Collaborative UAV Surveillance

Autonomous collaborative robotics is a topic of significant interest to groups such as the Air Force Research Lab (AFRL) and the National Aeronautics and Space Administration (NASA). These two groups have been developing systems for the operation of autonomous vehicles over the past several years, but each system has several critical drawbacks. AFRL’s Unmanned Systems Autonomy Services (UxAS) supports pathfinding for multiple tasks performed by groups of vehicles, but has no formal verification, very little physical flight time, and no concept of collision avoidance. NASA’s Independent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS) has collision avoidance, partial formal verification, and thousands of hours of physical flight time, but has no concept of collaboration. AFRL and NASA each wanted to incorporate the features of the other’s software into their own, and so the CRoss-Application Translator for Operational Unmanned Systems (CRATOUS) was created. CRATOUS creates a communication bridge between UxAS and ICAROUS, allowing for full feature integration of the two system. This combined software is the first system that allows for the safe and reliable cooperation of groups of unmanned vehicles

Task assignment algorithms for teams of UAVs in dynamic environments

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M.)--Massachusetts Institute of Technology, Operations Research Center, 2004.Includes bibliographical references (p. 113-118).For many vehicles, obstacles, and targets, coordination of a fleet of Unmanned Aerial Vehicles (UAVs) is a very complicated optimization problem, and the computation time typically increases very rapidly with the problem size. Previous research proposed an approach to decompose this large problem into task assignment and trajectory problems, while capturing key features of the coupling between them. This enabled the control architecture to solve an assignment problem first to determine a sequence of waypoints for each vehicle to visit, and then concentrate on designing paths to visit these pre-assigned waypoints. Although this approach greatly simplifies the problem, the task assignment optimization was still too slow for real-time UAV operations. This thesis presents a new approach to the task assignment problem that is much better suited for replanning in a dynamic battlefield. The approach, called the Receding Horizon Task Assignment (RHTA) algorithm, is shown to achieve near-optimal performance with computational times that are feasible for real-time implementation. Further, this thesis extends the RHTA algorithm to account for the risk, noise, and uncertainty typically associated with the UAV environment. This work also provides new insights on the distinction between UAV coordination and cooperation. The benefits of these improvements to the UAV task assignment algorithms are demonstrated in several simulations and on two hardware platforms.by Mehdi Alighanbari.S.M

Distributed coordination and control experiments on a multi-UAV testbed

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.Includes bibliographical references (p. 153-157).(cont.) of environmental disturbances and measurement noise.The product of this thesis is a robust planning system that is tolerant of the types of uncertainty experienced by real aircraft. This robustness has been demonstrated by more than 20 successful flights on a fully automated UAV testbed.This thesis presents the development and testing of a unique testbed consisting of a fleet of eight unmanned aerial vehicles (UAVs) that was designed as a platform for evaluating coordination and control algorithms. A hierarchical configuration of task assignment, trajectory design, and low-level, waypoint following, are used in a receding horizon framework to control the UAV system. Future UAV teams will have to autonomously demonstrate cooperative behaviors in dynamic and uncertain environments, and this testbed can be used to compare various control approaches to accomplish these coordinated missions. Flight demonstrations are made utilizing real-time mixed-integer linear programming techniques, exercising the algorithms in realistic environments with real-world disturbances. Large disturbance sources, computational delay and measurementnoise all represent significant error sources that reduce the ability of UAV teams to interact in a coordinated fashion by increasing uncertainty on higher planning levels. This thesis develops a method that explicitly accounts for this uncertainty by including feedback loops on the task assignment and trajectory design algorithms to prescribe added robustness for the uncertainty at each stage. This approach takes into account low level controller saturation limits that might cause infeasibilities in the plans created at the higher levels of the planning system. Detailed and realistic simulation environments are useful for large-scale multi-vehicle simulations, particularly when logistics prevent flight testing on that scale. This thesis validates one such hardware-in-the-loop simulation environment through the comparison of models obtained from experimentally collected flight data and detailed modelingby Ellis T. King.S.M

Coordinated rendezvous and surveillance for multiple unmanned aerial vehicles (UAVs) subject to actuator and sensor faults

In this thesis, the problem of employing multiple UAVs for carrying out a Coordinated Strike and a Multiple UAV Surveillance mission has been addressed. The goal of the Coordinated Strike mission is for multiple UAVs to cooperate in order to simultaneously arrive at a high priority target to carry out a coordinated strike. The coordination strategy is based on coordination variables and coordination functions. A distributed system architecture is proposed that allows vehicles to communicate coordinating information across the team without reliance on a central ground controller. Simulations have been conducted to illustrate the performance of the coordination strategy under an actuator fault in single and multiple vehicles. The Multiple UAV Surveillance problem has been investigated by developing a hypothetical Border Surveillance Mission, wherein a UAV team is tasked to monitor a region along a border between two countries. The goal of the UAVs is to cover the entire surveillance region, while minimizing the team cost, which is a function of each vehicle's fuel consumption and mission time. Three fault cases in a single vehicle in the team have been simulated, namely (1) actuator; (2) sensor; and (3) simultaneous actuator and sensor faults. These faults necessitate a resource allocation problem to be solved, which is used to determine the configuration of the team engaged in the surveillance mission. The team chosen to perform the surveillance mission is the one that incurs the minimum cost for performing the mission

Collision-free Multiple Unmanned Combat Aerial Vehicles Cooperative Trajectory Planning for Time-critical Missions using Differential Flatness Approach

This paper investigates the cooperative trajectory planning for multiple unmanned combat aerial vehicles in performing autonomous cooperative air-to-ground target attack missions. Firstly, the collision-free cooperative trajectory planning problem for time-critical missions is formulated as a cooperative trajectory optimal control problem (CTP-OCP), which is based on an approximate allowable attack region model, several constraints model, and a multi-criteria objective function. Next, a planning algorithm based on the differential flatness, B-spline curves and nonlinear programming is designed to solve the CTP-OCP. In particular, the notion of the virtual time is introduced to deal with the temporal constraints. Finally, the proposed approach is validated by two typical scenarios and the simulation results show the feasibility and effectiveness of the proposed planning approach.Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.299

Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

Decentralized control for UAV path planning and task allocation

The effort of this research is to move toward enabling Unmanned Air Vehicles to fly in autonomous formations with intelligent mission planning capabilities. In particular, UAVs will be able to autonomously perform path planning and task allocation. During missions, the UAVs must be able to avoid threats and no-fly zones while still reaching their target optimally in time.;A path planning and task allocation approach was first developed that treats the problem as a Multi-dimensional, Multiple-Choice Knapsack Problem. Paths are selected and task assigned while minimizing the UAV team\u27s overall mission cost. Next, a SIMULINK-based centralized simulation environment was created. This simulation uses the path planning and task allocation scheme previously developed, and adds time-varying, dynamic environment aspects. The latter part of the research effort was focused on development of a decentralized simulation environment. This decentralized version includes a vehicle\u27s own decision making capabilities and communication amongst a team of vehicles. (Abstract shortened by UMI.)

Using Multiattribute Utility Copulas in Support of UAV Search and Destroy Operations

The multiattribute utility copula is an emerging form of utility function used by decision analysts to study decisions with dependent attributes. Failure to properly address attribute dependence may cause errors in selecting the optimal policy. This research examines two scenarios of interest to the modern warfighter. The first scenario employs a utility copula to determine the type, quantity, and altitude of UAVs to be sent to strike a stationary target. The second scenario employs a utility copula to examine the impact of attribute dependence on the optimal routing of UAVs in a contested operational environment when performing a search and destroy mission against a Markovian target. Routing decisions involve a tradeoff between risk of UAV exposure to the enemy and the ability to strike the target. This research informs decision makers and analysts with respect to the tactics, techniques, and procedures employed in UAV search and destroy missions. An ever increasing UAV operations tempo suggests such research becoming increasingly relevant to the warfighter

Information-rich Task Allocation and Motion Planning for Heterogeneous Sensor Platforms

This paper introduces a novel stratified planning algorithm for teams of heterogeneous mobile sensors that maximizes information collection while minimizing resource costs. The main contribution of this work is the scalable unification of effective algorithms for de- centralized informative motion planning and decentralized high-level task allocation. We present the Information-rich Rapidly-exploring Random Tree (IRRT) algorithm, which is amenable to very general and realistic mobile sensor constraint characterizations, as well as review the Consensus-Based Bundle Algorithm (CBBA), offering several enhancements to the existing algorithms to embed information collection at each phase of the planning process. The proposed framework is validated with simulation results for networks of mobile sensors performing multi-target localization missions.United States. Air Force. Office of Scientific Research (Grant FA9550-08-1-0086)United States. Air Force. Office of Scientific Research. Multidisciplinary University Research Initiative (FA9550-08-1-0356