American Institute of Aeronautics and Astronautics 1 THREE-DIMENSIONAL MULTI-OBJECTIVE PATH PLANNING OF UNMANNED AERIAL VEHICLES USING PARTICLE SWARM OPTIMIZATION Jung Leng Foo 1 , Jared S. Knutzon 2 , James H. Oliver 3 , and Eliot H. Winer 4 Virtual Reality Applications Center, Iowa State University, Ames, IA 50010, USA Military operations are turning to more complex and advanced automation technology for minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an intended target, avoiding obstacles such as other aircrafts or enemy threats. Often automated path planning algorithms are employed to specify targets for a UAV to fly to. To date, path-planning algorithms have been limited to two-dimensional problem formulations. This paper presents a unique three-dimensional path planning problem formulation and solution approach using Particle Swarm Optimization (PSO). The problem formulation was designed to minimize risk due to enemy threats and also to minimize fuel consumption incurred by deviating from the original path. In addition, a third objective in the problem formulation takes into account reconnaissance targets. The initial design point is defined as the original path of the UAV. Using PSO, alternate paths are generated using B-spline curves, optimized based on preferences set for the three objectives. The resulting paths can be optimized with a preference towards maximum safety, minimum fuel consumption, or target reconnaissance. The problem formulation and solution implementation is described along with the results from several simulated scenarios. I. Introduction ilitary combat of the future will become highly dependent on the use of unmanned aerial vehicles (UAVs). In recent years, there has been rapid development in UAV technology such as swarm communication, command and control, and developing usable interfaces 1 . The complexity in UAV technology is rapidly growing, and according to the Department of Defense (DOD) Roadmap 2 , by the year 2012 it is estimated that F-16 size UAVs will be able to perform a complete range of combat and combat support missions. Thus, the ground control station – the human operator’s portal to the UAV – must evolve as UAVs grow in autonomy. The ground control station must facilitate the transformation of the human from pilot, to operator, to supervisor, as the level of interaction with UAVs moves to ever-higher levels. As humans interface with UAVs at more abstract levels, a UAV will be trusted to do more 3 . To develop and maintain that trust, a human must be able to understand a UAV’s situation quickly. Future ground control stations will need to provide an operator with situational awareness and quality information at a glance. To address the many research issues involved in the command and control that the DOD roadmap requires, a “Virtual Battlespace” at Iowa State University was created. In this paper, research into the issue of three- dimensional (3D) path planning for UAVs as part of the Virtual Battlespace project is presented. The method described allows a human operator to focus on selecting an appropriate path from a set of alternate paths produced by the path planner, easing the decision making process. Using a Particle Swarm Optimization (PSO) algorithm, the 1 Research Assistant, Department of Mechanical Engineering & Human Computer Interaction, Virtual Reality Applications Center, 2274 Howe Hall, Iowa State University, Ames, IA 50010, USA, Student Member. 2 Research Assistant, Department of Human Computer Interaction, Virtual Reality Applications Center, 2274 Howe Hall, Iowa State University, Ames, IA 50010, USA. 3 Professor, Department of Mechanical Engineering & Human Computer Interaction, Virtual Reality Applications Center, 2274 Howe Hall, Iowa State University, Ames, IA 50010, USA. 4 Assistant Professor, Department of Mechanical Engineering & Human Computer Interaction, Virtual Reality Applications Center, 2274 Howe Hall, Iowa State University, Ames, IA 50010, USA, Member. M 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference<br> 23 - 26 April 2007, Honolulu, Hawaii AIAA 2007-1881 Copyright © 2007 by Jung Leng Foo. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.