Posts

Sense and Avoid for Small Unmanned Aerial Systems

Image
Aerotenna’s uSharp Sense-and-Avoid Radar is a commercially available sense and avoid solution for small Unmanned Aerial Systems (sUAS).   The uSharp radar utilizes microwave radar in the K-band (24 GHz) frequency range to detect objects in the horizontal plane within a full 360 degrees (Aerotenna Technology, n.d.).   The use of microwave radar as opposed to commonly utilized sensors such as electrooptical (EO) and infrared (IR) cameras or Light Detection and Ranging (LiDAR), enables a sUAS equipped with uSharp to sense and avoid other objects in all weather (heavy rain, fog, snow, clouds) and visibility conditions (night or low light levels) (Aerotenna Technology, n.d.).   The radar consists of eight antennas arranged in an octagonal formation and has a highly accurate range resolution of five centimeters and a response rate of 90 Hertz (Aerotenna, 2016).   It also has a range of 0.5 to 120 meters and can detect both moving and stationary objects (uSharp, 2017).   The ability to de
Post a Comment
Read more

Unmanned System Control Station Analysis

Image
iRobot’s uPoint Multi-Robot Control (MRC) software is an Android based application for touch screen tablet devices, capable of running any Unmanned Ground Vehicle (UGV) (also referred to herein as “robot”) manufactured by iRobot (iRobot, 2014).   The software runs on any Android based handheld tablet device and offers an intuitive touch screen interface designed for ease of use during “high-stress, critical operations” according to Frank Wilson, senior vice president and general manager of the Defense and Security Unit at iRobot (iRobot, 2014).   The use of an application as opposed to a propitiatory hardware based controller takes advantage of the computing power of modern mobile devices and enables the end user to select the Android tablet that most suits their need or application.   Additionally, it empowers the user with an all-in- one device (UGV control, business software, email, mapping tools) that is cost effective to upgrade or replace compared to traditional bulky and exp
1 comment
Read more

Unmanned System Data Protocol and Format

Image
This week’s blog will focus on DJI’s Inspire 2, a professional quality quadrotor Unmanned Aerial System (UAS) designed for filmmakers.   The UAS is capable of speeds of up to 58 mph and has an endurance of 27 minutes through the use of dual, redundant, battery packs (Inspire 2, 2017).   In addition to an integrated first-person view (FPV), 2 axis, 4K camera built into the nose, the aircraft can support a variety of 360 degree rotatable gimbled film cameras, including DJI’s top of the line Zenmuse X7, capable of recording in 6K CinemaDNG at 30 frames per second (fps) or 5.2K Apple ProRes video formats at 30 fps; both formats are capable of 60 fps rates at reduced quality (Inspire 2, 2017; Zenmuse X7, 2017).   The Zenmuse X7 camera is also capable of capturing DNG RAW 20 fps (burst mode) still photos at 24 megapixels (MP), as well as the commonly used JEPG format (Zenmuse X7, 2017).     DJI. (2017). Inspire 2 [digital image]. Retrieved from https://www.dronethusiast.com/wp-content
2 comments
Read more

UAS Sensor Placement: A Comparison

Image
Unmanned Aerial Systems (UAS) rely on onboard sensors for navigation and to provide an operator or ground observer with imagery captured by the aircraft.   The selection and placement of these sensors is dependent on the design of the aircraft and the intended purpose.   Today’s blog will examine sensors utilized for DJI’s Phantom 4 Pro, a UAS designed for aerial photography, and ImmersionRC’s Vortex 250 Pro, a UAS designed for first person view (FPV) drone racing. DJI Phantom 4 Pro DJI. (2017). Phantom 4 Pro [digital image]. Retrieved from https://www.dji.com/phantom-4-pro DJI’s Phantom 4 Pro is an excellent UAS for obtaining high definition aerial video and still images at altitudes below 400 feet.   The quadrotor UAS is capable of flying for up to 30 minutes at speed s up to 31 mph while shooting 4K/60 fps video or still imagery, all while avoiding obstacles (Phantom 4, 2017).   The quadrotor design of the aircraft, as opposed to the use of a traditional fixed win
1 comment
Read more

Unmanned Systems Maritime Search and Rescue: IVER3 AUV

Image
OceanServer. (n.d.). IVER3 [digital image]. Retrieved from www.iver-auv.com/AP_003_Iver3.pdf The US Navy deployed three IVER3-580 Autonomous Underwater Vehicles (AUV) to Argentina during the recent search for the Argentine submarine A.R.A. San Juan, missing in the South Atlantic Ocean since November 15th (US Navy, 2017).   The IVER3 AUV can operate at depths of up to 100 meters at speeds of up to 4 knots for up to 14 hours (Overview, 2017).   Active search sensors include a side scan sonar, which is utilized to obtain an image of the ocean floor to detect objects, and a swath bathymetry sonar to map the depth of the ocean floor below the vehicle (IVER3, n.d.).   Navigational sensors include GPS for fixing the vehicle’s location on the surface, a doppler velocity log (DVL) and inertial navigation system (INS) for determining vehicle location under the surface, and a forward-looking echo sounder in the bow for collision avoidance (IVER3, n.d.).   The vehicle can also be e
1 comment
Read more

Combining exteroceptive sensors aboard a UGV

Image
This initial blog post will discuss the use of exteroceptive sensor systems aboard the Daredevil PackBot unmanned ground vehicle (UGV) to navigate in all weather conditions, as outlined in Yamauchi’s paper titled “Fusing Ultra-Wideband Radar (UWB) and LIDAR for Small UGV Navigation in All-Weather Conditions.” Yamauchi’s research into the use of UWB radar and LIDAR builds upon previous research that showed UWB radar was capable of detecting objects “in a snowstorm, through dense fog, and through sparse foliage” (2010).   However, the use of radar is not as accurate as LIDAR or visual navigation techniques, reducing the speed at which the UGV can operate.   The use of a calibrated max filter algorithm addressed issues experience differentiating between ground clutter returns and obstacles, but remained unable to match the accuracy of LIDAR or stereo vision sensors.   Yamauchi, Brian. (2010). Daredevil Packbot [digital image]. doi: 10.1117/12.850386 Advantages to the incorporati
2 comments
Read more