Sunday, March 1, 2015

UAS Sense and Avoid Sensor Selection

Sense and Avoid Sensor Selection
Mark C. Hardy
UNSY 605 Unmanned Systems Sensing, Perception, and Processing
Embry-Riddle Aeronautical University

                Unmanned aircraft systems (UAS) possess unique abilities which could have transformative effects on entire commercial industries.  However, in order to realize this potential UAS must be able to comply with applicable rules and regulations relative to the airspace in which the UAS will be operating. The Federal Aviation Administration has indicated that UAS operating in the United States must fulfill the requirements specified by 14 CFR §91.111, §91.113 and §91.181 that govern operations near other aircraft and various right-of-way rules. Key to the UAS airspace integration discussion, is language found in 14 CFR §91.113 that mandates all persons operating aircraft to maintain vigilance “so as to see and avoid other aircraft” (General Operating and Flight Rules, 2015). As a result, an effort is underway to develop sensor based technologies which will enable UAS to sense and avoid (SAA) potential collision hazards. SAA systems for large UAS such as the Global Hawk and Predator B are already in the operational test phases, however, a full source SAA solution for small UAS (sUAS) that weigh less than 55 pounds has yet to emerge (Carey, 2014).
            Researchers at the University of Kansas have developed a prototype for a radar based SAA system that may be sufficiently compact enough to meet the size, weight and power limitations (SWaP) of some sUAS (Allen, Ewing, & Keshmiri, 2015). Radar is an active remote sensing technology that emits pulses of electromagnetic energy and uses the reflected returns to determine the relative position of objects in the surrounding environment (Austin, 2010). Radar offers distinct advantages for SAA. Radar is essentially an all-weather technology capable of day/night detection of non-cooperative targets such as aircraft not equipped with transponders, birds, parachutists, or terrain obstacles (Barnhart, Marshall & Shappee, 2011).   
The system developed at the University of Kansas employs a multichannel frequency-modulated, continuous wave (FMCW) radar to sense targets within its field of interest (+/- 110 degrees azimuth, +/- 15 degrees elevation).  The system utilizes programmable, radar -ready chipset technology originally developed for FMCW radars used in automobile collision-avoidance systems. Processing for the SAA system is conducted by an Xlinix Spartan 6 field-programmable gate array (FPGA) which provides rapid processing and added flexibility. Objects within range are painted by a single low gain transmit antenna, while a set of four low-gain antennas, which combine to create a virtual anechoic chamber, form the receiving array.  Target range is gauged by the echo signal’s beat frequency and velocity is determined by measuring phase variation over successive radar returns. Target azimuth and elevation data are calculated by correlating the signal phases captured by the individual antennas of the receive array. Initial laboratory and flight tests indicated that the system could detect an object with a 1 square meter radar cross section (RCS) at a distance of approximately 430 meters. However, subsequent laboratory testing of the miniaturized system revealed significant improvements in sensitivity, leakage signal tolerance, and noise floor. These improvements could potentially extend the device’s detection range for a target with a 1 square meter RCS to 860 meters. Targets with larger RCS values could be detectable at greater ranges (Allen, Ewing & Keshmiri, 2015).
The prototype’s processing components and the radio frequency (RF) front end assembly weigh approximately 6 ounces and are housed in a 6.5” x 4” x 2.25” shielded container. The system operates at 2.37 gigahertz which enables the use of smaller commercial off the shelf (COTS) antennas. The prototype antenna array measures 7.5” tall x 3.4” diameter and weighs approximately 12 ounces. The FMCW architecture reduces transmit power requirements, during testing, the system’s RF front end assembly was shown to consume approximately 10 watts of power (Allen, Ewing & Keshmiri, 2015).
                Further refinement and testing of the system is required before it can become a market ready SAA solution. Additional data processing requirements may force developers to switch from the FPGA to a multi-core processor capable of managing an auto-pilot algorithm which is also under development at the University of Kansas (Allen, Ewing & Keshmiri, 2015). Additional miniaturization of the system’s components would increase its feasibility for use with a wide variety of sUAS. Pricing information for this system is not yet available; however, a miniature phased array radar developed at the University of Denver’s Unmanned Research Institute was recently licensed by Integrated Robotics Imaging Systems Ltd. which plans to offer the devices at a price point of $7,000-$10,000 (Brehmer, 2014).    
References
Allen, C., Ewing, M., & Keshmiri, S. (2015, January). Multichannel Sense-and-Avoid Radar for Small UAVs. Retrieved from http://nari.arc.nasa.gov/sites/default/files/Allen_LEARN%20Final%20Report%20-%20Kansas%20-%20Jan%202015%20%20%28v2%29.pdf

Austin, R. (2010, March). Payload Types. Unmanned Aircraft Systems : UAV Design, Development and Deployment (pp. 136-137). United Kingdom: John Wiley & Sons.

Barnhart, R., Marshall, D., & Shappee, E. (2011). Detect, Sense and Avoid. Introduction to Unmanned Aircraft Systems (pp.138-151). Boca Raton, FL: CRC Press.

Brehmer, E, (2014, May 22). Kenai Company Leading the Way on Unmanned Aircraft Radar. Alaska Journal of Commerce. Retrieved from http://www.alaskajournal.com/Alaska-Journal-of-Commerce/May-Issue-4-2014/Kenai-company-leading-the-way-on-unmanned-aircraft-radar/

Carey, B. (2014, July 11). U.S. Firms Advance UAS ‘Detect and Avoid’ Capability. Retrieved from http://www.ainonline.com/aviation-news/2014-07-11/us-firms-advance-uas-detect-and-avoid-capability


General Operating and Flight Rules, 14 C.F.R. §91 (2015).

Tuesday, February 24, 2015

Unmanned Surface Vehicle Control Station Analysis

Control Station Analysis
By Mark C. Hardy
UNSY 605-Unmanned Systems Sensing, Perception, and Processing
Embry-Riddle Aeronautical University

The Fleet-class Common Unmanned Surface Vessel (CUSV) was developed by AAI Corporation, a Textron subsidiary, in collaboration with the Maritime Applied Physics Corporation (MAPC). The CUSV is a surface-borne sea craft designed to conduct mine detection/neutralization, anti-submarine operations, intelligence collections, communications relay activities, and unmanned systems launch and recovery. The fourth generation CUSV is 39 feet long, has a top speed of 28 knots, and a cruising range of 1200 nautical miles (Naval-Technology, 2015). In October of 2014 the CUSV was selected by the U.S. Navy to serve as a component of its Unmanned Influence Sweep System (UISS) in conjunction with the Navy’s Freedom and Independence class of littoral combat ships (Textron Inc., 2014; Naval-Technology, 2015).

Command and control (C2) of the CUSV is conducted via the Universal Command and Control Station, which is essentially a maritime version of AAI’s Universal Ground Control Station (UGCS). AAI’s UGCS is utilized by the U.S. Army and U.S. Marine Corps for C2 of unmanned aircraft system (UAS). The CUSV’s UCCS was designed in compliance with NATO Standardization Agreement 4586, the Joint Architecture for Unmanned Systems (JAUS) protocol, and the littoral combat ship communications architecture (AAI Corporation, 2011). As such, the UGCS and UCCS are highly interoperable and can be reconfigured and reprogrammed for C2 compatibility with various unmanned systems. Moreover, the UGCS/UCCS is capable of simultaneous operation of multiple unmanned aircraft, surface vessels, and/or ground vehicles (AAI Corporation, 2010).  

The UCCS communicates with CUSV via the Harris SeaLancet RT-1944/U data link. The SeaLancet is a internet protocol based high bandwidth data link capable of transmitting information at up to 54 megabytes per second (Mbps). The SeaLancet has maximum range of 150 miles for line of sight (LOS) operations, but range can be extended beyond line of sight with the use of data link relays (Harris Corporation, 2015). The CUSV utilizes the data link to transfer real-time video, sensor data, navigation data, and other mission related information (AAI Corporation, 2011).

The UGCS framework, which the UCCS is based upon, incorporates intuitive web based interfaces combined with enhanced human machine interface software (AAI Corporation, 2010). The UCCS relies on traditional data presentation and user interface techniques to interact with the CUSV operator. The UCCS is equipped with basic keyboard, mouse, and joy stick interfaces to facilitate operator input. Visual information is presented via several display screens depending on the number of unmanned systems being operated. Visual display options include vehicle status information, geographical/navigational display, and sensor/mission oriented displays. Data points derived from sensor collections, such as the detection of a mine like object identified by the CUSV sonar, is transmitted to the UCCS where it can then be overlayed onto the UCCS geographic display for operator target situation awareness (Textron Inc., 2012; AAI Corporation, 2011).

Figure 1. Universal Command and Control Station. AAI Corporation. (2011). Performance, Persistence & Modularity. Retrieved from http://suat.aaicorp.com/sites/default/files/datasheets/AAI_CUSV_08-08-11_AAI.pdf

The CUSV has a demonstrated sliding autonomy capability, which allows it to conduct autonomous and man-in-the-loop operations. The UCCS is equipped with the Mine Warfare Environmental Decision Aid Library (MEDAL) software suite, which utilizes historical and in situ environmental data to assess mine threats, develop mine sweeping plans, and recommend tactics, techniques and procedures (TTP) (National Research Council, 2000). MEDAL generated mine countermeasure mission plans can then be preloaded to the CUSV and executed autonomously or with varying levels of operator input (Textron Systems, 2012).

The UCCS was designed to meet military interoperability standards which require unmanned system control stations to be universally compatible with most other unmanned platforms, therefore requiring a fairly simplistic data presentation scheme. However, the UCCS could be improved with the implementation of multimodal user interfaces that transmit and receive information to and from the operator via multiple sensory channels. For instance, speech control technology could be implemented to assist with operator command and control of the CUSV. Haptic feedback, such as vibro-tactile cues, could be incorporated into the operator controls to assist with the launch and recovery of sensors and/or other unmanned systems. Vibro-tactile technology could also be used to enhance obstacle avoidance and manual navigation in the open sea, or during docking operations. Virtual Reality displays could be employed to provide enhanced spatial situation awareness and safety by expanding the operator’s field of view and delivering a 3 dimensional perspective.

In conclusion, the UCCS is a highly adaptable and capable unmanned GCS. However, current research indicates that implementation of multimodal presentation methodologies, such as those recommended, could lead to improved unmanned system operator performance (Maza, Caballero, Molina, Pena & Ollero, 2010). The addition of such technologies to the UCCS, could ultimately enhance UCCS and CUVS capabilities. 
References
AAI Corporation. (2011). Performance, Persistence & Modularity. Retrieved from http://suat.aaicorp.com/sites/default/files/datasheets/AAI_CUSV_08-08-11_AAI.pdf

AAI Corporation. (2010). When the Mission Changes-We Adapt. Retrieved from http://www.maxvision.com/Downloads/MesaMaxinuseAAIShadow.pdf

Harris Corporation. (2015). SeaLancet™ RT-1944/U—NetCentric IP Solution for DoD Platforms at the Tactical Edge. Retrieved from http://webcache.googleusercontent.com/search?q=cache:ZmpqmIEB4hsJ:govcomm.harris.com/solutions/products/defense/sealancet.asp+&cd=1&hl=en&ct=clnk&gl=us

Maza, I., Caballero, F., Molina, R., Pe˜na, N. & Ollero, A. (2010). Multimodal Interface Technologies for UAV Ground Control Stations. Journal of Intelligent and Robotic Systems, 57(1-4), 371-391.

National Research Council (2000, March 6). Oceanography and Mine Warfare. Retrieved from http://www.nap.edu/openbook.php?record_id=9773&page=32

Naval-Technology. (2015). Fleet-Class Common Unmanned Surface Vessel (CUSV), United States of America. Retrieved from http://www.naval-technology.com/projects/fleet-class-common-unmanned-surface-vessel-cusv/

Textron Inc. (2012). Common Unmanned Surface Vessel Ushers in New Era of Naval Mine Countermeasure Operations. Retrieved from http://investor.textron.com/newsroom/news-releases/press-release-details/2012/Common-Unmanned-Surface-Vessel-Ushers-in-New-Era-of-Naval-Mine-Countermeasure-Operations/default.aspx

Textron Inc. (2014, October 22). Textron Systems Awarded $33.8 Million for the U.S. Navy’s Unmanned Influence Sweep System. Retrieved from http://investor.textron.com/newsroom/news-releases/press-release-details/2014/Textron-Systems-Awarded-338-Million-for-the-US-Navys-Unmanned-Influence-Sweep-System/default.aspx

Textron Systems (2012, September 21). CUSV: Trident Warrior Experiment 2012 [Internet Video]. Retrieved from https://www.youtube.com/watch?v=CT1xjn183n4

Sunday, February 8, 2015

Desert Hawk III UAS: Data Protocol and Format

Desert Hawk III UAS: Data Protocol and Format
Mark C. Hardy
Unmanned Systems Sensing, Perception, and Processing 605
Embry Riddle Aeronautical University

The Lockheed Martin Desert Hawk III is an electrically powered fixed-wing unmanned aircraft system (UAS) capable of performing low altitude, short endurance intelligence, surveillance, and reconnaissance (ISR) missions. The six pound Desert Hawk III’s airframe largely consists of carbon and foam reinforced with a Kevlar coated outer shell (Lockheed Martin, 2013). The Desert Hawk III is capable of carrying a two pound ISR sensor payload and has a maximum endurance of approximately two hours (Hemmerdinger, 2014). Lockheed Martin’s portable Ground Control Station (GCS) maintains two-way connectivity with the air vehicle via digital internet protocol (IP) data link. This allows the operator to make real-time changes to the Desert Hawk’s pre-programmed flight plan while also facilitating payload control (Lockheed Martin, 2013).

Sensor payloads available for the Desert Hawk III are easily interchangeable and offer users the ability to swiftly adapt the UAS to meet changing mission requirements. Payload technologies currently available for the Desert Hawk III include an electro-optical (EO) imager, long wave infrared (IR) imager, and a 300 milliwatt (mW) laser illuminator (LI) (Lockheed Martin, 2013).

The EO/IR/LI sensors are all housed in Lockheed Martin’s Perceptor Dual Sensor Gimbal. The Perceptor gimbal is a gyro stabilized turret design capable of 360 degree continuous rotation, allowing persistent surveillance of targets with limited aircraft maneuvering (Lockheed Martin, 2012).

The EO camera is able to produce high definition 720p quality video and 10 megapixel high resolution still imagery. The EO camera is also capable of electronic pan, tilt, zoom (PTZ) and image stabilization. Additionally, Desert Hawk’s EO camera utilizes the Lockheed Martin Onpoint onboard vision processing unit (VPU) to conduct advanced image processing enabling the Desert Hawk to perform ground target tracking. The Onpoint VPU consists of a 1.75 watt, dual core OMAP ARM/DSP (1 gigahertz ARM core, 800 megahertz DSP core) processor utilizing 512 Megabytes (MB) of flash memory and a 256 MB double data rate. The Onpoint system also feeds metadata overlay information to the GCS via data link (Lockheed Martin, 2012).    

The Desert Hawk III’s long wave IR thermal core imager, developed by FLIR, is an uncooled camera capable of producing 640x480 resolution imagery. The system features high shock and vibration tolerance and a power dissipation of approximately 1.2 watts with required input voltage of 3.3 volts direct current (VDC) (FLIR, 2012).        

Desert Hawk III relies on the Microhard Nano Digital Data Link Radio and the Lockheed Martin Procerus video digitizer to provide a high bandwidth, low latency datalink with the GCS. The system supports SD and HD video inputs and offers a two-way data link with data transfer speeds of up to 12 megabytes per second. The system is IP based but is also equipped with serial bridging for ease of integration. High quality h.264 compression is utilized to enhance bandwidth utilization and AES-256 encryption provides data security (Lockheed Martin, 2012). Digital video streaming also allows Desert Hawk III to leverage data transfer via 3G and 4G cellular networks (Hemmerdinger, 2014).

Data retrieved by Desert Hawk III’s onboard sensors can be viewed live at the GCS and digitally stored on the GCS’s digital video recorder (DVR). Synced video and other data can also be stored onboard the aircraft via a removable 32 gigabyte microSD card (Lockheed Martin, 2012).  

Desert Hawk III is equipped with the Kestrel auto-pilot which provides the UAS with high-bandwidth stability and control throughout semi-autonomous flight. The Kestrel auto-pilot integrated with onboard GPS and INS technology provides the Desert Hawk III with accurate navigation, payload control, and targeting. The Kestrel system is also capable of onboard data logging. Lockheed Martin’s Virtual Cockpit 3.0 software provides Desert Hawk III operators with a user friendly interface to monitor aircraft operations and manage sensor systems (Lockheed Martin, 2012).
        
The Desert Hawk III has a maximum operational range of approximately 9 statute miles. The operational range could possibly be extended if the system were deployed as part of an ad-hoc UAS network in which multiple Desert Hawks would be deployed simultaneously with each UAS acting as a data relay node for other aircraft in the network. Deploying the Desert Hawk III in this manner would expand the platforms ISR footprint and would economize long range data transfer but would require the aircraft to be capable of bi-directional communications between sister aircraft as well as the Desert Hawk III GCS.

References
FLIR (2012). Quark: Longwave Infrared Thermal Core Camera. Retrieved from http://www.unmannedsystemstechnology.com/wp-content/uploads/2012/04/FLIR-Quark-Brochure.pdf
Hemmerdinger, J. (2014, May 13). AUVSI: Desert Hawk gains endurance, updated software systems. Retrieved from http://www.flightglobal.com/news/articles/auvsi-desert-hawk-gains-endurance-updated-software-and-398761/
Lockheed Martin (2013a). Desert Hawk III. Retrieved from http://www.lockheedmartin.com/content/dam/lockheed/data/ms2/documents/Desert_Hawk_III_brochure.pdf
Lockheed Martin (2013b). Digital IP Video/Data Link. Retrieved from http://www.lockheedmartin.com/content/dam/lockheed/data/ms2/documents/procerus/Digital_Data_Link_Datasheet_080513.pdf
Lockheed Martin (2013c). Kestrel Flight System. Retrieved from http://www.lockheedmartin.com/content/dam/lockheed/data/ms2/documents/procerus/Kestral_Flight_Systems_Datasheet_080513.pdf
Lockheed Martin (2013d). Onpoint Onboard. Retrieved from http://www.lockheedmartin.com/content/dam/lockheed/data/ms2/documents/procerus/OnPoint_Vision_Systems_Datasheet_080513.pdf

Sunday, February 1, 2015

UAS Sensor Placement

Unmanned Aircraft System Sensor Placement
Mark C. Hardy
Embry Riddle University

Unmanned aircraft systems (UAS) have the potential to serve in a multitude of civilian roles impacting a large number of industries. As UAS mission sets have developed, so too have the electronic sensor technologies necessary to support new mission requirements. Ideally, new sensor technology would be coupled with new UAS platforms designed specifically for a particular sensor and its mission. However, given the range of unmanned aircraft currently available, it is commonplace for new sensor payloads to be designed for integration with UAS platforms already in service. One key consideration of the platform selection and integration design process is the placement and positioning of the sensor payload on the UAS airframe to optimize sensor performance (Austin, 2010).  

The Flying-Cam 3.0 SARAH and its sensor payload was designed to conduct high resolution aerial video/photography and in October of 2014, Flying-Cam Inc. was granted an exemption by the Federal Aviation Administration (FAA) for use in video production on closed sets within the United States. Prior to that the 3.0 SARAH had been used abroad to conduct high definition cinematography (Flying-Cam, 2014).

The 3.0 SARAH is an electric-powered twin engine UAS in a conventional single-rotor helicopter configuration.  The 3.0 SARAH’s effectiveness for conducting the aerial video/photography mission can be attributed to the forward looking “Gyro Head 3.0” gimbal system which is prominently mounted to the device’s nose section. The Gyro Head 3.0 utilizes a high grade inertial measuring unit (IMU), along with the attitude and heading reference system (AHRS), to provide automatic horizon leveling and stabilization even in high wind conditions. The Gyro Head 3.0 is capable of a 90 degree up tilt and a -110 degree down tilt at a maximum rate of 60 degrees per second. Flying-Cam’s Body Pan system employs the 3.0 SARAH’s gyro stabilized direct drive tail rotor to provide a full 360 degree unobstructed azimuth pan capability by yawing the entire aircraft at a maximum rate of 120 degrees per second (Flying-Cam, 2014).

The 3.0 SARAH and the Gyro Head 3.0 can accommodate a wide array of non-dispensable electro-optical payloads capable of capturing high resolution still imagery and high definition video (Austin, 2010; Flying-Cam, 2014). The 3.0 SARAH’s conventional single main rotor helicopter configuration combined with the Gyro Head 3.0’s forward placement provides the system with an unobstructed field of view throughout the vast majority of the Gyro Head 3.0’s specified range of motion. Operation of the system is further aided by the 3.0 SARAH’s computer assisted piloting (CAP) software which allows missions to be pre-programmed and flown autonomously. Additionally, the system’s versatility can be further enhanced by selection of an optional all-weather performance package (Flying-Cam, 2014).

While the 3.0 SARAH and its systems are clearly suited to collect aerial imagery, the Storm Racing Drone (SRD) was built for speed and performance for the task of first person view (FPV) multi-rotor racing. Many FPV multi-rotor racing enthusiasts prefer to build their own racing UAS utilizing customizable kits. However, the SRD is an off the shelf FPV quad-copter racer built on a lightweight but highly durable carbon fiber frame.

The SRD utilizes four 2204 brushless electric motors to power its tri-blade rotors which provide exceptional acceleration and maneuverability. The entire system is powered by an 11.1 volt, 1500 milliamp lithium polymer battery giving the SRD approximately 5-8 minutes of racing endurance (Helipal, 2015).

The SRD radio control system operates at 2.4 gigahertz while the FPV system transmits at 5.8 gigahertz. Both systems offer sub channels to allow multiple UASs to operate on the same frequency in close proximity. The SRD is equipped with a forward looking camera which is mounted on top of the SRD’s main support frame and located within the protective equipment bay to prevent damage in the event of a crash. The FPV camera is positioned in a fixed-mount and provides a 110 degree field of view for the operator. The camera’s forward looking fixed position is essential in the high speed FPV environment allowing the operator to quickly and accurately determine the aircraft’s spatial orientation. The SRD’s relatively low resolution camera system allows for low latency video data transfer which lends well to the FPV experience (Helipal, 2015).

The aforementioned aircraft are equipped with sensor payloads which are ideally positioned on their respective UAS platforms to perform their specific missions. The 3.0 SARAH equipped with the forward mounted Gyro Head 3.0 offers a system capable of supporting a range of high grade camera systems while providing the requisite unobstructed field of view to collect studio quality aerial imagery. The SRD, designed for the multi-rotor FPV racing enthusiast, does not necessitate a high quality imaging system or an adjustable field of view. The SRD’s mission allows for a lower resolution video system with a fixed mount system that can transmit data to the operator’s FPV receiver in real time for enhanced spatial orientation.

References
Austin, R. (2010). Payload Types. Unmanned Aircraft Systems-UAV Design, Development, and Deployment  (pp.127-141). Retrieved from http://site.ebrary.com.ezproxy.libproxy.db.erau.edu/lib/erau/reader.action?docID=10380998

Flying-Cam Inc. (2014). Flying-Cam 3.0 SARAH. Retrieved from http://www.flying-cam.com/en/products.php?product=2

Flying-Cam Inc. (2014, October 15). Official FAA Approval for Flying-cam 3.0 SARAH. Retrieved from http://www.flying-cam.com/en/news.php?id=133

Helipal (2015). Storm Racing Drone. Retrieved from http://www.helipal.com/storm-racing-drone-rtf-type-a.html


Sunday, January 25, 2015

Unmanned Systems Maritime Search and Rescue: HUGIN 4500 Autonomous Underwater Vehicle

Unmanned Systems Maritime Search and Rescue: HUGIN 4500 Autonomous Underwater Vehicle

Mark C. Hardy

Embry Riddle University

A multi-purpose survey vessel carrying a HUGIN 4500 autonomous underwater vehicle (AUV) was recently dispatched to the Indian Ocean in hopes of locating the missing wreckage of Malaysian Airlines flight 370 (AUVAC, 2015). The harsh conditions and extreme depths in this region of the Indian Ocean have created an environment that is not conducive to human divers. Additionally, the thick layer of sediment present on the ocean floor throughout the search area has limited the performance of shipborne sensors. As a result, AUVs capable of operating at punishing depths and equipped with specialized sensor arrays are now assisting in the search effort (Saltarin, 2014; AUVAC, 2015).

The HUGIN class of AUVs is manufactured by Norwegian based Kongsberg Maritime. Three models of the HUGIN AUV are available, each classified by its depth rating (i.e. HUGIN 1000, 3000, 4500). The most capable of the HUGIN models is the HUGIN 4500 which has the ability to descend to a maximum depth of 4500 meters, or approximately 15,000 feet (Button et al., 2009).

The HUGIN 4500 has a torpedo style body type consisting of a titanium hull. The 4500 is powered by a pressure tolerant Aluminum Oxygen Semi Fuel Cell which is capable of powering propulsion and sensor systems for 60 hours at 4 knots (Kongsberg Maritime, 2009).

Communications for vehicle control and data transfer can be handled via acoustic link, radio link, WLAN, or by iridium satellite. The HUGIN 4500 is capable of operating in one of three modes; supervised, autonomous, and semi-autonomous. Supervised mode allows the operator to manage the device and/or reprogram mission parameters. Autonomous mode allows the AUV to operate independently utilizing a pre-programmed mission profile. Semi-autonomous mode is a combination of supervised and autonomous modes (Kongsberg Maritime, 2009).

An advanced aided inertial navigation system (AINS) provides position and guidance information for the HUGIN 4500. Additional proprioceptive sensors such as GPS, doppler velocity log (DVL), compass, pressure sensors, and the high precision acoustic positioning (HiPAP) system; all assist by providing relevant information to the HUGIN’s Kalman filter which then processes data from the various systems and adjusts for error utilizing sophisticated navigation algorithms. Figure 1 provides illustrates the connectivity between AINS and other interrelated systems (Kongsberg Maritime, 2009).

Figure 1. Structure of the HUGIN aided internal navigation system. From “Autonomous Underwater Vehicle, HUGIN Family” Kongsberg Maritime, 2009.

One key advantage of the HUGIN 4500 is its large and versatile payload bay. The HUGIN’s sensor array is built around a centralized Payload Processor which allows the various sensor components to function holistically (Kongsberg Maritime, 2009). The HUGIN’s ability to support the simultaneous operation of sensors, which employ differing techniques and varying capabilities, provides end users with information that is fused from multiple sources culminating in more accurate and detailed products as compared to results generated from data retrieved from individual systems themselves (Thurman, Riordan, & Toal; 2013).

The HUGIN 4500 can be equipped with a vast array of exteroceptive sensors. The following is an example of a HUGIN 4500 payload:
  • High Resolution Interferometric Synthetic Aperture Sonar (HISAS) rated to 3000 m 
  • Multibeam Echosounder 
  • Sidescan sonar 
  • Sub-bottom profiler 
  • Still image camera 
Sonar (Sound Navigation and Ranging) refers to the basic principle of using sound waves to detect and/or locate objects, typically in the underwater domain (Hansen, R. 2011). Side scan sonar is a high frequency sonar sensor which transmits waves and measures return echoes to create an image of the ocean terrain. Side scan systems can cover a wide swath of area and is commonly used for mapping and/or object detection (NOAA, 2008; Zehner & Loggins, 2004).

Synthetic aperture sonar (SAS) is based on the principle of emitting successive pulses of sound along a known track to create a large synthetic array. SAS sensors are capable of producing precise imagery with centimeter level resolution from hundreds of meters away (Hansen, 2011). Interferometric SAS further enhances SAS technology by measuring the phase difference between sonar images taken by separate arrays to produce 3D imagery (Banks, Griffiths & Sutton, 2001).

The HISAS 1030 by Kongsberg Maritime is ideally suited for search and recovery operations as this Interferometric SAS system is able to provide detailed imagery of the ocean floor and is designed to detect and classify small objects in cluttered environments, such as an underwater debris field. Moreover, the HISAS 1030 is capable of producing high resolution bathymetric information which can subsequently be used to generate 3D terrain models (Kongsberg Maritime, 2009).

The HISAS 1030 can also be used as a conventional side scan sonar, which will not provide the high resolution of SAS but will allow for an increase in the average coverage rate. Fusing bathymetric information collected by the SAS with data collected by another acoustic sensor, known as the multi-beam echo sounder, is an example of optimizing the overall data collection effort by leveraging the complementary capabilities of multiple sensors (Kongsberg Maritime, 2010; Thurman et al., 2013).

The sub-bottom profiler, which utilizes lower frequency waves to penetrate the seabed, is another acoustic sensor that the HUGIN 4500 may employ to remotely sense what lies beneath the thick layer of sediment covering the ocean floor. Lastly, an electro optical camera system capable of capturing still imagery rounds out the suite of exteroceptive sensors which one might expect to see on the HUGIN 4500 tasked with locating the wreckage of Malaysian Airlines flight 370 (Kongsberg Maritime, 2009).

While it is clear that the HUGIN 4500 is a superb platform for a myriad of underwater exploration applications, it is only one AUV operating in a vast search area. As such, it may be prudent to consider the manner in which AUVs are deployed. Research conducted at the State University of Buffalo should serve as a framework for the implementation of “cooperative search and survey” methods utilizing a fleet of interconnected AUVs operating in a coordinated search effort (Yoon & Qiao, 2008). In order to accommodate such a system, the HUGIN 4500 would require an enhanced capability to send and receive large amounts of data acoustically. It would also need to be capable of sensing and/or communicating with other AUVs operating in the vicinity so as to avoid collisions and eliminate duplicative search efforts.

Sea based search and rescue efforts could be further augmented by immediately deploying unmanned aviation systems (UAS) to search areas to begin surveying for wreckage or locating survivors in a timely fashion. UAS systems could also provide additional cueing for seaborne unmanned assets operating on or beneath the surface by directing them to areas of interest as seen from above the ocean surface.

AUVs have the unique ability to operate in environments which are hazardous to human beings. Removing the requirement for human operators allows engineers to build smaller, more cost effective vessels with a focus on developing the payload suites necessary to collect desired information. While manned vessels are capable of carrying the same equipment and conducting the same missions as their AUV counterparts, the physiological limits of the human body would drastically increase cost and decrease overall efficiency.

References:


Autonomous Underwater Vehicle Application Center (AUVAC). (2015, January 14). MH370 Searchers to Look at Hard to Reach Sea Floor. Retrieved from http://auvac.org/community-information/community-news/view/2720

Button, Curtin, Dryden & Kamp. (2009). A Survey of Missions for Unmanned Undersea Vehicles. Retrieved from http://www.rand.org/content/dam/rand/pubs/monographs/2009/RAND_MG808.pdf

Hansen, R. (2011, September 12). Introduction to Synthetic Aperture Sonar. InTech. Retrieved from www.intechopen.com/download/pdf/18868

Kongsberg Maritime. (2009). Autonomous Underwater Vehicle, HUGIN Family. Retrieved from http://auvac.org/uploads/configuration_spec_sheets/HUGIN_Family_brochure_r2_lr.pdf

Kongsberg Maritime. (2010). HISAS 1030. Retrieved from http://www.km.kongsberg.com/ks/web/nokbg0397.nsf/AllWeb/86E9FFB43569CDEEC12576B9006D75C7/$file/HISAS_1030_brochure_v1_lowres_v2.pdf?OpenElement

National Oceanic and Atmospheric Administration (NOAA). (2008). Side Scan Sonar. Retrieved from http://oceanservice.noaa.gov/education/seafloor-mapping/how_sidescansonar.html

Saltarin, A. (2014, April 3). MH370: How Can Unmanned Submarines Help in the Search of the Missing Plane. Retrieved from http://www.techtimes.com/articles/5150/20140403/mh370-how-can-unmanned-submarines-help-in-the-search-of-the-missing-plane.htm

Thurman, E., Riordan, J., & Toal, D. (2013, July). Real-Time Adaptive Control of Multiple Co-located Acoustic Sensors for an Unmanned Underwater Vehicle. IEEE Journal of Oceanic Engineering, Vol. 38, NO. 3.

Yoon, S. & Qiao, C. (2008). Cooperative Search and Survey using Autonomous Underwater Vehicles (AUVs). Retrieved from ftp://ftp10.us.freebsd.org/pub/tech-reports/2008-04.pdf

Zehner, W. & Loggins, C. (2004). Selection Criteria for UUV Sonar System. Retrieved from http://ieeexplore.ieee.org.ezproxy.libproxy.db.erau.edu/stamp/stamp.jsp?tp=&arnumber=754729&tag=1

Sunday, January 18, 2015

Bathymetric Lidar for UAS

The following is a summary of an article written by Ben Coxworth of Gizmag.com:

Bathymetric Lidar for UAS

A team of researchers at Georgia Tech University, led by Dr. Grady Tuell, are in the process of developing a bathymetric lidar system which may be small enough to be carried by an unmanned aerial system (UAS). Lidar, which stands for Light Detection and Ranging, utilizes lasers to emit pulsating beams of light which reflect off of the surface below. The lidar receiving unit then monitors the beam’s rate-of-return to accurately determine the distance from the sensor to the surface below.  By combining millions of these data points, lidar systems are able to produce precise 3D terrain models.

Bathymetric lidar systems transmit two laser beams. One beam in the infrared and another in the higher frequency green spectrum which is capable penetrating columns of water to produce 3D imagery of the ocean floor. Typical bathymetric systems are bulky and weigh in the range of 600 pounds, therefore limiting their use to manned aircraft which have the ability to handle a payload of that size. The Georgia Tech team has developed a CAD model of their system which would be about half the size of current systems. The group hopes to develop an even smaller device which could perhaps be deployed on small UAS platforms. 

The Georgia Tech system also offers significant advancements in bathymetric lidar processing speeds. Their system utilizes a computing technique called “total propagated uncertainty” (TPU) and is capable of processing up to 37 million data points per second. Comparatively, some current systems are only capable of processing approximately one thousand points per second.

As of December 2014, the system was deployed on a gantry apparatus and was being tested over a pool for further evaluation. Possible applications for the system include seaborne mine and submarine detection, under water mapping, and land management applications- as the sensor is also capable of penetrating foliage to detect objects beneath the forest canopy.
  
The continued miniaturization of sensor technologies, both exteroceptive and proprioceptive, lends well to future implementation of UAS and will ultimately lead to increased efficiencies and cost reductions across a wide array of industries.  




Reference:
Coxworth, B. (2014, December 8).  UAVs Could Map The Bottom of the Sea, Using New Lidar Tech. Retrieved from http://www.gizmag.com/uav-bathymetric-lidar/35113/


NOAA (2014). Lidar. Retrieved from http://www.nauticalcharts.noaa.gov/hsd/lidar.html