After a sufficient amount of use under certain conditions, parachutes wear out and can no longer be safely employed. A simple and effective indicator of the remaining life of a parachute is the number of times it has been used, surviving opening shock, canopy inflation, transient parachute dynamics, and low velocity flight to the ground. This paper documents a small electronic device that automatically detects and logs each time a parachute has been used. Hardware and software that enables this device are described and data from bench and flight testing are detailed to highlight detection algorithm performance and long-term power consumption. Data shows that the proposed device accurately and reliably identifies individual parachute flights and that the ultra low power nature of the design permits the device to be continuously on for a period of time that is greater than the life span of the parachute. Thus, the device can be installed in the canopy during the fabrication process without the need for recharging or replacement of batteries.

The private eVTOL/UAM market is rapidly expanding, with many companies and investors betting on the possibility of low-cost-to-operate and zero emissions air taxis and last-mile-delivery sUAS dominating the skies of cities in the near future. While fixed wing and rotary wing aircraft regularly operate over populated areas, the envisioned orders-of-magnitude increase in air traffic increases the risk to those on the ground and in the air. The risk calculus is also altered by the novel designs of some eVTOL aircraft. Many designs have neither significant autorotation nor low-speed-gliding capability to land safely in the event of catastrophic power failure. Ballistic parachute systems (ballistic recovery systems), such as those found in increasing use on general aviation aircraft and small UAS (drones) provide some risk mitigation, but provide no control over the landing location; the aircraft will land wherever the wind blows it. Aviation safety experts have noted that while ballistic recovery systems improve safety and should be included on all UAM aircraft, there are still hazards from a vehicle descending uncontrolled under a parachute. This paper covers the analysis and design of a guided emergency recovery system, which uses bleed-air control and a gliding steerable parachute. The analysis shows that due to the use of a parachute which provides lift, rather than a pure drag device, and the use of lightweight bleed-air control, the overall weight of a guided recovery system can be lower than an equivalent ballistic recovery system. A key feature for an effective guided recovery system is selection of its landing point. The development of a massively parallel probabilistic evaluation method for selecting landing points which provide the best likelihood of avoiding hazards is shown with examples. Simulations of EGRES in New York City Urban Air Mobility, UAS interstate highway inspection, and UAS nuclear power plant inspection scenarios showed that EGRES increased the likelihood of landing in an acceptable area by 2.3, 1.8, and 2.9 times respectively, compared with a ballistic parachute recovery system. Finally, a preliminary design for a Group 2 UAS is presented, along with size and weight estimates.

There is a frequent need to cut lines or rigging on command in airdrop applications, and this is typically done with pyrotechnically driven cut knives. The use of hot wire cutters offers a simple, quiet, low-cost, low-weight alternative. This work provides an explanation of the principals behind hot wire cutters, outlines the design of an example hot-wire cutter system, provides results from experimental testing of this system on a variety of materials typically used in cargo airdrop, and shows results of the use of the system in small-scale airdrop testing. A simple, battery powered, small device was developed which can rapidly cut a wide variety of synthetic ropes including those made from nylon, polyester, Spectra, Dyneema, Vectran, and Kevlar. This device can be used to de-rig payloads from airdrop platforms, detach main parachutes from payloads after landing, and de-reef parachutes.

Guided airdrop systems offer the ability to deliver payloads with accuracy and with a high payload to air vehicle weight fraction. Traditionally, these systems use airborne guidance units with trailing edge control to steer ram-air parafoils in the same manner as a human pilot. Trailing edge control only provides lateral (turn rate) and limited airspeed control. Earthly Dynamics, LLC developed an in-canopy precision guided aerial delivery system that offers both lateral steering and longitudinal glide slope control of a ram-air parafoil. This paper will present the feasible mission CONOPS for this technology as a cargo delivery, spacecraft recovery, or personnel autopilot system. This work then details the flight-proven hardware and software architecture implemented in a 116 ft$^2$ ram-air parachute. Scalability of the design up to 2400 lb payloads is presented. Data and configuration for multiple flight test demonstrations as a cargo airdrop system and a spacecraft recovery system are provided. Finally, this paper concludes with future applications and development plans for this technology.

Impact attenuation is a critical component of cargo airdrop systems, ensuring payload survivability while allowing practical impact speeds. Cardboard honeycomb is the standard material used for this purpose, but this material is far too stiff to function well on small (5 – 50) lb payloads. This work describes an effort to investigate alternative impact attenuation options for small, lightweight payloads. A purpose-built impact test rig was constructed to facilitate rapid testing and enable repeatable impacts at various combinations of horizontal and vertical impact speeds. A computer vision system was developed to track the payloads as they impact to measure actual impact speeds, and custom sensors were used to measure impact accelerations. A variety of different impact attenuation materials were tested using the test rig and instrumentation systems and the results of these experiments are reported.

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. 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.

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.

Uncertainty in atmospheric winds represents one of the primary sources of landing error in airdrop systems. In this work, a ground-based LIDAR system samples the wind field at discrete points above the target and transmits real-time data to approaching autonomous airdrop systems. In simulation and experimentation, the inclusion of a light detection and ranging (LIDAR) system showed a maximum of 40% improvement over unaided autonomous airdrop systems. Wind information nearest ground level has the largest impact on improving accuracy.

A unique, beneficial feature of rotorcraft is their flexibility in aircraft-to-ground interfacing. For a variety of reasons, hard landings can occur when the descent rate of the aircraft is larger than intended. The resulting impact can result in vehicle damage, structural failure, injuries, etc. To reduce these risks, an attractive solution is the implementation of a robotic legged landing gear (RLLG) system. The system softens a hard landing by acting as a shock absorber with a relatively large stroke, allowing the aircraft to decelerate over a much larger distance compared with a tradition landing gear system. This paper explores the mitigation of rotorcraft hard landings via RLLG through a comprehensive multibody dynamics simulation tool. The purpose of this study is to demonstrate the efficacy of the RLLG as a robust solution to reduce loads during hard landings for multiple landing configurations. The results show that when using RLLG in place of conventional landing gear, peak loads are reduced by approximately 70–90%, depending on the landing conditions. Through Monte Carlo simulation, robotic landing gear system performance is shown to be robust to uncertain conditions.

Precision placement of guided airdrop systems necessarily requires some mechanism enabling effective directional control of the vehicle. Often this mechanism is realized through asymmetric deflection of the parafoil canopy trailing-edge brakes. In contrast to conventional trailing-edge deflection used primarily for lateral steering, upper-surface bleed air spoilers have been shown to be extremely effective for both lateral and longitudinal (i.e., glide slope) control of parafoil and payload systems. Bleed air spoilers operate by opening and closing several spanwise slits in the upper surface of the parafoil canopy, thus creating a virtual spoiler from the stream of expelled ram air. The work reported here considers the autonomous landing performance of a small-scale parafoil and payload system using upper-surface bleed air spoilers exclusively for both lateral steering and glide slope control. Landing accuracy statistics computed from a series of Monte Carlo simulations in a variety of atmospheric conditions and experimental flight tests were found to be in good agreement. Median miss distances for the combined lateral and longitudinal control logic are on the order of 13m, indicating an improvement in landing accuracy of nearly 50% over similar systems employing only lateral steering control.

Current autonomous airdrop systems critically rely on GPS feedback for inertial position and velocity information. Future airdrop missions will involve scenarios where it is desired to aerially deliver payloads accurately in an area where GPS is not available. In these situations, a pressing need exists to create useful feedback information from a non-GPS source for use by the autonomous airdrop system guidance and control algorithm. This work addresses this issue through implementation of radio beacons which can be deployed by ground crew on site to retrieve package delivery. Current radio beacon technology is presented in addition to two control algorithms designed around different levels of radio beacon feedback information.

Autonomous guided airdrop systems based on steerable, ram-air parafoils generally offer significant improvements in payload delivery accuracy compared to unguided airdrop. In the event of a malfunction or rigging error, the gliding and steering ability of the parafoil canopy can exhibit extremely large deviations from the nominal condition. Significant damage and deviations from the nominal flight characteristics can break the most advanced flight controllers, even ones employing adaptive elements to account for small variations in flight speed, atmospheric wind conditions, and small turning biases. This work details the theory and application of a hyperadaptive algorithm for the estimation and control of a parafoil and payload system through the implementation of a Hammerstein model. Integration of system identification methods into an advanced guidance algorithm allows for dynamic and steady state characterization for accurate landings at various levels of system damage.

The direct inclusion of human pilots into airdrop operations has a strong potential to increase the landing accuracy of conventionally autonomous guided airdrop systems. Human pilots have significant mental flexibility and an innate ability to prioritize requirements to aid safe and accurate landings. Autonomous algorithms on the other hand, are generally rigid in nature and cannot handle situations not directly programmed into the software. This paper outlines the work done to develop an integrated human-machine interface that combines the flexibility of human control decisions with the powerful ability of autonomous systems to measure data on the aircraft and generate key estimates. Two interfaces are presented melding a first person view camera mounted to the payload of the airdrop system and a bird's eye GPS based map of the drop zone with relevant system estimates overlaid. Flight testing of these digital feedback methods to the pilot are studied to identify the ability of human operators to successfully and accurately control an airdrop system to the ground. Results indicated that a trained human operator has the ability to improve landing accuracy over a conventionally autonomous system by 36%.

Uncertainty in atmospheric winds represents one of the primary sources of landing error in airdrop systems. While guided airdrop systems can compensate for uncertainties in the wind profile, unexpected winds in the drop zone can still result in large errors in landing location, and they can even lead to damage or complete loss of the cargo if the system hits the ground while traveling downwind. This work examines the impact of real-time knowledge of the winds in the drop zone on guided aidrop landing accuracy and landing quality. Measurements of the horizontal wind profile at multiple altitudes above the target provided by a ground-based LIDAR are considered in addition to a simple ground wind measurement. The guidance logic running on the airdrop system’s onboard autopilot is modified to integrate the wind measurements near the intended landing zone with onboard wind estimates to provide an improved, real-time estimate of the wind profile. The strategy is first developed in the framework of a rigorous simulation model and then validated in flight test. In both simulated and actual flight tests, knowledge of the wind profile near the target provided from the LIDAR unit improved landing accuracy by 40%. Knowledge of the ground winds alone provided by a low-cost, lightweight, highly portable device, again in both simulated and actual flight tests, is enough to improve landing accuracy by 33% and completely eliminate potentially dangerous downwind landings.