Sense and Avoid Technology

Sense and Avoid Sensor Selection

by

Stanley D. Pebsworth

Embry-Riddle Aeronautical University

May 2016

A Research Project Submitted to the Worldwide Campus in partial fulfillment of the requirements for course UNSY 605, Unmanned Systems Sensing, Perception, and Processing

Sense and Avoid Sensor Selection

             In the area of Sense and Avoid (SAA) for small unmanned aerial systems (sUAS) less than 55 pounds, there are few options to consider.  Many sUAS companies such as DJI are producing their versions of SAA technology in the new Phantom 4 (Amato, A., 2016).  The DJI SAA system offers two cameras that sense the area around the sUAS and determine if the sUAS will maneuver around an object or stop and wait for operator input (Amato, A., 2016).

            In order for a system to properly SAA, you must first determine how to best develop situational awareness (SA) of the environment around that particular system.  SA is described as the data collection, management, and dissemination that develops awareness of one’s surroundings (Erwin, T., 2015).  So why is it important?  Besides safety, SAA will be required to fully integrate all UAS into the National Airspace System (NAS).

            Mid Air Collision Avoidance System (MIDCAS) is a European project that uses sensor fusion to best tackle the job of SAA.  With the integration of Automatic Dependent Surveillance-Broadcast (ADS-B) transponders and electro-optical (EO) and Infrared (IR) sensors, MIDCAS has been successfully flight tested in Italy relying on fusion of non-cooperative sensors (European Defense Agency, 2015).  MIDCAS provides SA to UAS pilots, traffic avoidance or self-separation, and collision avoidance (Pellebergs, J., 2010).

            The technology that will make sUAS safe for our civilian skies will be a combination of integrated systems that provide the best SA to the operator.  The fusion of ADS-B and EO/IR sensors is the best current solution.  ADS-B systems have been developed that weigh approximately 3.5 ounces and EO/IR sensors have been developed that weight approximately 18 ounces (GCN.com, 2013) (Zarandy, A., Zsedrovits, T., Nagy, Z., Kiss, A., & Roska, T., 2012).  This light weight means that integration into sUAS weighing less than 55 pounds is feasible.  The MIDAS project developers plan to keep their design well contained as UAS systems come in all shapes and sizes from Global Hawk to ones that fit into your palm (GCN.com, 2013).

The real challenge is not the systems but regulatory issues.  Once SAA requirements are well defined by the FAA, systems can be designed that meet regulatory demands.  It is a difficult concept to grasp and one that is even harder given no boundaries or regulation to stay within.  Further research should be done that addresses the full spectrum of aviation for SAA and not just UAS.  The capabilities of electronically assisted SAA could prove beneficial to both manned and unmanned aircraft.

References

Amato, A. (2016). Why the DJI Phantom 4 may be the drone you are looking for. Retrieved from http://dronelife.com/2016/03/02/why-the-dji-phantom-4-may-be-the-drone-you-are-looking-for/

Erwin, T. (2015). Sense and avoid. Retrieved from http://www.harrisgeospatial.com/Company /PressRoom/Blogs/ImagerySpeaksDetail/TabId/901/ArtMID/2927/ArticleID/14506/Sense-and-Avoid.aspx

European Defense Agency. (2015). MIDCAS demonstrates progress for RPAS integration into civil airspace. Retrieved from https://www.eda.europa.eu/info-hub/press-centre/latest-news/2015/04/30/midcas-demonstrates-progress-for-rpas-integration-into-civil-airspace

GCN.com. (2013). The tech that will make drones safe for civilian skies. Retrieved from https://gcn.com/Articles/2013/07/12/Drone-UAV-sense-and-avoid-technologies-civilian-airspace.aspx?Page=2

Pellebergs, J. (2010). The MIDCAS project. Retrieved from http://www.icas.org/ICAS_ ARCHIVE/ICAS2010/PAPERS/821.PDF

Zarandy, A., Zsedrovits, T., Nagy, Z., Kiss, A., & Roska, T. (2012). Visual sense-and-avoid system for UAVs. Paper presented at the 1-5. doi:10.1109/CNNA.2012.6331447

Beware of Automation

    Automation has been used throughout history to reduce the equipment operators workload.  The use of autopilots on planes, and cruise control on vehicles was designed to reduce workload during operation.  There are 3 levels of automation as compared by Prinet, J. C., Terhune, A., & Sarter, N. B. (2012), and consist of manual, intermediate, and full automation.  Manual control is considered to simply be a state in which there is no automation.  The system is under complete manual control of the operator.  This type of control is best suited for simple tasks in reduced workload environments.  Performance suffers, but there is a reduction is loss of equipment due to automation failures.  Intermediate control can be defined as control in which there is both manual and automated control happening simultaneously.  This was the preferred level of control over full automation (Prinet, J.C. et al, 2012).  Full automation is control of a system in which there is no operator control or influence in operation (Prinet, J.C. et al, 2012).

Except for the UAV losses, full automation resulted in the highest performance on target detection and re-planning tasks combined. Still, participants overall preferred the intermediate LOA. This suggests that their preference was a combined function of performance and trust, both of which ranked between full automation and manual mode, as well as the potential consequences of automation failures. The loss of even one UAV can jeopardize the operator’s ability to successfully complete the mission. Only during high workload, when it was extremely difficult to divide their attention between the re-planning and target detection tasks, did participants prefer full automation (Prinet, J.C. et al, 2012)

    Automation has both its advantages and disadvantages in performance.  Automation is not always reliable; it infrequently fails due to hardware or software issues or simply does not perform as desired or expected.  Without a doubt, in non-failure automation of systems, automated performance exceeds human performance and reduces workloads.  However, when automation fails to properly perform; the results are often catastrophic (Onnasch, L., Wickens, C. D., Li, H., & Manzey, D., 2014).

These catastrophic effects may result from human’s reduced monitoring of highly reliable automation at the time it fails, trusting it too much (Parasuraman & Riley, 1997) and losing situation awareness (Endsley & Kiris, 1995). This is sometimes described as a form of complacency (Parasuraman, Molloy, & Singh, 1993) or an automation-induced decision bias (Mosier & Skitka, 1996). Indeed, operators occasionally over-rely on automation and exhibit complacency because the highly (but not perfectly) reliable automation functioned properly for an extended period prior to this first failure (Parasuraman et al.,1993; Parasuraman & Manzey, 2010; Yeh, Merlo, Wickens, & Brandenburg, 2003) (Onnasch, L. et al, 2014).

    When the operator is taken out of the loop with full automation, the possibility for failure and complacency increase.  To get the most from automation, the operator must use automation for its intended purpose and that is to reduce operator workload doing mundane tasks and focus their attention to more complex cognitive tasks (Onnasch, L. et al, 2014)

    There has been a new focus on flight deck automation to be more supportive due to pilots misdiagnosis of automated flight information and automated warnings.  It is suggested that the interaction between human and automation must start well before a failure occurs and recovery is solely dependent on the quick and accurate intervention of the operator (Geiselman, E. E., Johnson, C. M., Buck, D. R., & Patrick, T., 2013).

    Any system where automation is to be used must embrace both interface design concepts as well as effective operator training to access the benefits of automation in both workload and safety.  The goal should be to improve the interface between human and machine in order to reduce error and the compounding effects of automated surprise and confusion that have the potential to lead to catastrophic conclusions (Geiselman, E. E. et al., 2013).

Reference:
Geiselman, E. E., Johnson, C. M., Buck, D. R., & Patrick, T. (2013). Flight deck automation: A call for context-aware logic to improve safety. Ergonomics in Design: The Quarterly of Human Factors Applications, 21(4), 13-18.
Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human Factors: The Journal of Human Factors and Ergonomics Society, 56(3), 476-488. doi:10.1177/0018720813501549
Prinet, J. C., Terhune, A., & Sarter, N. B. (2012). Supporting dynamic re-planning in multiple uav control: A comparison of 3 levels of automation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 423-427.

General Atomics Advanced Cockpit Ground Control Station

 

General Atomics Advanced Cockpit Ground Control Station

by

Stanley D. Pebsworth

Embry-Riddle Aeronautical University

May 2016

A Research Project Submitted to the Worldwide Campus in partial fulfillment of the requirements for course UNSY 605, Unmanned Systems Sensing, Perception, and Processing

General Atomics Advanced Cockpit Ground Control Station

            General Atomics Advanced Cockpit Ground Control Station (GCS) is designed for use with Unmanned Aerial Systems (UAS) and offers the pilot significantly improved situational awareness and a reduced pilot workload.  The pilot centered display offers improved synthetic 3D video and moving maps via a 270^o wrap around field of view via multiple screens.  The enhanced situational awareness comes from a fused data link through Link 16 and Blue Force Tracking into a Common Operating Picture.  This data link integration offers collision avoidance, terrain avoidance, and special use overlays.  This GCS offers a truly ergonomic design that improves pilot comfort and increases efficiency with intuitive controls and displays (General Atomics, 2015).

            This GCS has been validated by the US Air Force and the National Institute for Aviation Research.  This GCS also complies with MIL-STD-1472 and uses the STANAG 4586 architecture which facilitates interoperability with various remotely piloted systems (General Atomics, 2015).  On November 15, 2012, General Atomics flight tested the Advanced Cockpit GCS by successfully flying a Predator C Avenger UAS.  This test was conducted to show that the wrap around ergonomic design enhances safety and improves pilot reaction time and decision making allowing the pilot to effectively and efficiently accomplish the mission (UAS Vision, 2013).

            The General Atomics Advanced Cockpit GCS uses Real-Time Innovations (RTI) middleware (RTI, 2007).

            RTI middleware is part of the software communications architecture that GA-ASI has implemented for its Advanced Cockpit GCS. The GCS makes use of RTI's publish-subscribe communications model, which allows any system component to subscribe to the incoming aircraft telemetry stream for such parameters as latitude and longitude, pitch, roll, and airspeed parameters (RTI, 2007).

RTI develops and integrates software for real time applications.  They are the experts in coupling communications with the highest performance middleware technology.  RTI technology has been used in industries such as defense, intelligence, simulation, transportation, and communications.  RTI is a privately held company in Santa Clara California and was founded in 1991 (RTI, 2007).

            Current issues and challenges faced by UAS operators is the loss of fidelity when operating a remotely piloted aircraft.  Without force feedback in the controls, and multi-sensory perception, it is difficult for the pilot to sense the remote aircrafts performance.  Future technology developed by General Atomics should include a higher level of fidelity, much like an actual aircraft simulator, to further improve the pilot’s situational awareness.  This increase in fidelity will certainly improve safety and enhance the ability of the pilot to complete their mission.

           

References

General Atomics. (2015). Advanced cockpit GCS. Retrieved from http://www.ga-asi. com/ Websites/gaasi/images/products/ground_control/pdf/AdvCockpit021915.pdf 
RTI. (2007). RTI Middleware Powers New General Atomics Aeronautical Systems UAS Ground-Control Station. Retrieved from https://www.rti.com/company/news/GeneralAtomics .html

UAS Vision. (2013). General Atomics Next-Generation GCS Successfully Integrates Flagship and Advanced Predator Platforms. Retrieved from http://www.uasvision.com/2013 /04/25/general-atomics-next-generation-gcs-successfully-integrates-flagship-and-advanced-predator-platforms/