Practical Considerations for Implementing an Autonomous, Persistent, Intelligence, Surveillance, and Reconnaissance System

摘要

We are interested in the persistent surveillance of an area of interest comprised of heterogeneous tasks (or targets) that need to be completed (or visited) in a repeated manner subject to constraints on time between successive visits. The task is undertaken by a set of heterogeneous UAVs which autonomously execute the mission. In addition to geographically distributed tasks, the mission may also include a central node (control target), where data collected from the different targets need to be delivered. In this context, the performance of the system, in addition to the desired revisit rate of the tasks may also entail minimizing the delay in delivering the data collected from a target/task to the central node. We detail, in this paper, a completely autonomous Persistent, Intelligence, Surveillance and Reconnaissance (PISR) System, that addresses the mission requirements. In particular, we focus on practical considerations in terms of scalable optimization and heuristic methods that solve the underlying problem and also discuss the on-board implementation of the chosen optimization schema. We provide details on an in-house software framework that enables easy implementation of the optimization algorithms on commercial drones. To solve the problem, we consider three different optimization schemes based on branch and bound (tree search), MILP formulation and Dynamic Programming. We compare and contrast the three approaches with details on the respective benefits and pitfalls and also touch upon easily implementable heuristic methods motivated by the optimal solution.