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.

This paper focuses on the problem of cooperatively searching, using a team of unmanned air vehicles (UAVs), an area of interest that contains regions of opportunity and regions of potential hazard. The objective of the UAV team is to visit as many opportunities as possible, while avoiding as many hazards as possible. To enable cooperation, the UAVs are constrained to stay within communication range of one another. Collision avoidance is also required. Algorithms for teamoptimal and individually-optimal/team-suboptimal solutions are developed and their computational complexity compared. Simulation results demonstrating the feasibility of the cooperative search algorithms are presented. 1

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.

Cooperation in multiple vehicle teams requires that information be shared between team members. If shared information is not synchronized across the team, then cooperation is adversely affected. This paper considers the problem of information synchronization in multiple agent teams. We define notions of asymptotic synchronizability and show that a team of agents is asymptotically synchronizable if and only if the associated communication topology admits a spanning tree. A linear synchronization strategy is proposed and demonstrated via several simulation examples. 1

A trajectory generation strategy that facilitates the coordination of multiple unmanned air vehicles is developed. Of particular interest is the planning of threat-avoiding trajectories that result in the simultaneous arrival of multiple UAVs at their targets. In this approach, paths to the target are modeled using the physical analogy of a chain. A unique strength of the planning approach is the ability to specify or alter the pathlength by adding or subtracting links from the chain. Desirable paths to the target are obtained by simulating the dynamics of the chain where threats apply repulsive forces to the chain and forces internal to the chain tend to straighten it out. The result for multiple vehicles and targets is a set of smooth and flyable paths of equal length that reduces exposure to threats. 1 INTRODUCTION In the future operation of unmanned air vehicles, the ability to coordinate the timing of activities of UAVs in a multiple vehicle system will be vital to many missions...

Abstract — This paper is about the allocation of tours of m targets to n vehicles. The motion of the vehicles satisfies a non-holonomic constraint, i.e., the yaw rate of the vehicle is bounded. Each target is to be visited by one and only one vehicle. Given a set of targets and the yaw rate constraints on the vehicles, the problem addressed in this paper is • to assign each vehicle, a sequence of targets to visit, and • to find a feasible path for each vehicle that passes through the assigned targets with a requirement that the vehicle returns to its initial position. The heading angle at each target location may not be specified. The objective function is to minimize the sum of the distances travelled by all the vehicles. A constant factor approximation algorithm is presented for the above resource allocation problem for both the single and the multiple vehicle

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.

In this paper we present a dynamical systems representation for multi-agent rendezvous on the phase plane. We restrict our attention to two agents, each with scalar dynamics. The problem of rendezvous is cast as a stabilization problem, with a set of constraints on the trajectories of the agents, defined on the phase plane. We also describe a method to generate control Lyapunov functions that when used in conjunction with a stabilizing control law, such as Sontag's formula, makes sure that the two-agent system attains rendezvous. The main result of this paper is a Lyapunov-like certificate theorem that describes a set of constraints, which when satisfied are sufficient to guarantee rendezvous.

Abstract—This paper considers a heterogeneous team of cooperating unmanned aerial vehicles (UAVs) drawn from several distinct classes and engaged in a search and action mission over a spatially extended battlefield with targets of several types. During the mission, the UAVs seek to confirm and verifiably destroy suspected targets and discover, confirm, and verifiably destroy unknown targets. The locations of some (or all) targets are unknown apriori, requiring them to be located using cooperative search. In addition, the tasks to be performed at each target location by the team of cooperative UAVs need to be coordinated. The tasks must, therefore, be allocated to UAVs in real time as they arise, while ensuring that appropriate vehicles are assigned to each task. Each class of UAVs has its own sensing and attack capabilities, so the need for appropriate assignment is paramount. In this paper, an extensive dynamic model that captures the stochastic nature of the cooperative search and task assignment problems is developed, and algorithms for achieving a high level of performance are designed. The paper focuses on investigating the value of predictive task assignment as a function of the number of unknown targets and number of UAVs. In particular, it is shown that there is a tradeoff between search and task response in the context of prediction. Based on the results, a hybrid algorithm for switching the use of prediction is proposed, which balances the search and task response. The performance of the proposed algorithms is evaluated through Monte Carlo simulations. Index Terms—Cooperative search, path planning, task allocation, unmanned aerial vehicle. I.