Autonomous aircraft initiative study

The results of a consulting effort to aid NASA Ames-Dryden in defining a new initiative in aircraft automation are described. The initiative described is a multi-year, multi-center technology development and flight demonstration program. The initiative features the further development of technologies in aircraft automation already being pursued at multiple NASA centers and Department of Defense (DoD) research and Development (R and D) facilities. The proposed initiative involves the development of technologies in intelligent systems, guidance, control, software development, airborne computing, navigation, communications, sensors, unmanned vehicles, and air traffic control. It involves the integration and implementation of these technologies to the extent necessary to conduct selected and incremental flight demonstrations

Modelling and Verification of Multiple UAV Mission Using SMV

Model checking has been used to verify the correctness of digital circuits, security protocols, communication protocols, as they can be modelled by means of finite state transition model. However, modelling the behaviour of hybrid systems like UAVs in a Kripke model is challenging. This work is aimed at capturing the behaviour of an UAV performing cooperative search mission into a Kripke model, so as to verify it against the temporal properties expressed in Computation Tree Logic (CTL). SMV model checker is used for the purpose of model checking

Task-driven multi-formation control for coordinated UAV/UGV ISR missions

The report describes the development of a theoretical framework for coordination and control of combined teams of UAVs and UGVs for coordinated ISR missions. We consider the mission as a composition of an ordered sequence of subtasks, each to be performed by a different team. We design continuous cooperative controllers that enable each team to perform a given subtask and we develop a discrete strategy for interleaving the action of teams on different subtasks. The overall multi-agent coordination architecture is captured by a hybrid automaton, stability is studied using Lyapunov tools, and performance is evaluated through numerical simulations

The Effect of Pilot and Air Traffic Control Experiences & Automation Management Strategies on UAS Mission Task Performance

Unmanned aircraft are relied on now more than ever to save lives and support the troops in the recent Operation Enduring Freedom and Operation Iraqi Freedom. The demands for UAS capabilities are rapidly increasing in the civilian sector. However, UAS operations will not be carried out in the NAS until safety concerns are alleviated. Among these concerns is determining the appropriate level of automation in conjunction with a suitable pilot who exhibits the necessary knowledge, skills, and abilities to safely operate these systems. This research examined two levels of automation: Management by Consent (MBC) and Management by Exception (MBE). User experiences were also analyzed in conjunction with both levels of automation while operating an unmanned aircraft simulator. The user experiences encompass three individual groups: Pilots, ATC, and Human Factors. Performance, workload, and situation awareness data were examined, but did not show any significant differences among the groups. Shortfalls and constraints are heavily examined to help pave the wave for future research

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

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

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

Asymptotic constant-factor approximation algorithm for the Traveling Salesperson Problem for Dubins' vehicle

This article proposes the first known algorithm that achieves a constant-factor approximation of the minimum length tour for a Dubins' vehicle through $n$ points on the plane. By Dubins' vehicle, we mean a vehicle constrained to move at constant speed along paths with bounded curvature without reversing direction. For this version of the classic Traveling Salesperson Problem, our algorithm closes the gap between previously established lower and upper bounds; the achievable performance is of order $n^{2/3}$