This paper presents an end-to-end solution to the battlefield scenario where M unmanned air vehicles are assigned to strike N known targets, in the presence of dynamic threats. The problem is decomposed into the subproblems of (1) cooperative target assignment, (2) coordinated UAV intercept, (3) path planning, and (4) feasible trajectory generation. The design technique is based on a hierarchical approach to coordinated control. Detailed simulation results are presented.

Autonomous unmanned air vehicle flight control systems require robust path generation to account for terrain obstructions, weather, and moving threats such as radar, jammers, and unfriendly aircraft. In this paper, we outline a feasible, hierarchal approach for real-time motion planning of small autonomous fixed-wing UAVs. The approach divides the trajectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation. The waypoint path planner determines the vehicle 's route without regard for the dynamic constraints of the vehicle. This results in a significant reduction in the path search space, enabling the generation of complicated paths that account for pop-up and dynamically moving threats. Kinematic constraints are satisfied using a trajectory smoother which has the same kinematic structure as the physical vehicle. The third step of the approach uses a novel tracking algorithm to generate a feasible state trajectory that can be followed by a standard autopilot. Monte-Carlo simulations were done to analyze the performance and feasibility of the approach and determine real-time computation requirements. A planar version of the algorithm has also been implemented and tested in a low-cost micro-controller. The paper describes a custom UAV built to test the algorithms.

This paper presents a solution strategy for achieving cooperative timing among teams of vehicles. Based on the notion of coordination variables and coordination functions, the strategy facilitates cooperative timing by requiring acceptably low levels of communication and computation. The application of the coordination variable/function approach to trajectory planning problems for teams of unmanned air vehicles with timing constraints is described. Three types of timing constraints are considered: simultaneous arrival, tight sequenc- ing, and loose sequencing. Simulation results demonstrating the viability of the approach are presented.

Abstract—This brief presents a real-time, feasible trajectory generation algorithm for unmanned air vehicles (UAVs) flying through a sequence of waypoints. The algorithm produces extremal trajectories that transition between straight-line path segments in a time-optimal fashion. In addition, the algorithm can be configured so that the dynamically feasible trajectory has the same path length as the straight-line waypoint path. Implementation issues associated with the algorithm are described in detail. Simulation studies show the effectiveness of the proposed method. Index Terms—Autonomous systems, optimal control, path planning, trajectory generation, unmanned air vehicles (UAVs). I.

Recent advances in many multi-discipline technologies have allowed small, low-cost fixed wing unmanned air vehicles (UAV) or more complicated unmanned ground vehicles (UGV) to be a feasible solution in many scientific, civil and military applications. Cameras can be mounted on-board of the unmanned vehicles for the purpose of scientific data gathering, surveillance for law enforcement and homeland security, as well as to provide visual information to detect and avoid imminent collisions for autonomous navigation. However, most current computer vision algorithms are highly complex computationally and usually constitute the bottleneck of the guidance and control loop. In this paper, we present a novel computer vision algorithm for collision detection and time-to-impact calculation based on feature density distribution (FDD) analysis. It does not require accurate feature extraction, tracking, or estimation of focus of expansion (FOE). Under a few reasonable assumptions, by calculating the expansion rate of the FDD in space, time-to-impact can be accurately estimated. A sequence of monocular images is studied, and different features are used simultaneously in FDD analysis to show that our algorithm can achieve a fairly good accuracy in collision detection. In this paper we also discuss reactive path planning and trajectory generation techniques that can be accomplished without violating the velocity and heading rate constraints of the UAV.

Unmanned aerial vehicles (UAVs) are playing increasingly prominent roles in defense programs and strategy around the world. Technology advancements have enabled the development of large UAVs (e.g., Global Hawk, Predator) and the creation of smaller, increasingly capable UAVs. The focus of this article is on smaller fixedwing miniature aerial vehicles (MAVs), which range in size from.25–2 m in wingspan. As recent conflicts have demonstrated, there are numerous military applications for MAVs, including reconnaissance, surveillance, battle damage assessment, and communications relays. Civil and commercial applications are not as well developed, although potential applications are extremely broad in scope. Possible applications for MAV technology include environmental monitoring (e.g., pollution, weather, and scientific applications), forest fire monitoring, homeland security, border patrol, drug interdiction, aerial surveillance and mapping, traffic monitoring, precision agriculture, disaster relief, ad hoc communications networks, and rural search and rescue. For many of these applications to develop to maturity, the reliability of MAVs will need to increase, their capabilities will need to be extended further, their ease of use will need to be improved, and their cost will have to come down. In addition to these technical and economic challenges, the regulatory challenge of integrating UAVs into the national and international airspace must be overcome. Critical to the more widespread use of MAVs is making them easy to use by nonpilots, such as scientists, forest firefighters, law enforcement officers, or military ground troops. One key capability for facilitating ease of use is the ability to sense and avoid obstacles, both natural and man made. Many of the applications cited require MAVs to fly at low altitudes in close proximity to structures or terrain. For example, the ability to fly through city canyons and around high-rise buildings is envisioned for future home-