch-and-cut
implementation and an off-the-shelf GTSP tool -- GLNS, while varying the points
of interest density, sampling resolution and camera field-of-view. We also show
results from preliminary field experiments
Collision-Free Trajectory Design for 2D Persistent Monitoring Using Second-Order Agents
This paper considers a two-dimensional persistent monitoring problem by
controlling movements of second-order agents to minimize some uncertainty
metric associated with targets in a dynamic environment. In contrast to common
sensing models depending only on the distance from a target, we introduce an
active sensing model which considers the distance between an agent and a
target, as well as the agent's velocity. We propose an objective function which
can achieve a collision-free agent trajectory by penalizing all possible
collisions. Applying structural properties of the optimal control derived from
the Hamiltonian analysis, we limit agent trajectories to a simpler parametric
form under a family of 2D curves depending on the problem setting, e.g.
ellipses and Fourier trajectories. Our collision-free trajectories are
optimized through an event-driven Infinitesimal Perturbation Analysis (IPA) and
gradient descent method. Although the solution is generally locally optimal,
this method is computationally efficient and offers an alternative to other
traditional time-driven methods. Finally, simulation examples are provided to
demonstrate our proposed results.Comment: 13 pages arXiv, to be published in IEEE Transactions on Control of
Network System