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In this article we consider a path planning problem for a single fixed-wing aircraft performing an ISR (Intelligence Surveillance Reconnaissance) mission using EO (ElectroOptical) camera(s). We give a mathematical formulation of the general aircraft visual reconnaissance problem for static ground targets in terrain and show that, under simplifying assumptions, it can be reduced to a variant of the Traveling Salesman Probem which we call the PVDTSP (Polygon-Visiting Dubins Traveling Salesman Problem). We design a genetic algorithm to solve the PVDTSP and validate it in a Monte Carlo numerical study. For fixed computation time, the genetic algorithm produces reconnaissance tours on average nearly half the length (and thus can be flown in half the time) of those delivered by a random search. The modular design of the genetic algorithm allows it to easily be extended to handle realistic assumptions such as wind and airspace constraints.

SUMMARYThis article presents a distributed algorithm for a group of robotic agents with omnidirectional vision to deploy into nonconvex polygonal environments with holes. Agents begin deployment from a common point, possess no prior knowledge of the environment, and operate only under line-of-sight sensing and communication. The objective of the deployment is for the agents to achieve full visibility coverage of the environment while maintaining line-of-sight connectivity with each other. This is achieved by incrementally partitioning the environment into distinct regions, each completely visible from some agent. Proofs are given of (i) convergence, (ii) upper bounds on the time and number of agents required, and (iii) bounds on the memory and communication complexity. Simulation results and description of robust extensions are also included.

This article considers a path planning problem for a single fixed-wing aircraft performing a reconnaissance mission using EO (Electro-Optical) camera(s). A mathematical formulation of the general aircraft visual reconnaissance problem for static ground targets in terrain is given and it is shown, under simplifying assumptions, that it can be reduced to what we call the PVDTSP (Polygon-Visiting Dubins Traveling Salesman Problem), a variation of the famous TSP (Traveling Salesman Problem). Two algorithms are developed to solve the PVDTSP. They fall into the class of algorithms known as sampling-based roadmap methods because they operate by sampling a finite set of points from a continuous state space in order to reduce a continuous motion planning problem to planning on a finite discrete graph. The first method is resolution complete, which means it provably converges to a nonisolated global optimum as the number of samples grows. The second method achieves slightly shorter computation times by using approximate dynamic programming, but consequently is only guaranteed to converge to a nonisolated global optimum modulo target order. Numerical experiments indicate that, for up to about 20 targets, both methods deliver good solutions suitably quickly for online purposes. Additionally, both algorithms allow trade-off of computation time for solution quality and are shown extensible to handle wind, airspace constraints, any vehicle dynamics, and open-path (vs. closed-tour) problems.

This article develops an algorithm for a group of guards statically positioned in a nonconvex polygonal environment with holes. Each guard possesses a single searchlight, a ray sensor which can rotate about the guard's position but cannot penetrate the boundary of the environment. A point is detected by a searchlight if and only if the point is on the ray at some instant. Targets are points which move arbitrarily fast. The objective of the proposed algorithm is to compute a schedule to rotate a set of searchlights in such a way that any target in an environment will necessarily be detected in finite time. This is known as the Searchlight Scheduling Problem and was described originally in 1990 by Sugihara et al. We take an approach known as exact cell decomposition in the motion planning literature. The algorithm operates by decomposing the searchlights' joint configuration space and the environment, and then by constructing a so-called information graph. Searching the information graph for a path between desired states yields a search schedule. We also introduce a new problem called the φ-Searchlight Scheduling Problem in which φ-searchlights sense not just along a ray, but over a finite field of view. We show that our results for searchlight scheduling can be directly extended for φ-searchlight scheduling. Proofs of completeness, complexity bounds, and computed examples are presented.