Task-Allocation Decisions of Human-UAS Collaboration: Effects of Workload, Trust, and Self-confidence

Area of Science:

• Human-Computer Interaction
• Aerospace Engineering
• Cognitive Psychology

Background:

• Uncrewed Aerial Systems (UAS) are increasingly vital for urban air mobility, package delivery, and emergency response.
• The m:N architecture, involving multiple operators (m) managing numerous vehicles (N), enhances UAS operational efficiency.
• Understanding human factors is crucial for effective UAS operation in complex scenarios.

Purpose of the Study:

• To investigate the influence of workload on operator task-allocation decisions in a simulated m:N UAS environment.
• To examine the mediating roles of trust and self-confidence in operator decision-making under varying workload conditions.
• To compare decision-making strategies between UAS pilots and novice university students.

Main Methods:

• A simulated UAS package-delivery task was designed using the m:N architecture.
• Participants (UAS pilots and university students) were exposed to two workload conditions via video stimuli.
• Perceived workload, trust, and self-confidence were measured post-simulation; task-allocation preferences were recorded.

Main Results:

• Workload significantly affects task-allocation decisions in m:N UAS operations.
• Trust and self-confidence mediate the relationship between workload and decision-making.
• Expertise level (UAS pilots vs. students) influences task-allocation strategies and responses to workload.

Conclusions:

• Operator workload and human factors like trust and self-confidence are critical for designing effective m:N UAS task-allocation systems.
• Tailoring task-allocation strategies based on operator expertise is essential for optimizing UAS mission performance.
• Future research should focus on real-world validation of these findings to enhance UAS safety and efficiency.