On Multiple UAV Routing with Stochastic Targets: Performance Bounds and Algorithms

Abstract

In this paper we consider the following problem. A number of Uninhabited Aerial Vehicles (UAVs), modeled as vehicles moving at constant speed along paths of bounded curvature, must visit stochastically-generated targets in a convex, compact region of the plane. Targets are generated according to a spatio-temporal Poisson process, uniformly in the region. It is desired to minimize the expected waiting time between the appearance of a target, and the time it is visited. We present partially centralized algorithms for UAV routing, assigning regions of responsibility to each vehicle, and compare their performance with respect to asymptotic performance bounds, in the light and heavy load limits. Simulation results are presented and discussed.

An economical micro-car testbed for validation of cooperative control strategies

“…As an example of the capabilities of the testbed, we report experimental results from the implementation of a very recent algorithm for UAV routing [16] and compare the observed performance to analytically-derived bounds. Consider a number of vehicles with constant speed and bounded curvature that must visit stochastically-generated targets in a convex, compact two dimensional plane.…”

Motion planning for urban driving using RRT

“…Since for the choice of p 0 , the Dubins path lengths from p 0 to the points (x, y) and (x, −y) are equal, it suffices to consider the caseq = (x, |y|) ∈ R × R + . The minimum length L ρ (q) of a Dubins path from p 0 to q can be obtained analytically [12]. Let ρ denote the minimum turning radius of the vehicle.…”

The coverage problem for loitering Dubins vehicles

“…Let L ρ : SE(2) × R 2 → R ≥0 be the length of the shortest Dubins path from initial position and orientation, described by an element of SE (2), to a point q ∈ R 2 , where R is the set of real numbers and R ≥0 is the set of non-negative real numbers. Recall that L ρ is continuous almost everywhere [11].…”