Sense and Avoid: LOAWS

Unmanned Aerial Vehicles (UAV) require for low-level flights activities, sensors that can effectively operate in areas where natural and man-made obstacles are present. The sensors must be reliable even when meteorological conditions do not make possible for the human eye to differentiate between obstacles or to even identify the obstacle, being this the ultimate purpose of the sensor. The sensor(s) to be used under such circumstances have to be able to detect all types of hazardous obstacles, including topographic features, vegetation, buildings, poles/masts, towers, cables and transmission lines. It must count with satisfactory technological readiness levels. A high range and bearing resolution will be indispensable and high minimum detection range will be adequate for the platform velocity and dynamic performances.

A sensor that can meet such requirement could be the LIDAR Obstacle Warning and Avoidance System (LOWAS), which has become a mature technology that small-to- medium size UAV’s resource to when their operations require close proximity to the ground.  LOWAS is of preference when warning and avoidance of obstacles is necessary. It is important to mention that LIDAR is an exteroceptive sensor. When we talk about sensors, it is essential to differentiate between proprioceptive and exteroceptive sensors. Proprioceptive sensors are those that measure internal state of a system, it could be battery level or even the performance of gyroscopes. On the other hand, exteroceptive sensors measure the external state of systems, this could be temperature, meteorological conditions, or detection of objects between others.

LOAWS is suitable for this mission because it able to detect obstacles placed in the UAV route of flight or nearby the operation area. LOWAS has the capability to classify and the same time prioritize the obstacles that were detected. It can also provide visual and aural warnings and information to the crew (Ramasamy, 2016). Therefore, LOWAS counts with three key algorithms: prediction of the future platform trajectory; calculation of the potential collisions with the detected obstacles; and generation of a set of optimal avoidance trajectories (in case a risk of collision is determined) (Ramasamy,2016). These algorithms are the key to how LOWAS will perform and also help to kind of predict how the UAV will behave in circumstances not favorable for the flight.

LOWAS goes beyond meeting the requirements.  It counts now with a cognitive remote pilot–aircraft interface that is being developed to dynamically assist remote pilots based on their physiological and cognitive states detected in real-time (Ramasamy, 2016). The purpose of this is to create a cooperation between pilots and the systems. I definitely recommend this system for warning and avoidance of obstacles, specially after working on the human and machine cooperation that will benefit the result of the mission to be carried by the UAV.

LOWAS also offers three levels of alerts.  They are: warning, caution, and advisory. These alerts help the remote pilot to prepare or be aware of the upcoming obstacles that will have to be evaded. These alerts come in the form of digital voice outputs and tone or they can also be displayed like is done in the chart presented below.

The article I have based my post about can be found in this website
https://www-sciencedirect-com.ezproxy.libproxy.db.erau.edu/science/article/pii/S1270963816301900

References

Kohanbash, D. (2014). Sensor types (modalities) for robots to experience the world. Robots for Roboticits. Retrieved from
http://robotsforroboticists.com/sensor-modalities/

Ramasamy, S. (2016). LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid. Science Direct. Retrieved from
 https://www-sciencedirect-com.ezproxy.libproxy.db.erau.edu/science/article/pii/S1270963816301900

Newcomb, D. (2017). Velodyne LIDAR for Safety's Sake. Cthreegroup. Retrieved from
 http://cthreegroup.com/velodyne-lidar-for-safetys-sake/

Cargo Delivery Drones for Humanitarian Aid

Humanitarian Aid

Unmanned Aerial Systems (UAS) has positively revolutionized sectors like the military in terms of how they conduct certain operations. It also has provided the entertainment industry with new ways to capture images and record videos essential to films and tv series. But one of the most important, is how unmanned systems has revolutionized the way humanitarian groups can help on the aid of people in distress. The outcome of the missions depends on the sensors installed in UAS like drones. This blog will focus on the state of the art in sensor technology for humanitarian aid, the sensor requirements for this mission, the existing sensors and processing approaches used in humanitarian aid, the technical challenges that have to be overcome in order for this application to succeed, as well as the technologies and operational processes that are being developed that would enable this application.

Humanitarian aid has been benefited by how drones can serve to delivery cargo. It is essential for those living and working in areas where catastrophes are taking place, to receive the resources needed for them to survive and to go back to normality.  Specific needs require specific UAS’s, and this translate to specific sensors. Sensors are brain and heart of those machines and in order to operate and provide the best service possible, it is important to count with the best sensors available.

Sensors

For cargo delivery drones to help on humanitarian aid, they need to be equipped with SONAR sensors, stereo-vision cameras, and LIDAR technology.  According to Casey Coombs, it is also required to count with optical sensors and radio transmitter “In order to activate flaps, parachutes or compressed air canisters built into the package to alter the vertical descent path of the package to avoid obstructions, such as trees, or other structures such as carports, balconies, power lines, eaves, etc." These features are very important because in areas of difficult access like places where natural disasters took place, it is important to deliver medicines that may require to be handle with care or the containers are sensitive to impacts. Those are the cases of vaccines or first aid supply.

Challenges

Cargo drones regardless of its acceptance and the tremendous service they, they still present technical challenges that must be overcome for this application of humanitarian aid to succeed. Three of the most challenging are collision avoidance, obstacle detection, and weight.

For drone operators it is not only important to avoid a collision with other drones, property, and vehicles, but also with people. As a result of drones being unmanned, no human operator on board, it is very difficult to stay clear of collisions. Cargo delivery drones must be able to fly long distances out of sight of a human operator (Braasch, 2016). Regardless if drones are equipped with cameras that provide video transmission, this is limited a range of a few miles. This the case of civil drone video transmission technology (Braasch, 2016).

The success of the mission also depends on how drones will detect obstacles. It is important to differentiate between people, infrastructure, and any other natural or animal obstruction. Not only the drone can result in getting damage as a result of the failure to detect any obstruction, but also the content it carries. Imagine you are living in a place that has become almost inaccessible due to a hurricane and the only way your diabetic child can receive his insulin is through the drone. If the drone fails to detect the obstacles on its way, unfortunately this child won’t be able to receive his medication on time and this could lead to complications. The examples are endless.

Another challenge cargo delivery drones used for humanitarian aid face, is its weight limitation.  The challenge is influenced by several factors like motor power, propeller size, number of propellers, battery type, frame weight, and the UAV’s operating altitude above sea level (Aitken, 2015). But probably the biggest problem is battery capacity. For battery powered drones it is essential to count with enough power to fly not only from A to point B but also to provide power to the propeller thrust needed to counter the force of gravity.

Possible Solutions

A possible solution to take into consideration for the collision avoidance and obstacle detection problems is to consider developing autonomous drones. Drones capable of making decisions without the need of a remote pilot.  In areas where weather conditions do not permit the cameras to show a clear view of the area or after loosing track or contact with the drone, still it will be possible to count with a drone that will do the job and reach its destination without problems.

It is also recommended to resource to the drone with the best obstacle avoidance system, the new DJI Mavic Pro 2 and Mavic 2 Zoom. Two of the most relevant features of the Mavic 2 is that it can fly not only behind or in front of obstacles but also around them, it also counts with obstacle detection all 6 sides of the drone (Corrigan, 2018). In the case of the DJI it counts with new cameras, superb stabilization, 5 directions of obstacle sensing and 4 directions of obstacle avoidance, which is outstanding. It also has many intelligent flight modes, super smooth stability and top 4k camera (Corrigan, 2018). The solution for the weight problem will come, when the power source of drones become smaller to the point the weight of the energy source won’t become a weight problem.  The weight that previously was occupied by the energy source will then be used for cargo.

Technology it is also about perfectionating and looking to the future for better options to offer. Humanitarian aid cargo drones are not the exception. The tremendous help they provide will inspire more scientist, engineers, and UAS’s enthusiast to look for better solutions for the problems encountered.

Sources:

Aitken, Ricardo. (2015). How much weight can delivery drones carry? Unmanned Cargo. Retrieved from http://unmannedcargo.org/how-much-weight-can-delivery-drones-carry/

Braasch, M. (2016). Obstacle avoidance: The challenge for drone package delivery. The Conversation. Retrieved from                                                                                                          http://theconversation.com/obstacle-avoidance-the-challenge-for-drone-package-delivery-70241

Coombs, C. (2017). Amazon patents fly-by drone package delivery technology (Images). Puget Sound Business Journal. Retrieved from                      https://www.bizjournals.com/seattle/news/2017/02/15/amazon-patents-fly-by-drone-package-delivery.html

Corrigan, F. (2018). 12 Top Collision Avoidance Drones And Obstacle Detection Explained. Drone Zon. Retrieved from                                                                                                               https://www.dronezon.com/learn-about-drones-quadcopters/top-drones-with-obstacle-detection-collision-avoidance-sensors-explained/

           

ALTI UAV Ground Control Station

When we look at the sky and we see airplanes flying over our houses, some of us think that those metal birds are being flown by pilots seating in their cockpit reading charts, communicating with air traffic controllers, or even drinking coffee. In today’s world, the way airplanes are being flown is changing, and one of those changes is that now we count with Unmanned Air Vehicles (UAV). UAVs as its name indicates, does not count with a pilot in the cockpit but with a ground control station were the decisions made by remote pilots affect positively or negatively how the UAV will perform or behave. The ground control station of UAVs varies depending on the characteristics and use of the vehicle, some more complex than others.

ALTI, one of the world leaders in unmanned vertical take-off and land (VTOL) aircraft manufacturers and developer company, has among its fleet of UAVs the ALTI Transition. The ALTI Transition counts with a Ground Control Station (GCS) unique in its type. It is a complete control and command station as well as a stand-alone aircraft control camera gimbal and sensor control unit (ALTI, n.d.). 

The frame and power life

The GCS is housed by a lightweight pelican ‘air’ case that includes a high-quality intel computer, relevant mission planning, and operation software (ALTI, n.d.). These features are very convenient for the transportation of the GCS as well as for the maneuvering and control of the ALTI Transition UAV. Two of the biggest advantages of the ALTI GCS is that it can be set up with multiple power options including hot swappable batteries. The result, the operations can last for extended periods of time.

Long Range Data and Video

ALTI GCS counts with advanced long-range aircraft data telemetry and video link, as well as Multiple Input Multiple Output (MIMO) technology which includes (ALTI, n.d.):

·         High quality Intel NUC computer

·         Dual screen design layout

·         15.6” LED HD display monitors (allows for 160°/160° viewing angles)

·         Digital and video link powered by Microhard MIMO 2.4Ghz pMDDL2450 ENC radio systems

·         Up to 62 miles of long-range data and video features

·         Backup data telemetry link

·         Long range control link

·         Main digital data link (flight planning and telemetry)

·         MicroHard radio modem system

·         Encryption

·         Control links

·         Any payload/HD video software the client has opted to use with ranges of up to 70km

Communication between the GCS and the UAV is essential for the success of the mission carried.  Communication is maintained thanks to a radio controller for aircraft control, power packs, antennas, and all extras and accessories (ALTI, n.d.). 

Long Range and Control Link

According to ALTI’s official website, “ALTI GCS counts with a long-range low frequency C2 control link for full time LOS control and override of the aircraft.” This means that the command and control of the UAV can be well handled from long distances without being interrupted by environmental changes.

Visual Display

ALTI GCS counts with a high-quality intel computer with dual HD display (ALTI, n.d.). This is of great help for remote pilots/operators because it will be displaying better images with details of where the UAV is located. The visual display can help on the decision making of the mission carried. The GCA is also loaded with mission planning, payload, and control software that helps the UAV to be ready to fly at any moment (ALTI, n.d.).

As a result of ALTI giving its costumers the option to select their prefer software, it could be recommended to use the Visionair GCS software from UAV Navigation, a company specialized in the design of flight control solutions. Visionair is perfect for the planning and execution of UAV missions, specially because it is compatible with fixed wing UAV like it is the case of the the ALTI Transition UAV. Some of its feature includes (UAV Navigation, n.d.):

·         Mission-oriented interface.

·         Fully featured UAV mission planning and execution application, with GUI.

·         Platform: PC/laptop (with keyboard + mouse).

·         Suitable for fixed-wing, helicopter or multi-rotor UAVs and targets.

·         Main Flight Modes: Auto, Manual, Fly-To, Hold (Hover), Safe (Return To Base).

·         Fully Auto Take-Off.

·         Fully Auto Landing.

·         Camera Control Modes: Air, Ground, Geo, Pilot View, Stow, Rate (with integrated video display).

·         Waypoints: 100 max.

·         Range: Unlimited.

·         Altitude: Unlimited.

·         Full DEM support.

·         Link Quality panel.

·         Target Editor.

·         Fly/No-fly Zones.

·         Rulers.

·         Full range of configurable alarms.

·         Compatible with all kinds of digital mapping.

Determining the right hardware for a GCS depends on the physical requirements the UAV will be subject to and the computing requirements the GCS needs to provide in order to collect accurate data. The following charts presens different GCS options to take into consideration when purchasing a hardware. 

Small UAVs GCS can be subject to be stolen, lost, or damaged. A possible solution to this as well as for the rugged hand-held controllers is to take advantage of the new era of automation. Taking some of the control functions from the GCS and providing the UAS with more autonomy could be a good solutions (Nace, 2013).  

Sources

ALTI. n.d. ALTI Ground Control System. ALTI. Retrieved from https://www.altiuas.com/ground-control-station/

Nace, R. (2013). Communication hardware for Universal Ground Control Stations: Narrowing the field to AdvancedTCA. XTAC&CompactPCI Systems. Retrieved from www.lcrembeddedsystems.com/wp-content/uploads/2016/01/CompactPCI-AdvancedPCA.pdf

Press. (2018). ALTI UAS Launches Complete UAV Ground Control and Command Station Inbox x. Suas News. Retrieved from                                           https://www.suasnews.com/2018/07/alti-uas-launches-complete-uav-ground-control-and-command-station-inbox-x/

Reagan, J. ALTI Launches New UAS Ground Control System. Drone Life. Retrieved from https://dronelife.com/2018/07/27/alti-launches-new-uas-ground-control-system/

UAV Navigation. (n.d.) GCS Software. UAV Navigation. Retrieved from  https://www.uavnavigation.com/products/gcs/gcs-software

EAARL, GIS, and IMA Data Processing Options for LIDAR

Since its beginning, LIDAR has changed the course of technology thanks to the numerous advantages it provides. The data processing options of LIDAR play a significant part in the results obtained. LIDAR counts with different systems like EAARL, GIS, and IMA whose data processing options bring advantages but also disadvantages to its users.

EAARL and LIDAR

Experimental Advanced Airborne Research Lidar (EAARL) is a light weight low-power sensor used on light aircrafts.  Unfortunately, because of the unique nature of the system, off-the-shelf software is not available for EAARL data processing, feature extraction, and image display (Hansen, 2008). Another disadvantage of EAARL according to Wright, lead investigator for NASA's Experimental Advanced Airborne Research Lidar (EAARL) system at Wallops Flight Facility on Virginia's eastern shore, is that “EAARL is uniquely able to make measurements over ground that varies tremendously in reflectivity and complexity.”

EAARL bright side is that compared to other LIDAR’s that tune for either weak or strong signals, EAARL can capture them all because it makes frequent measurements -- about four billion per second using multiple detectors (NASA, 2004). This trait makes it perfect for tracking hurricane damage in coastal areas, which are made up of water, sand and plant life (NASA, 2004). Other advantages of EAARL include its flexibility for charting storm damages, it does not require to be re-calibrated for every project and every sort of terrain (NASA, 2004).

GIS and LIDAR

GIS is one of the most commercially available software packages, it can be used to import and export LIDAR data. Unfortunately, GIS is limited in the number of points that can be handled at any one time but there is still hope (Barnwell, 2009).    Many LiDAR practitioners have developed proprietary software to handle the data volumes, and commercial GIS and photogrammetric software developers are beginning to address the problem (Barnwell, 2009). GIS in future years will have the advantage of making easer for service providers and clients alike to manipulate LIDAR data (Barnwell, 2009). Another advantage is that the data is acquired, processed and clients alike to manipulate LIDAR data (Barnwell, 2009).

IMA and LIDAR

Infrastructure Mapping and Autonomy (IMA), has the advantage of leveraging infrastructure developed for Civil Maps (Infrastructure Mapping, n.d.). This allows to process the data collected swiftly and accurately when the processing of LIDAR data is time consuming and hardware intensive (Infrastructure Mapping, n.d.). IMA benefits its customers through:

·         faster data processing

·         lower data creation cost and higher ROI on LiDAR

·         detailed, highly accurate maps of project areas

·         reduced landowner interaction

·         safe, secure and non-invasive data collection

·         rapid turnaround time with regular updates

·         easy asset inventorying and change detection

Sources

Barnwell, Charles. (2009). LiDAR for Terrain Mapping on the Alaska Pipeline Corridor. Arlis. Retrieved from http://www.arlis.org/docs/vol1/AlaskaGas/Paper/Paper_OFC_2009_LiDAR_TerrainMapping.pdf

Hansen, Mark. n.d. Louisiana Barrier Island Comprehensive Monitoring Program. Pontchartrain Institute for Environmental Sciences. Retrieved from https://www.lacoast.gov/reports/project/BICM4_part1-Lidar%20Systems%20and%20Data%20Processing%20Techniques.pdf

Infrastructure Mapping. n.d. Infrastructure Mapping and Autonomy: A New Era in Mapping. Infrastructure Mapping. Retrieved from                                  https://www.infrastructuremapping.com/

NASA. (2004). LIDAR: In the Wake of the Storm. NASA. Retrieved from https://www.nasa.gov/missions/earth/f_lidar.html

Wright, Wayne. (2016). Depth Calibration and Validation of the Experimental Advanced Airborne Research Lidar, EAARL-B. Coastal Education Research Foundation. Retrieved from http://www.jcronline.org/doi/pdf/10.2112/SI76-00

Research Blog: Data Volume Challenges

It is important for Unmanned Autonomous Systems to find ways to decrease the amount of data that their sensors accumulate over time.  Many vehicles due to the limited space and power they possess, need to discard the data not longer used.

One data treatment method to consider by users and developers of sensors for Unmanned Autonomous Vehicles is the one suggested by Andrew Christopher Tebay. According to Mr. Tebay, “The data transfer system can include a first sensor associated with the unmanned vehicle for collecting a first data set, and a second sensor associated with the unmanned vehicle and for collecting a second data set, wherein the second data set is associated with the first data set by a predetermined association.” Tebay also suggest to use a transceiver that will transmit data from the unmanned vehicle to a remote data storage system (Tebay, 2011). Another important element to consider will be a mass data storage computer located on the unmanned vehicle.  This mass data storage computer will be able to store the first and second data sets based on the predetermined association (Tebay, 2011).  This same computer will also have the job to execute the requests from the remote data storage system and transmit the first data set from the unmanned vehicle to the remote data storage system (Tebay, 2011). Other important functions will include the transfer of selected ones of the second data set from the unmanned vehicle to the remote data storage system (Tebay,2011).

The following pictures are diagrams of the suggested method. 

A second data treatment method to consider could be one suggested by Tiffiny Rossi on her article, Commercial drones bring valuable data but where is it stored? Imagine your sensor data storage unit like the sim card in your phone. You always have the option to delete the data that you do not need any longer or you could simply buy a sim card with more space. Apply this idea to the sensor data storage unit of any Unmanned Autonomous Systems. According to the general manager and vise-president of Lexar, Wes Brewer, “8K resolution video, multiple sensor data, and 360-degree video capture will drive the need for greater performance and capacity in removable storage. SD cards have several advantages over embedded drone storage, including scalable capacity, higher-quality image storage, and lower-power data transfer.”

Tuxera, a company that creates quality-assured software for world-leading companies across the world to help store data, has focus their attention now on how to help the developers and operators of sensors used by Unmanned Autonomous Vehicles. Tuxera offers a software that according to Miss Rossi, “gives some added benefits alongside those brought on by SD cards including fail-safe data integrity in the event of power loss or crash, no longer SD card lifetime, fast data transfer speeds, and low write latency when saving data to the SD card."

The more commercial drones are used for commercial applications, the more will be required to count with drones equipped with a storage unit capable of handling so much data. The method proposed by Mr. Tebay as well as the software offered by Tuxera, suggest good solutions to the problem of the amount of data that sensors accumulate over time.

References

Stockton, S. G., & Tebay, A. C. (2016, March 8). United States Patent No. US 9282144B2.
Retrieved from https://patents.google.com/patent/US9282144B2/en

Rossi, Tiffiny. (2016). Commercial drones bring valuable data – but where is it stored? https://www.tuxera.com/blog/commercial-drones-bring-valuable-data-but-where-is-it-stored/

Data Volume Challenges-Module 5