Experimentally Scoping the Range of UAS Sense and Avoid Capability

Maroney, David; Bolling, Robert; Athale, Ravindra A.; Christiansen, Alan D.

doi:10.2514/6.2007-2850

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…For this reason, we have been deliberate about how we have approached the airborne side of testing. We designed a specific test architecture to support the development of the platform capabilities and the support of SAA testing [6]. That architecture is outlined in Figure 2.…”

Section: C1-2mentioning

confidence: 99%

“…Adding dimensionality is the broad variety of sensors, cooperative and non-cooperative, active and passive, that might be used for SAA; these sensors have widely varying capabilities to measure distance or angle to an object, as well as closing rate and direction [5]. Most sensors are not able to provide all required information about the geometry of an encounter, so a combination of sensors is a likely solution [6]. This paper reports on the progress of our research methods for sensor evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Experiemntal platforms for evaluating sensor technology for UAS collision avoidance

Maroney

Boiling

Flathers³

2007

2007 IEEE/AIAA 26th Digital Avionics Systems Conference

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The problem of "sense-and-avoid" for Unmanned Aircraft Systems (UAS) is genuinely multi-dimensional: there is a wide range of UAS sizes, speeds, and maneuverability, as well as missions for which these UAS will be used. There are also a variety of sensors that might be used for sense-and-avoid, which have widely varying capabilities to measure distance or angle to an obstacle, as well as closing rate and time to collision. Most sensors are not able to provide all required information about the geometry of an encounter; therefore, using a combination of sensors offers one possible solution. We are investigating the concept that there may be a variety of possible sensor solutions for each distinct UAS capability or mission. By studying the breadth of UAS types and missions, and selectively testing certain sensors in the field, we are evaluating the limits to the capabilities of sensors and sensor combinations.This paper reports on part of the progress of this multi-dimensional evaluation. We have scoped the dimensions for evaluating UAS capabilities and the capabilities of sensors. We are experimentally evaluating sensor parameters on a variety of aircraft to validate the specified capabilities. This paper reports on our methodology for field evaluation of sensor technology and the lessons learned on evaluation platforms and capabilities.

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Section: C1-2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experiemntal platforms for evaluating sensor technology for UAS collision avoidance

Maroney

Boiling

Flathers³

2007

2007 IEEE/AIAA 26th Digital Avionics Systems Conference

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“…In the project of the Italian Aerospace Research Center (CIRA) the former one has a sensor suite composed by four Electro Optical (EO) cameras and a Ka-band pulse radar. This choice has been demonstrated to be adequate to support systems with a reliable full autonomy from ground [6][7][8]. Indeed, the radar permits the all-time all-weather use and provides a direct range-to-obstacle measure whereas EO cameras are adopted as auxiliary sensors to increase the angular resolution and the data rate.…”

Section: Introductionmentioning

confidence: 99%

A hardware in the loop facility for testing multisensor sense and avoid systems

Forlenza

Fasano

Accardo

et al. 2009

2009 IEEE/AIAA 28th Digital Avionics Systems Conference

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The Italian Aerospace Research Centre and the Department of Aerospace Engineering at University of Naples have been involved in a project for the development of an Obstacle Detection and Tracking suite for autonomous Collision Avoidance of Unmanned Aerial Systems. In this framework, a flight prototype of an autonomous Obstacle Detect Sense and Avoid system has been designed and realized. It is installed onboard a Very Light Aircraft named FLARE. The system is based on multiplesensor data integration and it includes several components, such as a Ka-band pulsed radar, four Electro Optical sensors and two processing units. A hierarchical sensor configuration has been chosen in which the radar is the main sensor while EO cameras are the auxiliary ones to increase accuracy and data rate.In order to maximize the outcome of flight tests, an indoor facility for Hardware-In-The-Loop tests has been developed. The indoor facility includes processing units dedicated to simulate aircraft and intruder dynamics that are provided as input to sensors. The radar is replaced by a simulator, while the real visible camera unit is used. Flight images are displayed on a LCD screen. The facility permits to test multiple critical flight configurations and different sensors combinations. Moreover, the availability of a well assessed simulator allows the research team to support several activities such as: i) tuning of the data fusion techniques (i.e. tracking based on Kalman filtering); ii) system performance validation for a wide range of scenarios; iii) evaluation of alternative architectures that are difficult to be reproduced during flights. Some results of hardware-in-the-loop tracking tests based on flight data are briefly summarized and expected flight performance of the electro-optical system as auxiliary sensor is discussed.

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“…10,[22][23][24] The RCAT System and Eagle Tree Systems units seem to be the only data recording devices currently available with a small enough form factor to allow them to be installed in a small to mid-sized UAV without being a major hindrance. The specifications for these units are given in Table 3.…”

Section: A Literature Review Of Data Acquisition Systems For Small Tmentioning

confidence: 99%

“…Closed-source commercial autopilots examined include the Cloud Cap Piccolo II, 1 MicroPilot MP2128g, 2 and Lockheed Martin Kestrel Flight Systems Autopilot v2.4; 3 these closed-source autopilots have been used for many years and often appear referenced in literature: Cloud Cap Piccolo, [4][5][6][7][8] MicroPilot, 9,10 and Kestrel. 11 The specifications for these units are given in Table 1.…”

Section: A Literature Review Of Data Acquisition Systems For Small Tmentioning

confidence: 99%

High-Frequency Sensor Data Acquisition System (SDAC) for Flight Control and Aerodynamic Data Collection

Dantsker

Mancuso

Selig

et al. 2014

32nd AIAA Applied Aerodynamics Conference

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This paper describes a high-frequency sensor data acquisition system (SDAC) for flight control and aerodynamic data collection research on small to mid-sized unmanned aerial vehicles (UAVs). The system is both low weight and low power, operates at 100 Hz and features: a high-frequency, high-resolution six degree-offreedom (6-DOF) inertial measurement unit (IMU) with a global positioning system (GPS) receiver, a 3-axis magnetometer, a pitot probe, seven 10-bit analog-to-digital converters (ADC), sixteen 12-bit analog-to-digital converters, a 14-bit analog-to-digital converter, twenty digital input/outputs (I/O), eight pulse width modulation (PWM) signal inputs, a 40 mile downlink transceiver, an open serial and an open CANbus port, and up to 64 GB of onboard storage. The data acquisition system was completely fabricated from commercial-off-theshelf (COTS) components, which reduced the system cost and implementation time. The SDAC combines the large variety of sensor streams into a unified high-fidelity state data stream that is recorded for later aerodynamics analysis and simultaneously forwarded to a separate processing unit, such as an autopilot.

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Experimentally Scoping the Range of UAS Sense and Avoid Capability

Cited by 14 publications

References 4 publications

Experiemntal platforms for evaluating sensor technology for UAS collision avoidance

Experiemntal platforms for evaluating sensor technology for UAS collision avoidance

A hardware in the loop facility for testing multisensor sense and avoid systems

High-Frequency Sensor Data Acquisition System (SDAC) for Flight Control and Aerodynamic Data Collection

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