Engineering topics

Dolph, Chester V.

Publications and source records attributed to Dolph, Chester V..

5 records

NASA NTRS2019

Optical Geolocation for Small Unmanned Aerial Systems

This paper presents an airborne optical geolocation system using four optical targets to provide position and attitude estimation for a sUAS supporting the NASA Acoustic Research Mission (ARM), where the goal is to reduce nuisance airframe noise during approach and landing. A large precision positioned microphone array captures the airframe noise for multiple passes of a Gulfstream III aircraft. For health monitoring of the microphone array, the Acoustic Calibration Vehicle (ACV) sUAS completes daily flights with an onboard speaker emitting tones at frequencies optimized for determining microphone functionality. An accurate position estimate of the ACV relative to the array is needed for microphone health monitoring. To this end, an optical geolocation system using a downward facing camera mounted to the ACV was developed. The 3D positioning of the ACV is computed using the pinhole camera model. A novel optical geolocation algorithm first detects the targets, then a recursive algorithm tightens the localization of the targets. Finally, the position of the sUAS is computed using the image coordinates of the targets, the 3D world coordinates of the targets, and the camera matrix. A Real-Time Kinematic GPS system is used to compare the optical geolocation system.

Dolph, Chester V.↗

NASA NTRS2018

Safe2Ditch Autonomous Crash Management System for Small Unmanned Aerial Systems: Concept Definition and Flight Test Results

Small unmanned aerial systems (sUAS) have the potential for a large array of highly-beneficial applications. These applications are too numerous to comprehensively list, but include search and rescue, fire spotting, precision agriculture, etc. to name a few. Typically sUAS vehicles weigh less than 55 lbs and will be performing flight operations in the National Air Space (NAS). Certain sUAS applications, such as package delivery, will include operations in the close proximity of the general public. The full benefit from sUAS is contingent upon the resolution of several technological areas in order to provide an acceptable level of risk for widespread sUAS operations. Operations of sUAS vehicles pose risks to people and property on the ground as well as manned aviation. Several of the more significant sUAS technological areas include, but are not limited to: autonomous sense and avoid and deconfliction of sUAS from other sUAS and manned aircraft, communications and interfaces between the vehicle and human operators, and the overall reliability of the sUAS and constituent subsystems. While all of the technological areas listed contribute significantly to the safe execution of the sUAS flight operations, contingency or emergency systems can greatly contribute to sUAS risk mitigations to manage situations where the vehicle is in distress. The Safe2Ditch (S2D) system is an autonomous crash management system for sUAS. Its function is to enable sUAS to execute emergency landings and avoid injuring people on the ground, damaging property, and lastly preserving the sUAS and payload. A sUAS flight test effort was performed to test the integration of sub-elements of the S2D system with a representative sUAS multi-rotor.

Glaab, Louis J.↗

NASA NTRS2018

Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

f autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN.

Dolph, Chester V.↗

NASA NTRS2018

Small Unmanned Aerial System (UAS) Flight Testing of Enabling Vehicle Technologies for the UAS Traffic Management Project

Small unmanned aerial systems (sUAS) have been studied and results indicate that there is a large array of highly-beneficial applications. These applications are too numerous to list, but include search and rescue, fire spotting, precision agriculture, etc. to name a few. Typically sUAS vehicles weigh less than 55 pounds and will be performing flight operations in the presence of manned aircraft and other sUAS. Certain sUAS applications, such as package delivery, will include operations in the close proximity of the general public. The full benefit from sUAS is contingent upon the resolution of several technological areas to enable free and widespread use of these vehicles. Technological areas in question include, but are not limited to: autonomous sense and avoid and deconfliction of sUAS from other sUAS and manned aircraft, communications and interfaces between the vehicle and human operators, and high-reliability autonomous systems. The NASA UAS Traffic Management (UTM) project is endeavoring to develop a traffic management system and concept of operations for these types of vehicles. An extensive sUAS flight test effort was performed to partially address vehicle-related technological areas and to shape an understanding of future developmental and test efforts for vehicles intended to use the UTM traffic management system. The flight testing described herein had the following objectives: 1) Install and test Dedicated Short Range Communications (DSRC) systems developed for the automotive industry for potential sense and avoid sUAS applications; 2) Evaluate the use of cellular 4G systems to provide vehicle control; 3) Obtain high-resolution video imagery in support of image-based optical detection sense and avoid systems; 4) Acquire data in fixed-wing flight to support validation and maturation of an autonomous range containment system known as Safeguard in fixed-wing flight. A total of 53 flights were performed over 12 operational days at Beaver Dam Airpark in Elberon, VA. This work was sponsored by the UTM project that is part of the Aviation Operations and Safety Program (AOSP) at NASA.

Glaab, Louis J.↗

NASA NTRS2017

Sense and Avoid for Small Unmanned Aircraft Systems

The ability for small Unmanned Aircraft Systems (sUAS) to safely operate beyond line of sight is of great interest to consumers, businesses, and scientific research. In this work, we investigate Sense and Avoid (SAA) algorithms for sUAS encounters using three 4k cameras for separation distances between 200m and 2000m. Video is recorded of different sUAS platforms designed to appear similar to expected air traffic, under varying weather conditions and flight encounter scenarios. University partners and NASA both developed SAA methods presented in this report.

Dolph, Chester V.↗