Small Unmanned Aerial Vehicles: The Way Forward By: M.E. McCauley, PhD Naval Postgraduate School P. Matsagas, M.Sc. Hellen...
Small Unmanned Aerial Vehicles The Way Forward <ul><li>This work is based on: </li></ul><ul><li>McCauley, M.E. & Matsagas,...
UAV Categories <ul><li>Micro-UAVs </li></ul><ul><ul><li>spanning six to nine inches </li></ul></ul><ul><li>Mini UAVs </li>...
DoD SUAV inventory Lamartin, G. F. (2005). Testimony given before the United States House Committee on Armed Services Subc...
Mission <ul><li>Frame of UAV missions </li></ul><ul><ul><li>Intelligence, surveillance, and reconnaissance (ISR)  </li></u...
Why UAVs? <ul><li>Eliminate risk to ground troops and aircrew,  </li></ul><ul><li>Enhance aerodynamic performance over man...
Are UAVs Useful? <ul><li>Lessons from Kosovo, and the demand for UAV services in Iraq and Afghanistan indicates that: </li...
System description
System example - POINTER <ul><li>Wingspan: 108” </li></ul><ul><li>Length: 72” </li></ul><ul><li>Gross weight: 10 pounds </...
Definition of “Small UAV” 1 <ul><li>UAVs designed to be employed </li></ul><ul><ul><li>by themselves; transportable by foo...
Problem definition <ul><li>Current SUAVs:  </li></ul><ul><ul><li>Are manpower intensive;  </li></ul></ul><ul><ul><li>Have ...
UAV Loss Rate <ul><li>Mishap Rate  is the number of accidents occurring per 100,000 flight hours.  </li></ul><ul><li>Mean ...
UAV Mishaps DoD HSIAC UAV sources (2004) 48 mishaps, of A and B type,  in 10 years
HSI issues <ul><li>Human Roles, Responsibilities and Level of Automation </li></ul><ul><li>Command and Control; Concept of...
Near-term improvements I. Human Roles, Responsibilities, Automation Level <ul><li>Human role </li></ul><ul><ul><li>Operate...
Near-term improvements II. Command and Control, and Operations  <ul><li>SUAV operators need to know where the sensor is po...
Near-term improvements III. Manning, Selection, Training, and Fatigue  <ul><li>Unmanned Aerial Vehicles are NOT unmanned s...
Near-term improvements IV. Difficult Operational Environments <ul><li>UAV systems’ attributes must be capable of supportin...
Near-term improvements Moving Control Platforms <ul><li>Major challenges of moving control platforms: </li></ul><ul><ul><l...
Longer-term Perspective Automation <ul><li>Goal </li></ul><ul><ul><li>Shift away from the human-in-the-loop control </li><...
Longer-term Perspective Allocation of System Functions 10. Terminate/Recall Mission  9. Launch Weapons  8. Takeoff and Lan...
Conclusions <ul><li>SUAV systems may </li></ul><ul><ul><li>Increase our operational efficiency and performance, </li></ul>...
<ul><li>Questions? </li></ul>
Supplemental
Swarming Concept
Visual Detection
Tactical Mini UAV (TACMAV) <ul><li>Wingspan: 53 cm </li></ul><ul><li>Length: 50 cm </li></ul><ul><li>Cruise speed: 80 kph ...
System example - HUNTER
Automation level of current UAV designs UV comparison to ten levels of automation
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McCauley &amp; Matsangas (2005) - Small Unmanned Aerial Vehicles: The Way Forward

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  • Micro-UAVs spanning six to nine inches Small UAV (SUAV) TERN, Silver Fox, Swift, Pointer, Raven, Dragon Eye Tactical UAV (TUAV) Hunter, Shadow Medium Altitude and Endurance (MAE) Global Hawk, Predator High-Altitude Long-Endurance (HALE) High altitude generally refers to above 50,000 feet and long endurance to 24 hours or more. By contrast, current SUAVs tend to operate at less than 5,000 feet and their endurance is on the order of several hours. Examples of current SUAVs are the TERN, Silver Fox, Swift, Pointer, Raven, and Dragon Eye. The Hunter and the Shadow, by contrast, are larger and usually are called “Tactical” UAVs (TUAVs). In this report, the term SUAV is used routinely, but the content of this report is equally appropriate for both SUAV and TUAV design concepts and operation.
  • The SUAV mission is Intelligence, surveillance, and reconnaissance (ISR) over the battlefield. SUAVs provide electro-optical (EO) or infrared (IR) sensor data leading to the detection, classification, and identification of vehicles, people, and other tactically relevant objects. From the HSI perspective, it is important to note that SUAVs do not detect, classify, or identify anything—people do. The SUAV provides the video or IR imagery or other data that enables properly trained observers to perform the detection, classification, and identification functions.
  • So, the major question is “ARE UAV useful in the battlefield?” Well, the answer is crystal clear. Yes, the UAVs are useful. Unfortunately, although this finding is obviously promising, observation of SUAV operations at field tests and discussions with operators indicate that many issues are yet to be solved.
  • On this slide we can see the Schematic diagram of a typical UAV system. The basic structure of a typical UAV system may be viewed as an aircraft with the crew physically remote from the vehicle. The upside of the remote crew is reduced human risk, the downside is reduced sensory input about the status of the aircraft (e.g., no noise, vibration, vestibular, or proprioceptive input) and reduced visual information regarding terrain, weather, air traffic, and threats. A typical UAV system has a minimum of two operators, a pilot or Air Vehicle Operator (AVO) and a sensor operator or Mission Payload Operator (MPO). As shown in Figure 1, these operators work in the Ground Control Station (GCS), which typically is installed in a stationary High Mobility Multipurpose Wheeled Vehicle (HMMWV; or “Humvee”). A schematic diagram of a typical UAV system is given in Figure 1, including a single UAV, a GCS with two operators, and an image analyst, who represents either a unit commander or a “customer” of the UAV payload data.
  • The definition given by the Department of Defence Roadmap (published in 2002) defined SMALL UAVs as: UAVs designed to be employed by themselves – any UAV system where all system components (i.e., air vehicles, ground control/user interface element, and communication equipment) are fully transportable by foot-mobile troops UAVs designed to be employed from larger aircraft (manned or unmanned) – any UAV system where the air vehicle can be loaded on the larger aircraft without the use of mechanical loaders (i.e., two-man lift, etc.)
  • The current performance of SUAVs reflects a technology that is still under development. Some of the challenges that must be faced are that SUAVs: • A re M anpower Intensive Current U.S. Army practice is to assign up to 24 people and three UAVs per UAV system. The UAVs are controlled from the GCS with two operators and often two additional people—an image analyst and a unit leader. Thus, the “Human-to-UAV” ratio could be characterized as 4:1, or 3:1, or, best case, 2:1. • Have a large Footprint The term “footprint” is used to convey both physical and logistical footprint. Future concepts of Small or Micro-UAVs envision air vehicles small enough and light enough to be carried in a backpack, hand-launched, and managed by one person. The current reality is that a manned SUAV system includes the following components: • GCS, housed and transported in a HMMWV; • Antennas, generators, and communications gear; • Remote Video Terminal; • 24 people and 3 UAVs per UAV system; and • Consumable spares. The challenge is to develop future SUAV systems that are smaller, lighter, more agile, and require less logistic support Need to improve Visual Data Display and Search Effectiveness Visual search is a demanding, time-consuming task that requires multilayered analysis of human perceptual and cognitive systems (Neisser, 1967). The time needed to detect a target (found to be 2.8 seconds in a visual detection experiment (Itti, Gold, and Koch, 2001)), may be much larger compared to the time a target remains visible on the monitor. Operational factors that contribute to this visual search problem are unstabilized imagery, narrow field of view (FOV), low visibility, communication drop outs, and high rate of visual flow (high speed and/or low altitude). Visual Data Display Challenges. The demand for “Tactical”-size UAV services in Iraq and Afghanistan indicates that commanders in the field place a high value on the tactical data provided by the TUAVs. However, observation of SUAV operations at field tests and discussions with operators indicate that improved imagery and image stabilization are needed to improve the ISR capability of SUAVs. Based on our observations, camera type had a large influence on ISR effectiveness. The searches conducted with a fixed camera were unproductive. If the altitude were high enough (approximately 2,000 ft above ground level) to support a reasonable field of view, then the size (retinal angle subtended by the displayed target) and resolution of the target/object images was insufficient to enable detection by the human observer, given the “bounce” of the imagery caused by mild turbulence. If the SUAV altitude were low enough, then the field of view (sweep width) was small, the optical flow rate was high, and the time available for detection was low. The image analyst’s task was difficult at low altitude due to the combination of the high rate of optical flow and the “bounce” of the imagery. The imagery bounce was considerably more than a small vibration. It was observed to be a magnitude of nearly one video frame, oscillating irregularly at a frequency of approximately 2-4 Hz. For example, if an object were at the top of the monitor image, it would go near, or off, the bottom of the monitor image several times per second. Searches conducted with a stabilized PTZ camera operated by a MPO were far superior to the fixed-camera system. The relationship between image quality, probability of detection, and the weight penalties that come with a shock-mounted, PTZ camera need to be quantified to support future SUAV design decisions. One HSI challenge is to define the detection system requirements by working “backwards” from the observer to the sensor. Existing methods for characterizing sensor resolution, like the National Imagery Interpretability Rating Scales (NIIRS) or Johnson criteria (Leachtenaur and Driggers, 2001), need to be extended to characterize the performance of trained human analysts monitoring a streaming video image. This analysis is needed not only for static optical resolution, but for dynamic issues such as optical flow, vibration, and image bounce caused by turbulence and the aerodynamic response of the air vehicle. Additionally, research is needed to determine the probability of detection for streaming video imagery compared to a series of still images. The frequency and duration of still images are variables that need to be tested. Tests are needed to determine whether operator detection performance with unstabilized video imagery is better when viewing a series of static frames. Research on this issue should be done with both static and moving targets. It is possible that static images may be better for stationary targets, while streaming video could have an advantage for detecting moving targets. Experts in visual perception should analyze the engineering characteristics of UAV video displays to optimize visual performance and pattern recognition. Engineering progress in reducing the size and weight of UAVs will be beneficial only if the video product is useful, i.e., stabilized and with sufficient resolution and contrast for the analyst/observer to achieve acceptable detection/classification performance. Have a high loss rate. More details on the next slide
  • Reliability of UAV is based on four metrics as defined by DoD. The loss data depicted on the matrix are extracted from DoD study (2002). There are two kinds of mishaps: Class A Mishap: Damage costs of $1,000,000 or more and/or destruction of an aircraft, and/or fatality or permanent total disability of personnel. Class B Mishap: Damage costs of between $200,000 and $1,000,000 and/or permanent partial disability and/or three or more people hospitalized as inpatients The “HUNTER” is considered to be a Tactical UAV.
  • A preliminary analysis of 48 UAV mishaps was reported by the DoD’s Human-Systems Information Analysis Center (HSIAC), based on data from the Air Force and Army Safety Centers (Rogers, Palmer, Chitwood, and Hover, 2004). This analysis included 10 years of data for Class A and Class B mishaps. Of the 48 mishaps, approximately one-third (15) were attributed to mechanical failure and, according to the judgments of Rodgers et al. (2004), all of the remaining 33 mishaps involved some form of human-systems issues. The cost of the 48 mishaps was nearly $14 million for the Army and $177 million for the Air Force, with the overall average cost per mishap close to $4 million. A breakout by UAV type for the 48 mishaps is as follows: Predator 32%, Hunter 19%, Shadow 17%, Global Hawk 8%, others 24%. These data do not provide insight into SUAV loss rates because they will be less than the “Class B” definition of $200,000. While cost, fatalities, and injuries are the traditional measures of “loss” for manned aircraft mishaps, and may be appropriate for large UAVs, they may not be the appropriate criteria for SUAVs. SUAV mishaps are not terribly expensive, nor are they fatal, but they compromise ISR effectiveness and add to logistic burdens. The HSIAC preliminary analysis of Rodgers, et al. (2004) suggests that two-thirds of SUAV mishaps involve human systems integration issues. This estimate is consistent with historical estimates of “human error” as a causal or contributing factor, which typically are in the range of 60%-80% of all accidents in ground transportation, aviation, and industry (Perrow, 1999). Given the current human-in-the-loop design of UAV control systems, we have no reason to believe that SUAV operations will be different. A study of the role of human and organizational factors in Army UAV accidents was reported by Manning, Rash, LeDuc, Koback, and McKeon (2004). The authors identified 56 UAV accidents from the U.S. Army Safety Center’s database during the period FY95-FY03. The Army traditionally identifies three basic causes of accidents—human, materiel, and environmental factors. This report summarizes categories of human-causal factors such as workload, fatigue, SA, training, crew coordination, and ergonomic design. Human error was found to play a role in approximately one-third (32%) of the reported accidents
  • The following categories of HSI issues apply to UAV operations, both now and in the future: • Human Roles, Responsibilities and Level of Automation; • Command and Control; Concept of Operations; • Manning, Selection, Training, and Fatigue; • Difficult Operational Environments; • Procedures and Job Performance Aids; and • Moving Control Platforms. HSI improve SUAV operations in two different perspectives: near-term improvements that require no new technologies and longer-term improvements, such as semiautomated systems, that will require time and research and development investment.
  • What we must understand about the human role in small UAV systems is to manually operate a remotely piloted vehicle or teleoperated system. According to AVOs and MPOs interviews the most difficult tasks in UAV use are considered to be: • Launch and landing; • Identifying and reacting in emergencies; and • Maintaining situational awareness (SA). Technologies are under development to support UAV operators in accomplishing the aforementioned tasks, through automation of basic functions such as altitude hold and waypoint following. These automation features reduce the operator workload by taking him/her out of the continuous manual control loop. They also change the role of the human operator from direct control to a hybrid of direct and supervisory control. Further development is needed to provide a mission management system that comprises pre-mission planning and dynamic replanning during a mission. A mission management system for SUAVs might contain the following features: • Preprogram, reprogram, and manage the flight profile; • Automated takeoff and landing; • Preprogram, reprogram, and manage the sensor payload; • Decision aiding for emergencies and degraded mode operations; and • Supplemental displays to support SA. The objectives of a mission management system are to reduce the mishap rate, shrink the 24-person SUAV unit, and enable simultaneous control of multiple UAVs.
  • SUAV operators need to know where the sensor is pointing: This capability is not provided in most current SUAVs. System response time and the temporal aspect of team communications: Significant factor of mission performance is the time lags of communication between an image analyst communicating “possible contact” and the coordinated response from the AVO and the MPO. This problem was exacerbated when the operators did not have information about the coordinates of the possible contact. ISR search pattern analysis and codification in a “search template” tool: SUAV operators currently define a search pattern by manually entering data for a series of waypoints. This process is time consuming, tedious, and may not reflect analyses of alternative search patterns. A software tool could be provided to SUAV operators that would support optimal search patterns while eliminating the requirement to enter every waypoint. Operations analysis leading to recommendations for system improvements: Most current SUAV systems were developed and fielded without the benefit of systems engineering and analysis. A thorough analysis of current SUAV operations, procedures, and training is recommended to determine ways to improve SUAV system effectiveness.
  • UAVs are not unmanned systems. People operate UAVs and how the people are selected, trained, and scheduled contributes to system effectiveness. The manning criteria for SUAV operations, both the number of personnel in a unit and the entry qualifications, should be reviewed and re-evaluated periodically as the systems evolve. The expert operators and teams are much more likely to be successful. The challenge is to define the selection criteria and continually update them as the job and the pool of candidates evolve. Strong training programs produce excellent operators. The architecture of UAV systems is conducive to embedded training in GCS design. Mission rehearsal training also is feasible, given that a database of the operating terrain and expected objects of interest are available and the setup tools are provided to establish meaningful scenarios. Fatigue, sleep, and circadian rhythm have strong effects on human performance (Miller, Nguyen, Sanchez, and Miller, 2003). Watch schedules for UAV operators are particularly important when engaged in day and night, 24-hour missions. Recent data indicates that a human operating on no sleep in a 24-hour period exhibits signal detection performance equivalent to someone with a 0.08 blood alcohol level, the legal limit in most states (Doheny, 2004). Manning decisions should ensure that a sufficient number of qualified and trained personnel is available to man a watch schedule. Pushing a crew to extend work schedules will result in performance decrements, no matter how dedicated and motivated the personnel may be.
  • HSI issues in difficult environments (night operations, environmental issues such as extreme temperatures, precipitation, and poor visibility due to sand, dust, or fog) should be analyzed to determine risk areas and to identify potential solutions. Finally, worst-case scenarios must be entertained, such as the requirement to operate in chemical and biological protective gear (MOPP 4). The UAV equipment interfaces, training, and procedures must be capable of supporting effective operations in all environments.
  • Human controllers will be confronted with the requirement to maintain spatial orientation and situational awareness (SA) of the controlled UAV(s), while simultaneously sensing and understanding the dynamics of their own moving vehicle (ground vehicle or ship). Research will need to be extended to determine how a moving operator can best retain spatial orientation and SA with dynamic displays of multiple vehicle dynamics (Durbin, Havir, Kennedy, and Schiller, 2003). In addition to the issue of navigation/spatial orientation, moving vehicles present three other HSI challenges: motion sickness Motion sickness occurs in these situations due to a combination of actual motion plus “cybersickness” biodynamic interference with manual control Biodynamic interference is simply the feed-through to manual control interfaces from the shock and vibration of rough terrain, transmitted through the vehicle suspension to the operator’s body, arms, and hands. This can be a significant problem, depending on the vehicle, the speed, and the terrain. head-mounted display (HMD) bounce HMDs tend to resonate at certain frequencies of vertical axis vibration, which are typical in ground vehicles (Sharkey, McCauley, Schwirzke, Casper, and Hennessy, 1995). As the HMD “bounces” relative to the face and eyes, degraded visual performance will occur.
  • Advances in technology are proceeding rapidly in many areas that are relevant to unmanned systems such as robotics, semiautonomous systems, biomimetics, and agent-based systems. The term “autonomy” can be applied to all of these technologies because they contribute to a shift away from the human-in-the-loop control that characterizes SUAV control systems. Automation is likely to play a large role in future UAV systems, but should not be viewed as a panacea. The introduction of highly automated systems can cause serious difficulties such as mode confusion, “automation surprise,” distrust, complacency, and the “out-of-the-loop” operator performance problem. All the aforementioned can be summarized as “Clumsy automation”, a term describing automation that makes easy tasks easier and difficult tasks more difficult. Human-in-the-loop simulation is an excellent tool for addressing HSI issues prior to final design of the technological subsystems. The manned-simulation approach enables the tradeoffs to be evaluated between the technical requirements, including automation features, and the HSI issues such as operator roles, tasking, manning, workload, user interfaces, the probability of operator error, and training requirements.
  • The depicted table on the slide gives a first approximation of separating top-level (human control), mid-level (overlapped, shared, or adaptive control), and lower-level tasks (prime candidates for automation). The “top-level” list is intended to endure, no matter how sophisticated the technology may become in the future. The mid-level tasks are currently accomplished by humans, but could be allocated, at least some of the time (adaptively), to intelligent automation in the future. The lower-level tasks also are frequently accomplished by humans, especially during takeoff and landing, but current technology is capable of performing these functions.
  • Several concepts merge under the heading of “SWARMS” including biomimetics, stigmergy, self-organizing systems, and emergent behavior. “BIOMIMETICS”is the term given to biologically inspired technology. Biomimetic programs seek to simulate or mimic the mobility, intelligent operation, and functionality of biological creatures. One aspect of biomimetics is the analysis of swarming behavior, which is common, in nature (bees, wasps, or ants). Similarly, flocks of birds and schools of fish exhibit closely coordinated group behaviors. Swarm Intelligence is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. Swarm intelligence provides a basis by which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model.
  • Ncw Suav Presentation

    1. 1. Small Unmanned Aerial Vehicles: The Way Forward By: M.E. McCauley, PhD Naval Postgraduate School P. Matsagas, M.Sc. Hellenic Navy General Staff
    2. 2. Small Unmanned Aerial Vehicles The Way Forward <ul><li>This work is based on: </li></ul><ul><li>McCauley, M.E. & Matsagas, P. (2004) – Human Systems Integration and Automation Issues in Small Unmanned Aerial Vehicles. Technical Report NPS-OR-04-008, Naval Postgraduate School, Monterey, CA. </li></ul><ul><li>The research was funded by: </li></ul><ul><li>Army/ NASA RotorCraft Division, Aeroflightdynamics Directorate, NASA Ames Research Center, Moffet Field, CA 94305 </li></ul>
    3. 3. UAV Categories <ul><li>Micro-UAVs </li></ul><ul><ul><li>spanning six to nine inches </li></ul></ul><ul><li>Mini UAVs </li></ul><ul><ul><li>TACMAV </li></ul></ul><ul><li>Small UAV (SUAV) </li></ul><ul><ul><li>TERN, Silver Fox, Swift, Pointer, Raven </li></ul></ul><ul><li>Tactical UAV (TUAV) </li></ul><ul><ul><li>Hunter, Shadow </li></ul></ul><ul><li>Medium Altitude and Endurance (MAE) </li></ul><ul><li>High-Altitude Long-Endurance (HALE) </li></ul><ul><ul><li>Global Hawk, Predator </li></ul></ul>
    4. 4. DoD SUAV inventory Lamartin, G. F. (2005). Testimony given before the United States House Committee on Armed Services Subcommittee on Tactical Air and Land Forces, March 9, 2005.Assecced on October 16, 2005, at http://www.house.gov/hasc/testimony/109thcongress/TacAir%20Land/3-9-05LamartinOSD.pdf
    5. 5. Mission <ul><li>Frame of UAV missions </li></ul><ul><ul><li>Intelligence, surveillance, and reconnaissance (ISR) </li></ul></ul><ul><ul><li>Intelligence, surveillance, target acquisition and reconnaissance (ISTAR) </li></ul></ul><ul><ul><li>Battle Damage Assessment (BDA) </li></ul></ul><ul><li>Lamartin, G. F. (2005). Testimony given before the United States House Committee on Armed Services Subcommittee on Tactical Air and Land Forces, March 9, 2005.Assecced on October 16, 2005, at http://www.house.gov/hasc/testimony/109thcongress/TacAir%20Land/3-9-05LamartinOSD.pdf </li></ul><ul><li>House of Commons Defence Committee (2004). Lessons of Iraq: Government Response to the Committee’s Third Report of Session 2003-04. First Special Report of Session 2003-04, HC 635, London: The Stationery Office, June 8, 2005. Assessed on October 16, 2005, at www.publications.parliament.uk/pa/cm200304/cmselect/cmdfence/635/635.pdf </li></ul><ul><li>Defence Science Board (2004). Unmanned Aerial Vehicles and Unmanned Combat Aerial Vehicles. Washington DC: Office of the Under Secretary of Defence, for Acquisition, Technology, and Logistics. Assessed at www.acq.osd.mil/dsb/reports/uav.pdf on October 16, 2005. </li></ul>
    6. 6. Why UAVs? <ul><li>Eliminate risk to ground troops and aircrew, </li></ul><ul><li>Enhance aerodynamic performance over manned flight due to lighter weight and freedom from human G-tolerance constraints </li></ul><ul><li>Reduced cost </li></ul>
    7. 7. Are UAVs Useful? <ul><li>Lessons from Kosovo, and the demand for UAV services in Iraq and Afghanistan indicates that: </li></ul><ul><ul><li>UAVs can provide vastly improved acquisition and more rapid dissemination of ISR data </li></ul></ul><ul><ul><li>commanders in the field place a high value on the tactical data provided by UAVs </li></ul></ul><ul><li>Lamartin, G. F. (2005). Testimony given before the United States House Committee on Armed Services Subcommittee on Tactical Air and Land Forces, March 9, 2005.Assecced on October 16, 2005, at http://www.house.gov/hasc/testimony/109thcongress/TacAir%20Land/3-9-05LamartinOSD.pdf </li></ul><ul><li>House of Commons Defence Committee (2004). Lessons of Iraq: Government Response to the Committee’s Third Report of Session 2003-04. First Special Report of Session 2003-04, HC 635, London: The Stationery Office, June 8, 2005. Assessed on October 16, 2005, at www.publications.parliament.uk/pa/cm200304/cmselect/cmdfence/635/635.pdf </li></ul><ul><li>Defence Science Board (2004). Unmanned Aerial Vehicles and Unmanned Combat Aerial Vehicles. Washington DC: Office of the Under Secretary of Defence, for Acquisition, Technology, and Logistics. Assessed at www.acq.osd.mil/dsb/reports/uav.pdf on October 16, 2005. </li></ul>
    8. 8. System description
    9. 9. System example - POINTER <ul><li>Wingspan: 108” </li></ul><ul><li>Length: 72” </li></ul><ul><li>Gross weight: 10 pounds </li></ul><ul><li>Airspeed: 22-50 kts </li></ul><ul><li>Patrol radius: 3-5 miles </li></ul><ul><li>Endurance: 90 minutes </li></ul><ul><li>Missions: ISR </li></ul><ul><li>System: 3 UAV per 3-man team </li></ul><ul><li>GCS: Back-packed </li></ul><ul><li>Capability </li></ul><ul><ul><li>Daylight CCD camera </li></ul></ul><ul><ul><li>GPS receiver </li></ul></ul><ul><ul><li>Infrared camera (optional) </li></ul></ul><ul><ul><li>Assembly: <5 minutes. No tools. </li></ul></ul>
    10. 10. Definition of “Small UAV” 1 <ul><li>UAVs designed to be employed </li></ul><ul><ul><li>by themselves; transportable by foot </li></ul></ul><ul><ul><li>from larger aircraft (manned or unmanned) </li></ul></ul>1 DoD UAV Roadmap 2002
    11. 11. Problem definition <ul><li>Current SUAVs: </li></ul><ul><ul><li>Are manpower intensive; </li></ul></ul><ul><ul><li>Have a large “footprint”; </li></ul></ul><ul><ul><li>Need to improve visual data display and search effectiveness; and </li></ul></ul><ul><ul><li>Have a high loss rate. </li></ul></ul>
    12. 12. UAV Loss Rate <ul><li>Mishap Rate is the number of accidents occurring per 100,000 flight hours. </li></ul><ul><li>Mean Time Between Failure (MTBF) is the ratio of hours flown to the number of maintenance-related cancellations encountered. </li></ul><ul><li>Availability is the number of times a given aircraft type is able to perform its missions compared to the number of times it is tasked to do so, expressed as a percentage. </li></ul><ul><li>Reliability is 100 minus the percentage of times a launched mission is either canceled before takeoff or aborted during flight due to maintenance issues, expressed as a percentage </li></ul>DoD Roadmap (2002) Camp Roberts SUAV field tests (2004) One mishap per 16-24 flight hours Estimate that average airframe life duration is approximately 20 flight hours. DoD Defence Science Board Study (2004) Class A mishaps per 100.000 hrs. Class A mishaps per 100.000 hrs. Large Airliners – 0.01 Regional Commuter – 0.1 Hunter – 55 General Aviation – 1 Pioneer – 334 F-16 – 3 Predator – 32 Aircraft Mishaps UAV Mishaps 82% 98% 11 55 Hunter 86% 76% 14 334 Pionner 82% 67% 44 32 Predator Reliability Availability MTBF (hrs.) Mishap Rate System
    13. 13. UAV Mishaps DoD HSIAC UAV sources (2004) 48 mishaps, of A and B type, in 10 years
    14. 14. HSI issues <ul><li>Human Roles, Responsibilities and Level of Automation </li></ul><ul><li>Command and Control; Concept of Operations </li></ul><ul><li>Manning, Selection, Training, and Fatigue </li></ul><ul><li>Difficult Operational Environments </li></ul><ul><li>Procedures and Job Performance Aids </li></ul><ul><li>Moving Control Platforms </li></ul>
    15. 15. Near-term improvements I. Human Roles, Responsibilities, Automation Level <ul><li>Human role </li></ul><ul><ul><li>Operate a remotely piloted vehicle or teleoperated system </li></ul></ul><ul><li>Automation of basic functions </li></ul><ul><ul><li>Altitude control, waypoint following etc </li></ul></ul><ul><ul><li>Reduce operator workload </li></ul></ul><ul><li>Mission management system </li></ul>
    16. 16. Near-term improvements II. Command and Control, and Operations <ul><li>SUAV operators need to know where the sensor is pointing </li></ul><ul><li>System response times </li></ul><ul><li>ISR search pattern analysis and codification in a “search template” tool </li></ul><ul><li>Operations analysis </li></ul>
    17. 17. Near-term improvements III. Manning, Selection, Training, and Fatigue <ul><li>Unmanned Aerial Vehicles are NOT unmanned systems </li></ul><ul><li>Manning criteria </li></ul><ul><li>Strong training programs </li></ul><ul><li>Watch schedules based on: </li></ul><ul><ul><li>Fatigue, sleep, and circadian rhythms </li></ul></ul>
    18. 18. Near-term improvements IV. Difficult Operational Environments <ul><li>UAV systems’ attributes must be capable of supporting EFFECTIVELY operations in all environments </li></ul><ul><ul><li>Interfaces </li></ul></ul><ul><ul><li>Training </li></ul></ul><ul><ul><li>Procedures </li></ul></ul>
    19. 19. Near-term improvements Moving Control Platforms <ul><li>Major challenges of moving control platforms: </li></ul><ul><ul><li>motion sickness </li></ul></ul><ul><ul><li>disorientation </li></ul></ul><ul><ul><li>biodynamic interference with manual control </li></ul></ul><ul><ul><li>head-mounted display (HMD) bounce </li></ul></ul>
    20. 20. Longer-term Perspective Automation <ul><li>Goal </li></ul><ul><ul><li>Shift away from the human-in-the-loop control </li></ul></ul><ul><ul><li>Human controls multiple UAVs </li></ul></ul><ul><li>But </li></ul><ul><ul><li>Mode confusion </li></ul></ul><ul><ul><li>Automation surprise </li></ul></ul><ul><ul><li>Distrust </li></ul></ul><ul><ul><li>Complacency </li></ul></ul><ul><ul><li>“ Out-of-the-loop” operator </li></ul></ul>
    21. 21. Longer-term Perspective Allocation of System Functions 10. Terminate/Recall Mission 9. Launch Weapons 8. Takeoff and Landing 8. Modify Mission Objectives and Tactics 7. Sensor/Payload Control 7. Initiate Operation 6. Teamwork Dynamics 6. Determine Contingency Plans 5. Store or Transmit Data 5. Avoid or Investigate Possible Threats 5. Determine Tactics 4. Collision Avoidance Processing 4. Navigate 4. Specify Time Period 3. Maintain Sensor Lock-on 3. Implement Tactics 3. Specify Location 2. Counter Environmental Disturbances 2. Terrain Avoidance 2. Specify/Assign Team and Assets 1. Vehicle Control (autopilot functions) 1. Airspace Deconfliction; “See and Avoid” 1. Define Mission Objectives Lower-Level Automated Tasks Mid-Level Shared Tasks Top-Level Human Tasks
    22. 22. Conclusions <ul><li>SUAV systems may </li></ul><ul><ul><li>Increase our operational efficiency and performance, </li></ul></ul><ul><ul><li>Reduce risk, and </li></ul></ul><ul><ul><li>Change the way we operate </li></ul></ul><ul><li>Several technological developments are needed to achieve efficient operational systems </li></ul>
    23. 23. <ul><li>Questions? </li></ul>
    24. 24. Supplemental
    25. 25. Swarming Concept
    26. 26. Visual Detection
    27. 27. Tactical Mini UAV (TACMAV) <ul><li>Wingspan: 53 cm </li></ul><ul><li>Length: 50 cm </li></ul><ul><li>Cruise speed: 80 kph </li></ul><ul><li>Endurance: 30-50 minutes </li></ul><ul><li>Missions: ISR </li></ul><ul><li>GCU: Back-packed </li></ul><ul><li>Capability </li></ul><ul><ul><li>2 color CCD cameras </li></ul></ul><ul><ul><li>Video transmitter </li></ul></ul>
    28. 28. System example - HUNTER
    29. 29. Automation level of current UAV designs UV comparison to ten levels of automation

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