Combinatorial Effects of Unmanned Vehicles on Operator’s Mental Workload and Performance for Searching

Huang, Yonghao; Alqatami, Omar; Zhang, Wei · 2024 · Crossref

DOI: 10.54941/ahfe1005011

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Summary

This study investigates the combinatorial effects of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) on operator mental workload and search performance. As unmanned systems become prevalent in logistics, emergency rescue, and military applications, understanding human-machine collaboration is critical. While UAVs and UGVs offer complementary advantages, operators must manage semi-autonomous systems, often leading to information overload. The research specifically addresses how different combinations of these vehicles impact task efficiency and cognitive load, aiming to optimize system design and prevent safety incidents caused by mental under- or overloading. The researchers employed a 2×4 repeated-measures experimental design involving 16 participants from Tsinghua University. The independent variables were task complexity (low: 4 targets; high: 8 targets) and unmanned vehicle combinations (1UAV+1UGV, 1UAV+2UGVs, 2UAVs+1UGV, and 2UAVs+2UGVs). Participants performed simulated search tasks using PsychoPy software, controlling vehicles via a mouse to locate colored cubes with letters in a virtual environment. Performance metrics included recognition accuracy, completion time, total vehicle movements, and vehicle utilization rate. Mental workload was assessed subjectively using the NASA-TLX scale, focusing on mental demand. The results indicated that increasing the number of controllable vehicles did not improve recognition accuracy, which remained above 90% across all conditions. However, task completion times increased significantly as the number of vehicles grew, with the 2UAVs+2UGVs combination taking the longest. Vehicle utilization rates were highest for the 1UAV+1UGV combination and significantly lower for combinations with more vehicles. Furthermore, mental demand scores were significantly higher for the 2UAVs+2UGVs combination during high-complexity tasks compared to the 1UAV+1UGV setup. The data revealed that adding vehicles, particularly UAVs, increased attention shifts and processing time without enhancing efficiency. The study concludes that a binary growth in controllable unmanned vehicles negatively impacts operator performance by increasing mental workload and completion times, rather than improving utilization. The authors attribute this to "change blindness" and attention disruption caused by frequent switching between different vehicle types and frames of reference. These findings suggest that simply adding more vehicles to a human-machine team is counterproductive. Instead, system designers should focus on optimizing interface design and interaction technologies to manage information flow, ensuring that human operators are not overwhelmed by redundant data. This research provides critical insights for designing efficient human-computer collaboration frameworks in unmanned systems.

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• mental demand