MISSION ENGINEERING METHODOLOGY FOR REALIZATION OF UNMANNED SURFACE VESSEL OPERATIONS

The Navy has included unmanned systems as a key enabler for the future fleet. Congress has mandated that the Navy (PMS 406) provide demonstrated testing and documentation sufficient to support transition of Unmanned Surface Vessels (USVs) from prototype to operational. Commercial USV certification examples only address safety of navigation and do not provide certification requirements for autonomy, nor do they consider the operational mission context or requirements for the USVs. No current methodology exists that decomposes the certification metrics and standards, including the complexity of the intended USV missions. Mission engineering (ME) provides the systems engineering rigor and methodology to ensure that the USV prototypes are evaluated in their intended missions. The mission objectives were captured in Design Reference Missions (DRMs). The DRMs provided the operational sequence of events for the USVs to accomplish their mission in support of commander's intent. The DRMs decomposed into mission essential tasks (METs). The METs were mapped to the critical systems performing the METs. This methodology can be further analyzed to produce the complete complement of certification requirements for PMS 406. Analysis revealed several gaps. The communications systems and the human-in-the-loop interaction with the USVs need to be reevaluated based upon the mission analysis.Civilian, Department of the NavyApproved for public release. Distribution is unlimited

Modeling and Simulation of Elementary Robot Behaviors using Associative Memories

International audienceToday, there are several drawbacks that impede the necessary and much needed use of robot learning techniques in real applications. First, the time needed to achieve the synthesis of any behavior is prohibitive. Second, the robot behavior during the learning phase is – by definition – bad, it may even be dangerous. Third, except within the lazy learning approach, a new behavior implies a new learning phase. We propose in this paper to use associative memories (self-organizing maps) to encode the non explicit model of the robot-world interaction sampled by the lazy memory, and then generate a robot behavior by means of situations to be achieved, i.e., points on the self-organizing maps. Any behavior can instantaneously be synthesized by the definition of a goal situation. Its performance will be minimal (not necessarily bad) and will improve by the mere repetition of the behavior

Prohibited Volume Avoidance for Aircraft

This thesis describes the development of a pilot override control system that prevents aircraft entering critical regions of space, known as prohibited volumes. The aim is to prevent another 9/11 style terrorist attack, as well as act as a general safety system for transport aircraft. The thesis presents the design and implementation of three core modules in the system; the trajectory generation algorithm, the trigger mechanism for the pilot override and the trajectory following element. The trajectory generation algorithm uses a direct multiple shooting strategy to provide trajectories through online computation that avoid pre-defi ned prohibited volume exclusion regions, whilst accounting for the manoeuvring capabilities of the aircraft. The trigger mechanism incorporates the logic that decides the time at which it is suitable for the override to be activated, an important consideration for ensuring that the system is not overly restrictive for a pilot. A number of methods are introduced, and for safety purposes a composite trigger that incorporates di fferent strategies is recommended. Trajectory following is best achieved via a nonlinear guidance law. The guidance logic sends commands in pitch, roll and yaw to the control surfaces of the aircraft, in order to closely follow the generated avoidance trajectory. Testing and validation is performed using a full motion simulator, with volunteers flying a representative aircraft model and attempting to penetrate prohibited volumes. The proof-of-concept system is shown to work well, provided that extreme aircraft manoeuvres are prevented near the exclusion regions. These hard manoeuvring envelope constraints allow the trajectory following controllers to follow avoidance trajectories accurately from an initial state within the bounding set. In order to move the project closer to a commercial product, operator and regulator input is necessary, particularly due to the radical nature of the pilot override system

From the Analysis of Argumentation to the Generation of Typologies: A Model of Qualitative Data Analysis

In this paper we present a model of qualitative data analysis developed through an example from an empirical study that focused on analyzing the causes of why people obey or disobey traffic rules. Specifically, we focus on the study of the arguments that people use to justify their behaviors regarding such rules. The study was developed from in-depth interviews with men and women between the ages of 18 and60, who drive cars or motorcycles. The model is organized into three stages that are applied to the empirical study. In the first stage we form the research question and objectives. In the second stage argumentative statements are studied for later access to the systems of beliefs that support those statements and to the mental models which form the basis of the systems of beliefs. Finally, we build typologies of individuals based on how arguments, systems of beliefs, and mental models are combined in such individuals

COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning

Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of Deep Reinforcement Learning (DRL) in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization (PPO), a DRL algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an underactuated Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters