A framework for predicting the mission-specific performance of autonomous unmanned systems

Durst, Phillip J.; Gray, W J; Ņikitenko, Agris; Caetano, J. V.; Trentini, Michael; King, Roger L.

doi:10.1109/iros.2014.6942823

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…The assessment of autonomy level is an important aspect to compare and evaluate the performance of the system presented in this paper. Several models have been proposed to assess the autonomy level of the systems, including autonomy level evaluation [38] formulated by the National Aeronautics and Space Administration (NASA), the Autonomous Control Levels (ACL) method [39] proposed by the U.S. military, and the Autonomy Levels for Unmanned Systems (ALFUS) framework [40] by the National Institute of Standards and Technology (NIST)-each model has its drawbacks [41].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Hinas

Ragel

Roberts

et al. 2020

Sensors

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Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

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Section: Related Workmentioning

confidence: 99%

A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Hinas

Ragel

Roberts

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Autonomous ground vehicle companies resort to driving millions of miles to perform validation tests, which is generally economically impractical [13][14][15]. Some strategies focus on autonomy level opposed to mission performance such as in the Autonomy Levels for Unmanned Systems (ALFUS) framework [10,16] and the US Army Mission Performance Potential (MPP) framework [17]. These techniques predict expected performance for a mission set and level of autonomy but it does not directly compare the performances of varying types of autonomy algorithms, nor does it provide a direct measure of vehicle performance for individual iterations of a given mission scenario.…”

Section: Introductionmentioning

confidence: 99%

PERFORM: A Metric for Evaluating Autonomous System Performance in Marine Testbed Environments Using Interval Type-2 Fuzzy Logic

2021

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Trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The authors introduce the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e., providing direct comparisons and quantitative evaluations between autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g., for surveying, surveillance, search and rescue). Three case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission.

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“…Within the last twenty years, the development of mobile robotics and autonomous intelligent systems has experienced a rapid growth and becoming a part of our way of life, ranging from factory transport systems [1], airport transport systems, road/vehicular systems, to military applications [2], automated patrol systems [3], homeland security surveillance [4], rescue operations [5], and ambient assistants for elderly or disabled people [6].…”

Section: Introductionmentioning

confidence: 99%

Extendable behavioral cognitive architecture based on dynamic field theory

Nikkhoo

Menhaj

2016

2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA)

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In order to support the computational modeling and simulation of socially-distributed cognitive processes, a new behavioral based cognitive architecture using Dynamic field theory for each individual agent is proposed. This cognitive architecture is applied to construct an agent based simulation model to examine the impact of different swarming variables on different social scenarios that the swarm agents may conduct. This simulation model provides a flexible framework based on this cognitive architecture to study social phenomena and emergent behaviors. In the end, simulation results over some real-world case studies are provided, which can easily approve the capability of the proposed approach.

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A framework for predicting the mission-specific performance of autonomous unmanned systems

Cited by 8 publications

References 6 publications

A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

PERFORM: A Metric for Evaluating Autonomous System Performance in Marine Testbed Environments Using Interval Type-2 Fuzzy Logic

Extendable behavioral cognitive architecture based on dynamic field theory

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