1 Graduate Student, Department of Aerospace Engineering, Student Member AIAA 3 Associate Professor, Department of Aerospace Engineering, Associate Fellow, AIAA SMART Heuristic for Pickup and Delivery Problem (PDP) with Cooperative UAVs Chelsea Sabo 1 University of Cincinnati, Cincinnati, Ohio, 45221 Kelly Cohen 2 University of Cincinnati, Cincinnati, Ohio, 45221 Abstract Pickup and Delivery Problems (PDPs) are a subset of Vehicle Routing Problems (VRPs) which require a vehicle to service targets by picking them up at an origin and delivering them to their unique destination. With respect to surveillance functions, this becomes a realistic problem as UAVs are restricted by operating range, data rate, Anti-Jam margins, and cost. Therefore, UAVs must be allocated to “pickup” targets and then “deliver” them, from within a prescribed communication space, back to a command and control HQ. To maximize the speed/amount of information transmitted from this communication region, the objective of allocating the UAVs is such that the total service time (pickup and delivery) of all the targets is minimized. Previous work on PDPs has shown that as the problem gets more complicated (i.e. more targets and more vehicles) the solution space increases exponentially, and the execution time to find an optimal solution is impossible to implement. Additionally, previously related work using a heuristic solution has been applied to this problem showing that good result can be maintained (within ~15- 20% of the optimal solution for small cases). The focus of this research is to develop an alternate heuristic algorithm, deemed SMART from here on, that can perform near optimally (within ~5%) and scales as the problem gets more complicated. Also, the algorithm is developed such that it is easily extendable to a dynamic scenario as this research progresses. This algorithm is described in detail and has shown that it reaches this performance metric while requiring a significantly reduced computational time when compared to the time needed to obtain the optimal solution. I. Introduction Surveillance functions are of paramount importance to U.S. defense system, and these systems are comprised of various means for acquiring and processing information needed by military commanders/national security decision makers. The future of these systems focus on including human intelligence, measurement and signature intelligence, signals intelligence, imagery intelligence, and open source intelligence through algorithms, software, and automation. Additionally in the not too distant future, cooperative UAV teams are anticipated to provide this much needed support more effectively [1]. A very important aspect in the design of UAV cooperative control systems is the ability to collect and transmit the data collected to a decision making authority such as command and control headquarters. Ideally speaking, a group of collaborating UAVs should be able to communicate “whenever and as much as they need to [ 1].” While this should be the standard, it is far from a reality. Typically the focus is on minimizing mission costs while communication restrictions are often ignored. However, there is a distinct need for collecting high-resolution snapshots of targets anywhere in the environment (no communication constraints) in addition to the need to reliably get this information back in a timely way. In reality, operating range, data rate, Anti-Jam margins and cost are limiting factors that need to be considered in order to operate effectively [2]. While „command and control‟ signals can be delivered with low bandwidth data, there is a distinct need for a high bandwidth delivery mechanism. Moreover, communication constraints and limits define all UAV activity and there is a growing interest in the research community to expand current capabilities. Infotech@Aerospace 2011 29 - 31 March 2011, St. Louis, Missouri AIAA 2011-1464 Copyright © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. Downloaded by UNIVERSITY OF CINCINNATI on November 24, 2014 | http://arc.aiaa.org | DOI: 10.2514/6.2011-1464