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Goal model analysis of autonomy requirements for Unmanned Aircraft Systems

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Abstract

Designing Unmanned Aircraft Systems (UASs) for optimal autonomy while meeting user requirements is quite challenging. Researchers have focused on improving autonomy algorithms and verification methods to ensure safe and reliable autonomous behavior in UASs, but little research has been conducted on requirements engineering for UASs to answer design questions and explore the trade space for using autonomy to satisfy user requirements. This paper introduces a method to determine an optimal set of autonomous capabilities that satisfies UAS user requirements in the early stages of conceptual design. The method uses a modified Autonomy Requirements Engineering (ARE) process that applies quantitative measures and statistical analysis to Goal-Oriented Requirements Engineering (GORE). We demonstrate this method in a case study of a “disaster robot,” i.e., a hazard response UAS for which the autonomy requirements were optimized using a goal model developed in the Goal-oriented Requirement Language (GRL), as implemented in the modeling tool jUCMNav. The high-level goals of the hazard response UAS—system performance, cost, and safety—were evaluated using the formula-based GRL strategy evaluation algorithm resident in jUCMNav version 6.0. An autonomy trade space study was conducted through a Design and Analysis of Simulation Experiments (DASE). Our designed simulation experiment inserted the number of trials (evaluation strategies) and inputs into the goal model, and evaluation data were analyzed to optimize design factors based on user weightings of the response variables. This paper presents a structured method of ARE for UASs, which could be adopted more broadly across other domains, demonstrating how to optimize autonomous capabilities for different design conditions.

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Notes

  1. Autonomicity (or self-*objectives) are the essential characteristics of autonomous systems. The self-*objectives describe the autonomous behavior of the system [7].

  2. Kleijnen defines a metamodel as “an approximation of the input/output function defined by the underlying simulation model,” which may be used to examine trends and determine influences across the design factor space [11].

  3. Deliberate use of the biological term autonomic is to imply similar performance in AI systems as their human counterparts by having the management aspects of such systems always running in the background to ensure the system’s health and goals are maintained and optimized for the operating environment.

  4. In GRL, dependency links do not contribute to satisfaction of dependent elements; instead, they establish a limit on the satisfaction level to the lowest level of all dependencies.

  5. For a complete description of regression models and developing metamodels, refer to [11, 44, 45].

  6. The trade space is the space of possible design options given a set of design variables. The trade space can be evaluated in terms of benefits and costs to decision makers [41].

  7. Lin and Goodrich [18] tested a sliding autonomy management approach for UAS path planning conducting Wilderness Search and Rescue. The authors evaluated an autonomy management approach that allowed human operators to interact with the behavior of the autonomous system along two dimensions, spatial and temporal constraints. Their approach gave the operators the ability to allocate the degrees of authority and flexibility to the UAS’s path planning algorithms by adding and removing spatial and temporal constraints (referred to as sliding autonomy). They demonstrated that performance of human–autonomy collaboration outperformed either humans or autonomy working alone.

  8. Tomic et al. [12] implemented a visual odometry algorithm using two EO sensors for stereo image processing. Based on this 3D information, delta position and orientation along with corresponding measurement covariance were calculated for state vectors that included position, velocity, and altitude. This autopilot approach does not rely on GPS as a source of information, which is especially useful in areas where GPS is degraded or denied such as the disaster scenario presented in Sect. 4.

  9. We refer the reader to review the abundant literature on how to use GORE to create goal models in GRL [34, 39,40,41, 49,50,51].

Abbreviations

3D:

Three dimensional

AAR:

Autonomy-assistive requirement

AI:

Artificial intelligence

AMA:

Auto-mission-agent

ANA:

Auto-navigation-agent

ARE:

Autonomy Requirements Engineering

ARTUE:

Autonomous Response to Unexpected Events

AS:

Autonomic system

ASCENS:

Autonomic Service-Component Ensembles

AVA:

Auto-vehicle-agent

AViA:

Auto-vision-agent

CONOPS:

Concept of operations

DASE:

Design and Analysis of Simulation Experiments

DDNS:

Dynamic Decision Networks

DoD:

Department of Defense

DoE:

Design of Experiments

DSB:

Defense Science Board

EISI:

Emergency Informatics Summer Institute

EO:

Electro-optical

GAR:

Generic autonomy requirements

GDA:

Goal-driven autonomy

GORE:

Goal-Oriented Requirements Engineering

GPS:

Global Positioning System

GRL:

Goal-oriented Requirement Language

HRA:

Hazard Response Agency

IAEA:

International Atomic Energy Agency

IC:

Incident Commander

IEEE:

Institute of Electrical and Electronics Engineers

IR:

Infrared

ITU:

International Telecommunication Union

KPI:

Key Performance Indicator

KR&R:

Knowledge representation and reasoning

M1:

Mission 1

M2:

Mission 2

MS:

Mission Specialist

O&S:

Operations and sustainment

OCD:

Operational Concept Document

OpCon:

Operational Concept

PGRB:

Protective Gear Replaceable Battery

SA:

Situational awareness

SAR:

Search and rescue

SAS:

Self-adaptive systems

SNR:

Signal-to-noise ratio

SO:

Safety observer

SysML:

Systems Modeling Language

UAS:

Unmanned Aircraft System

UAV:

Unmanned Aerial Vehicle

UCM:

Use Case Maps

UML:

Unified Modeling Language

URN:

User Requirements Notation

XOR:

Exclusive OR

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Authors and Affiliations

  1. Johns Hopkins Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA

    Kerry Neace

  2. Engineering Management and Systems Engineering Department, The George Washington University, 2121 I St NW, Washington, DC, 20052, USA

    Kerry Neace, Robert Roncace & Pavel Fomin

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  1. Kerry Neace
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Neace, K., Roncace, R. & Fomin, P. Goal model analysis of autonomy requirements for Unmanned Aircraft Systems. Requirements Eng 23, 509–555 (2018). https://doi.org/10.1007/s00766-017-0278-6

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