Human-in-the-loop control of guided airdrop systems

Abstract

Advances in guided airdrop technology including guidance, navigation, and control algorithms, novel control mechanisms and wind sensing algorithms have led to significant improvements over unguided airdrop systems. Guided systems are autonomously controlled with an embedded microprocessor using position and velocity feedback. While capable of highly accurate landing, these systems struggle to overcome deviations from expected flight dynamics due to canopy damage or cargo imbalance, complex terrain at the drop zone, and loss of sensor feedback. Human operators are intelligent, highly adaptive, and can innately judge the flight vehicle and environment to steer the vehicle to the desired impact point provided sufficient information. This work experimentally explores operators' abilities to accurately land an airdrop system using different sensing modalities. Human operator landing results are compared with a state of the art fully autonomous airdrop system. Across the methods analyzed, human operators attained up to a 40% increase in landing accuracy over the fully autonomous control algorithm.