Shared Control Between Pilots and Autopilots: An Illustration of a Cyberphysical Human System

Eraslan, Emre; Yıldız, Yıldıray; Annaswamy, Anuradha M.

doi:10.1109/mcs.2020.3019721

Cited by 15 publications

(5 citation statements)

References 36 publications

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“…Regarding the function (or task) allocation problem, several CPHS studies have modeled human behaviors and cognitive states to assist humans. For example, shared control approaches between pilots and autopilots have been explored to enhance safety by allocating functions based on the timeline of off-nominal conditions [20]. In other shared control approaches, cyber-physical systems can assist humans by partially or completely taking on control functions based on observed human behaviors and inferred cognitive states [21], [22].…”

Section: A Related Work 1) Computational Modelingmentioning

confidence: 99%

A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario

Byeon,

Choi,

Hwang

2024

IEEE Open J. Control. Syst.

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This paper proposes a quantitative framework for optimally allocating task functions in human-autonomy teaming (HAT). HAT involves cooperation between humans and autonomous agents to achieve common goals. As humans and autonomous agents possess different capabilities, function allocation plays a crucial role in ensuring effective HAT. However, designing the best adaptive function allocation remains a challenge, as existing methods often rely on qualitative rules and intensive humansubject studies. To address this limitation, we propose a computational function allocation approach that leverages cognitive engineering, computational work models, and optimization techniques. The proposed optimal adaptive function allocation method is composed of three main elements: 1) analyze the teamwork to identify a set of all possible function allocations within a team construction, 2) numerically simulate the teamwork in temporal semantics to explore the interaction of the team with complex environments using the identified function allocations in a trial-and-error manner, and 3) optimize the adaptive function allocation with respect to a given situation such as physical conditions, available information resources, and human mental workload. For the optimization, we utilize performance metrics such as task performance, human mental workload, and coherency in function allocations. To illustrate the effectiveness of the proposed framework, we present a simulated HAT scenario involving a human work model and drone fleet for last-mile delivery in disaster relief operations.

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Section: A Related Work 1) Computational Modelingmentioning

confidence: 99%

A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario

Byeon,

Choi,

Hwang

2024

IEEE Open J. Control. Syst.

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“…Researchers have also considered human as supervisor and autonomous system as assistant, e.g., [11,12,13,14,15,16,17,18], In [11], a theoretic controller is formulated for shared control of a quadruped rescue robot that composes human inputs with autonomous controls to guide human controlled leg positions. In a subsequent work [12], the authors adopt a "prediction-human" feedback loop by applying a dual-mode MPC technique and search-and-rescue inspired human operator trial.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To keep the safety of the vehicles, the authors [14] propose to model the driver from empirical observations and incorporate the model into a control framework to correct the driver's input. In [15], the authors propose a model based on supervisor authority over autonomous flight controller during anomaly and validate the model with human-in-the loop simulations.…”

Section: Literature Reviewmentioning

confidence: 99%

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Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Tabassum,

Bai

2021

Preprint

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This paper develops and evaluates the performance of an allocation agent to be potentially integrated into the onboard Detect and Avoid (DAA) computer of an Unmanned Aircraft System (UAS). We consider a UAS that can be fully controlled by the onboard DAA system and by a remote human pilot. With a communication channel prone to latency, we consider a mixed initiative interaction environment, where the control authority of the UAS is dynamically allocated by the allocation agent. In an encounter with a dynamic intruder, the probability of collision may increase in the absence of pilot commands in the presence of latency. Moreover, a delayed pilot command may not result in safe resolution of the current scenario and need to be improvised. We design an optimization algorithm to reduce collision risk and refine delayed pilot commands. Towards this end, a Markov Decision Process (MDP) and its solution are employed to create a wait time map. The map consists of estimated times that the UAS can wait for the remote pilot commands at each state. A command blending algorithm is designed to select an avoidance maneuver that prioritizes the pilot intention extracted from the pilot commands. The wait time map and the command blending algorithm are implemented and integrated into a closed-loop simulator. We conduct ten thousands fast-time Monte Carlo simulations and compare the performance of the integrated setup with a standalone DAA setup. The simulation results show that the allocation agent enables the UAS to wait without inducing any near mid air collision (NMAC) and severe loss of well clear (LoWC) while positively improve pilot involvement in the encounter resolution.

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“…To achieve a reliable human-automation harmony, a mathematically rigorous human operator model is paramount. A human operator model helps develop safe control systems, and provide a better prediction of human actions and limitations [8], [9], [10], [11]. Quasi-linear model [12] is one of the first human operator models, which consists of a describing function and a remnant signal accounting for nonlinear behavior.…”

Section: Introductionmentioning

confidence: 99%

A Control Theoretical Adaptive Human Pilot Model: Theory and Experimental Validation

Tohidi¹,

Yıldız²

2020

Preprint

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This paper proposes an adaptive human pilot model that is able to mimic the crossover model in the presence of uncertainties. The proposed structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov-Krasovskii stability criteria. The model can be employed for human-in-the-loop stability and performance analyses incorporating different types of controllers and plant types. For validation purposes, an experimental setup is employed to collect data and a statistical analysis is conducted to measure the predictive power of the pilot model.

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Shared Control Between Pilots and Autopilots: An Illustration of a Cyberphysical Human System

Cited by 15 publications

References 36 publications

A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario

A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

A Control Theoretical Adaptive Human Pilot Model: Theory and Experimental Validation

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