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Updated: May 18, 2026

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

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Published on: April 4, 2025

Embedding Human Expert Cognition Into Autonomous UAS Trajectory Planning.

Pritesh Narayan, Patrick Meyer, Duncan Campbell

IEEE Transactions on Cybernetics

|September 6, 2012

Summary

This summary is machine-generated.

This study integrates human expert cognition into autonomous trajectory planning for unmanned aerial systems (UASs). This approach optimizes UAS flight paths by incorporating human decision-maker preferences for improved mission alignment.

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Last Updated: May 18, 2026

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Published on: April 4, 2025

Area of Science:

Robotics
Artificial Intelligence
Aerospace Engineering

Background:

Autonomous trajectory planning for unmanned aerial systems (UASs) often involves multiple, conflicting objectives.
Existing methods may not fully capture the nuanced decision-making of human operators.

Purpose of the Study:

To develop and demonstrate a novel method for incorporating human expert cognition into autonomous trajectory planning for low-altitude UAS operations.
To enhance trajectory optimization by aligning automated decisions with human preferences.

Main Methods:

Application of additive multiattribute value theory for multicriteria optimization of UAS trajectories.
Development of a graphical user interface for eliciting human decision-maker (HDM) preferences through simulated scenarios.
Conversion of HDM data into value functions and criteria weightings using utility additive theory.

Main Results:

Successfully integrated human expert preferences into an automated decision system for UAS trajectory planning.
Generated UAS trajectories that more closely align with elicited human decision-maker preferences.
Demonstrated the approach's efficacy through simulations involving a fixed-wing UAS in low-altitude environments.

Conclusions:

Human expert cognition can be effectively integrated into autonomous trajectory planning for UASs.
Multicriteria decision aid techniques, specifically additive value theory, enhance trajectory optimization by incorporating human preferences.
This approach offers a promising direction for developing more intelligent and human-aligned autonomous systems.