Guided airdrop systems have traditionally used position and velocity information from a GPS receiver as their only source of feedback.
The use of additional sensors in the guidance units is challenging because sensors in the guidance unit are in close proximity to powerful electric motors. Furthermore, there is a large amount of relative motion between the guidance unit and the parachute as they are coupled by a flexible network of rigging lines.
By placing sensors in the parachute itself, it is possible to obtain accurate estimates of the canopy motion and orientation with low-cost sensors requiring minimal calibration.
Specialized in-canopy sensor pods were developed to provide distributed sensing throughout a parachute canopy and a sensor fusion algorithm was developed to combine the raw data from these sensor pods into useful canopy state estimates. The effectiveness of this approach is demonstrated first in simulation, and then with flight test results on full-scale airdrop systems.
Closed loop steering control using in-canopy sensor data feedback on a guided airdrop system is shown to provide dramatic enhancements in heading tracking capability. The rich feedback signal available from in-canopy sensors can provide improved datasets for more detailed system identification as well as enabling novel guidance, navigation and control approaches which will lead directly to improved landing accuracy.