Attention
Measure dwell time, video monitoring, alarms and competing tasks.
Deployment evaluation · 16
Measure sustained attention, alarm handling, recovery, training and the real number of robots one person can supervise.
FUURAA thesis
Engineering map
Each layer states the boundary to establish and the evidence needed for the next decision.
Measure dwell time, video monitoring, alarms and competing tasks.
Record cause, frequency, latency, duration and outcome.
Track initial and recurrent training, scenario coverage and skill decay.
Verification questions
Each question needs an object, conditions, denominator, threshold and accountable decision owner.
How many robots can one person safely supervise?
Which events require continuous attention?
What happens when the operator is unavailable?
Is intervention counted as failure?
Evidence to preserve
A conclusion alone loses reviewability; raw records, configuration and exclusions matter too.
Task-based workload study
Intervention and alarm distribution
Training and proficiency record
Scope boundary
A robot described as autonomous may still depend on route teaching, monitoring, exception handling and retrieval.
This is an engineering reading and decision framework; it does not replace application-specific risk assessment, conformity work, procurement acceptance or professional advice.
Sources and evidence status
Source dates and review status remain visible; external sources open in a new tab.
Defines repeatable measures across mobility, sensing, endurance, communications, autonomy, logistics, safety and operator proficiency; mission profiles combine multiple elemental tests.
Teams deployed autonomous systems to map, navigate and identify artifacts in unfamiliar tunnel, urban-underground and cave environments.
Documents mechanical, electrical, software, service-fault and time-synchronisation requirements for integrating sensing and manipulation payloads.