2 research outputs found
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
Asymptotic Analysis for Greedy Initialization of Threshold-Based Distributed Optimization of Persistent Monitoring on Graphs
This paper considers the optimal multi-agent persistent monitoring problem
defined for a team of agents on a set of nodes (targets) interconnected
according to a fixed network topology. The aim is to control this team so as to
minimize a measure of overall node state uncertainty evaluated over a finite
time interval. A class of distributed threshold-based parametric controllers
has been proposed in prior work to control agent dwell times at nodes and
next-node destinations by enforcing thresholds on the respective node states.
Under such a Threshold Control Policy (TCP), an on-line gradient technique was
used to determine optimal threshold values. However, due to the non-convexity
of the problem, this approach often leads to a poor local optima highly
dependent on the initial thresholds used. To overcome this initialization
challenge, we develop a computationally efficient off-line greedy technique
based on the asymptotic analysis of the network system. This analysis is then
used to generate a high-performing set of initial thresholds. Extensive
numerical results show that such initial thresholds are almost immediately
(locally) optimal or quickly lead to optimal values. In all cases, they perform
significantly better than the locally optimal solutions known to date.Comment: Submitted to Automatic