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

A Case based Online Trajectory Planning Method of Autonomous Unmanned Combat Aerial Vehicles with Weapon Release Constraints

As a challenging and highly complex problem, the trajectory planning for unmanned combat aerial vehicle (UCAV) focuses on optimising flight trajectory under such constraints as kinematics and complicated battlefield environment. An online case-based trajectory planning strategy is proposed in this study to achieve rapid control variables solution of UCAV flight trajectory for the of delivery airborne guided bombs. Firstly, with an analysis of the ballistic model of airborne guided bombs, the trajectory planning model of UCAVs is established with launch acceptable region (LAR) as a terminal constraint. Secondly, a case-based planning strategy is presented, which involves four cases depending on the situation of UCAVs at the current moment. Finally, the feasibility and efficiency of the proposed planning strategy is validated by numerical simulations, and the results show that the presented strategy is suitable for UCAV performing airborne guided delivery missions in dynamic environments

Game Theoretic Target Assignment Strategies in Competitive Multi-Team Systems

The task of optimally assigning military ordinance to enemy targets, often termed the Weapon Target Assignment (WTA) problem, has become a major focus of modern military thought. Current formulations of this problem consider the enemy targets as either passive or entirely defensive. As a result, the assignment problem is solved purely as a one sided team optimization problem. In practice, however, especially in environments characterized by the presence of an intelligent adversary, this one sided optimization approach has very limited use. The presence of an adversary often necessitates incorporating its intended actions in the process of solving the weapons assignment problem. In this dissertation, we formulate the weapon target assignment problem in the presence of an intelligent adversary within the framework of game theory. We consider two teams of opposing units simultaneously targeting each other and examine several possible game theoretic solutions of this problem. An issue that arises when searching for any solution is the dimensionality of the search space which quickly becomes overwhelming even for simple problems with a small number of units on each side. To solve this scalability issue, we present a novel algorithm called Unit Level Team Resource Allocation (ULTRA), which is capable of generating approximate solutions by searching within appropriate subspaces of the search space. We evaluate the performance of this algorithm on several realistic simulation scenarios. We also show that this algorithm can be effectively implemented in real-time as an automatic target assigning controller in a dynamic multi-stage problem involving two teams with large number of units in conflict

,The Impact of Human-Automation Collaboration in Decentralized Multiple Unmanned Vehicle Control

For future systems that require one or a small team of operators to supervise a network of automated agents, automated planners are critical since they are faster than humans for path planning and resource allocation in multivariate, dynamic, time-pressured environments. However, such planners can be brittle and unable to respond to emergent events. Human operators can aid such systems by bringing their knowledge-based reasoning and experience to bear. Given a decentralized task planner and a goal-based operator interface for a network of unmanned vehicles in a search, track, and neutralize mission, we demonstrate with a human-on-the-loop experiment that humans guiding these decentralized planners improved system performance by up to 50%. However, those tasks that required precise and rapid calculations were not significantly improved with human aid. Thus, there is a shared space in such complex missions for human–automation collaboration

Optimal pilot decisions and flight trajectories in air combat

The thesis concerns the analysis and synthesis of pilot decision-making and the design of optimal flight trajectories. In the synthesis framework, the methodology of influence diagrams is applied for modeling and simulating the maneuvering decision process of the pilot in one-on-one air combat. The influence diagram representations describing the maneuvering decision in a one sided optimization setting and in a game setting are constructed. The synthesis of team decision-making in a multiplayer air combat is tackled by formulating a decision theoretical information prioritization approach based on a value function and interval analysis. It gives the team optimal sequence of tactical data that is transmitted between cooperating air units for improving the situation awareness of the friendly pilots in the best possible way. In the optimal trajectory planning framework, an approach towards the interactive automated solution of deterministic aircraft trajectory optimization problems is presented. It offers design principles for a trajectory optimization software that can be operated automatically by a nonexpert user. In addition, the representation of preferences and uncertainties in trajectory optimization is considered by developing a multistage influence diagram that describes a series of the maneuvering decisions in a one-on-one air combat setting. This influence diagram representation as well as the synthesis elaborations provide seminal ways to treat uncertainties in air combat modeling. The work on influence diagrams can also be seen as the extension of the methodology to dynamically evolving decision situations involving possibly multiple actors with conflicting objectives. From the practical point of view, all the synthesis models can be utilized in decision-making systems of air combat simulators. The information prioritization approach can also be implemented in an onboard data link system.reviewe

Real-Time Navigation and Flight Path Generation for Tracking Stop-and-Go Targets with Miniature Air Vehicles

This research effort focuses on using a heuristic approach to determine the optimal flight path required to put an Unmanned Aircraft System’s (UAS) sensor on a moving target in the presence of a constant wind field. This thesis builds on past work using dynamic optimization techniques to calculate minimum time to target. The computationally intensive dynamic optimization routines in their current form take a prohibitive amount of time to calculate and ultimately result in erroneous flight path predictions due to inherent execution time latencies. Therefore an iterative, suboptimal heuristic approach was explored to mitigate excessive calculation times and ultimately yield improved flight path predictions. This report not only explores the heuristic techniques used for flight path calculation, but also includes real world application and flight test results in a Micro Air Vehicle equipped with an autopilot

Safe trajectory planning of AV

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 153-163).This thesis presents a novel framework for safe online trajectory planning of unmanned vehicles through partially unknown environments. The basic planning problem is formulated as a receding horizon optimization problem using mixed-integer linear programming (MILP) to incorporate kino-dynamic, obstacle avoidance and collision avoidance constraints. Agile vehicle dynamics are captured through a hybrid control architecture that combines several linear time-invariant modes with a discrete set of agile maneuvers. The latter are represented by affine transformations in the state space and can be described using a limited number of parameters. We specialize the approach to the case of a small-scale helicopter flying through an urban environment. Next, we introduce the concept of terminal feasible invariant sets in which a vehicle can remain for an indefinite period of time without colliding with obstacles or other vehicles. These sets are formulated as affine constraints on the last state of the planning horizon and as such are computed online. They guarantee feasibility of the receding horizon optimization at future time steps by providing an a priori known backup plan that is dynamically feasible and obstacle-free.(cont.) Vehicle safety is ensured by maintaining a feasible return trajectory at each receding horizon iteration. The feasibility and safety constraints are essential when the vehicle is maneuvering through environments that are only partially characterized and further explored online. Such a scenario was tested on an unmanned Boeing aircraft using scalable loiter circles as feasible invariant sets. The terminal feasible invariant set concept forms the basis for the construction of a provably safe distributed planning algorithm for multiple vehicles. Each vehicle then only computes its own trajectory while accounting for the latest plans and invariant sets of the other vehicles in its vicinity, i.e., of those whose reachable sets intersect with that of the planning vehicle. Conflicts are solved in real-time in a sequential fashion that maintains feasibility for all vehicles over all future receding horizon iterations. The algorithm is applied to the free flight paradigm in air traffic control and to a multi-helicopter relay network aimed at maintaining wireless line of sight communication in a cluttered environment.by Tom Schouwenaars.Ph.D

Approximate multi-agent planning in dynamic and uncertain environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, February 2012."December 2011." Cataloged from PDF version of thesis.Includes bibliographical references (p. 120-131).Teams of autonomous mobile robotic agents will play an important role in the future of robotics. Efficient coordination of these agents within large, cooperative teams is an important characteristic of any system utilizing multiple autonomous vehicles. Applications of such a cooperative technology stretch beyond multi-robot systems to include satellite formations, networked systems, traffic flow, and many others. The diversity of capabilities offered by a team, as opposed to an individual, has attracted the attention of both researchers and practitioners in part due to the associated challenges such as the combinatorial nature of joint action selection among interdependent agents. This thesis aims to address the issues of the issues of scalability and adaptability within teams of such inter-dependent agents while planning, coordinating, and learning in a decentralized environment. In doing so, the first focus is the integration of learning and adaptation algorithms into a multi-agent planning architecture to enable online adaptation of planner parameters. A second focus is the development of approximation algorithms to reduce the computational complexity of decentralized multi-agent planning methods. Such a reduction improves problem scalability and ultimately enables much larger robot teams. Finally, we are interested in implementing these algorithms in meaningful, real-world scenarios. As robots and unmanned systems continue to advance technologically, enabling a self-awareness as to their physical state of health will become critical. In this context, the architecture and algorithms developed in this thesis are implemented in both hardware and software flight experiments under a class of cooperative multi-agent systems we call persistent health management scenarios.by Joshua David Redding.Ph.D

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

Real-time Power Management of Hybrid Power Systems in All Electric Ship Applications.

Motivated by the need for achieving flexible shipboard arrangement and meeting future on-board power demand, the concept of all-electric ships (AES) has been pursued. The integrated power systems enable this initiative by providing a common electrical platform for the propulsion and ship-service loads and are a classic example of hybrid power systems (HPS). In order to leverage the complementary dynamic characteristics of the diverse sources, effective power management (PM) is essential to coordinate the sources and energy storage to achieve efficient power generation and fast load following. Although extensive research has been done on the PM of hybrid land vehicles for commercial applications, this problem for shipboard military applications remains largely unaddressed, leading to its exclusive focus in this dissertation. While HPS brings in many opportunities for power management, there are many associated challenges for systems used in military applications since both performance as well as survivability criteria have to be satisfied. While the on-demand goal for the power management problem makes real-time control a key requirement, leveraging the look-ahead opportunities for the shipboard missions makes it difficult to attain this goal. Furthermore, the nonlinearity and the complexity of hybrid power systems, make the optimal control of HPS challenging. In this dissertation, we address real-time power management for the AES and general hybrid power systems targeting military applications. The central theme of this work is the development of power management schemes with real-time computational efficiency by exploring HPS dynamic properties, for improved performance (namely fuel economy and fast load following) during normal mode conditions as well as increased survivability during component failure. A reduced order dynamic HPS model and a scaled test bed is developed as a numerical tool for controller design and validation. The power management (PM) schemes for both normal as well as failure mode conditions are proposed and implemented on a real-time simulator which demonstrated the real-time performance of the proposed method. While the normal mode PM leverages the complementary dynamic characteristics of the HPS for real-time look-ahead control and performance, the failure mode PM uses a reference governor approach for real-time constraint enforcement.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77863/1/gseenuma_1.pd