Development of Unmanned Aerial Vehicle Systems for Terrain Mapping and Geospatial Data Management

Transcription

1 INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 5, No 3, 2015 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN Development of Unmanned Aerial Vehicle Systems for Terrain Mapping and Geospatial Data Management Balaji Sethuramasamyraja, Nick Simonian, and Darnell Austin Department of Industrial Technology, California State University, Fresno 2255 E Barstow Ave, M/S IT 09, Fresno, CA , USA Impact Marketing Enterprises & Hydroponic Mastery Inc., Fresno, CA 93740, USA balajis@csufresno.edu ABSTRACT The application of unmanned aerial vehicles (UAV) for short range civilian applications has reached new dimensions with the advent of modern yet affordable autonomous guidance systems. Low altitude light weight UAV systems were developed that have capability for semi-autonomous and autonomous way point navigation with a payload of sensing system. While UAV I was utilized to test the feasibility of application with manual control using radio control and first person wireless video, UAV II was built for autonomous flight and field data collection. With UAV II, in addition to an autopilot navigation system, a Global Positioning Satellite (GPS) receiver and fight stabilization sensors were utilized for system stability and guidance during completely autonomous flights. Both the UAVs successfully performed in flight conditions for terrain mapping of property, range, agricultural and natural resource management with respect to initial test flights, flight parameter calibration and field trials. In UAV II, average calibration parameters were +/-0.64 deg. for pitch, +/-3.35 deg. for roll in speed range of mph indicating good flight stability. The UAVs were tested over a user set waypoint path on fields, facilities/properties, and urban locales, altitude ranging 100 ft to 1000 ft, speed from 25 to 60 mph, and maneuvers including straight paths, turns, and banks. An autonomous image acquisition system was developed that records images and videos with onboard sensors at specified time, frequency and coverage area. Georeferenced aerial images were created to 4 in. resolution using mosaic images and post processing software. UAV I and UAV II maintained intended flight path parameters within 55 ft. and 8.23 ft. spatial position or cross track error, respectively. Key words: Unmanned Aerial Vehicle, Autonomous Navigation, Farm Automation. 1. Introduction Unmanned aerial vehicles (UAV) have shown rapid development in the recent past due to availability of technology such as, autonomous navigation hardware, software and sensing systems. UAV is typically defined as self-powered aerial vehicle that carries no humans, uses aerodynamic forces for vehicle lift, fly autonomously or be remotely piloted and carry a payload (Bone and Bolkcom, 2003). UAVs can be deployed quickly, operated in remote areas and have relatively low operating cost. UAVs are commonly associated with defense applications while they are constantly gaining popularity in civilian applications as well. UAVs can benefit civilian applications such as, facilities/property management, environmental surveillance, and terrain mapping for range/agricultural management, photogrammetry and surveying (Doherty et al. 2000). Submitted on October 2014 published on January

2 Park and Ro (2004) developed a military UAV with high mobility and quick provision of bird s eye view video over hostile terrain with semi autonomous control using ground operator, which was not realistic for civilian applications due to size and cost issues. Beard et al. (2005) investigated the feasibility of avoidance system for use in military UAV applications using semi autonomous larger aircrafts that were cost prohibitive for civilian and scientific applications as well. In general, UAVs developed for defense were too customized for military applications and not suitable for transfer to civilian use due to size, technology and cost. Hardin and Jackson (2005) developed a UAV with off-the-shelf components for low-altitude large-scale photography for rangeland documentation. The stabilization technology available then was limited and completely autonomous navigation was not viable which forced the use of assisted stability control systems. Wang et al. (2008) developed a UAV system that adjusts flight parameters in real time to provide detailed field information for decision making in oilgas application using neural network techniques that has become redundant now with availability of standard flight stability and autonomous systems (hardware, firm ware and software). Hirokawa et al. (2007) developed a simple UAV system for natural disaster assessment and decision support on the fly through real time data relayed from onboard sensors to a ground control station with the absence of flight stability study. Herwitz et al. (2004) utilized a remotely controlled UAV for crop management of coffee plantations including monitoring weeds, applying fertilizers & water, and also differential harvesting based on fruit ripeness. This UAV was capable of completely autonomous flight and could stay aloft for days while its large size and expensive development costs were prohibitive for small scale civilian applications. Logan et al. (2005) reported the technological challenges involved in small UAV development including size constraints, development cost, lack of design and analysis tools and unique mission requirements. All these factors add to the level of difficulty involved in the development of universal lightweight UAVs that could be used in a variety of applications. Although there are currently a variety of UAVs available in a range of sizes and navigation methods which include full manual navigation control for the most basic aircraft as well as fully autonomous navigation systems, a low cost and lightweight UAV that was easy to deploy and completely autonomous with low fixed and operational costs for terrain mapping in agriculture, natural resources and property management is uncommon. The basic goal of this research was to develop a lightweight UAV that could be rapidly deployed and autonomously controlled as applied economically in terrain mapping for property, range, agricultural and natural resources management. The objectives were, 1. To assess feasibility of flight deployment in aerial terrain mapping, in relatively small land parcels, < 100 acres while maintaining stability and consistent flight path 2. To design and develop of manual and autonomous aircrafts that collect georeferenced sensor data for decision making 2. Materials and Methods 2.1 UAV I Manual Control A fixed wing electric plane was designed from scratch and used an electric motor producing 200 W supplied by a lithium polymer 11.1 V 2.4 Ahr battery pack providing 12 min. flight duration at single launch. UAV I has a 60 in. wingspan and total flying weight of 2.7 lbs (Table 1). 405

3 Table 1: UAV I Specifications Wingspan (in.) Length (in.) Weight (lbs) Power (w) Rang e(mi n) Payload (lbs) Max speed (mph) UAV I was piloted using handheld Futaba 6 Channel 72 MHz radio controller (Futaba, Champaign, IL). Video data was collected by a charged coupled device (CCD) camera, Sony SN555 1/3rd CCD (Sony Corporation of America, NY) mounted to pan and tilt gimbals allowing for 360 deg. pan and 90 deg. tilt. UAV I was remotely controlled via real time video feed wirelessly sent by T GHz 500 mw transmitter with a maximum range of 7 miles, on board to a ground station head display unit allowing navigation control. Figure 1: UAV I. a. Manual Flight, b: Remote Real Time Control with First Person View (FPV) A second still camera the Canon SD 950 IS 12.1 mega pixel (Canon U.S.A., Inc., Lake Success, New York) triggered by Futuba radio controller, was utilized for high resolution orthographic pictures. Panorama Tools Graphical User Interface, PT GUI (New House Internet Services B. V., Rotterdam, The Netherlands) application photogrammetric software was utilized for geo-referencing and stitching mosaic images. UAV I (Figure 1) was utilized for mosaic image collection of a 36 acre alfalfa field through manual control. UAV I collected image and video data at 30 mph airspeed, 450 ft. above ground in north-south pattern with turns completed to allow 100 yd. pass to pass spacing. The image acquisition was triggered manually using handheld Futaba radio controller in 10 s intervals. A second flight was carried out over a riverbed (Main Fork, Three River, CA) using a single pass at 900 ft. altitude in east-west direction every 15 s. 2.2 UAV II Autonomous Control After successful initial results from UAV I a second UAV was developed to allow for improvement of data and image collection. UAV II was designed with an electric motor producing 425 W supplied by two lithium polymers 11.1 V 2.5 Ahr battery pack in parallel 406

4 totaling 5Ahr. UAV II has a top mounted wing design for high lift using an under camber airfoil 60 in. in length weighing 4.3 lbs with a maximum payload of 1 lb (Table 2). Table 2. Specifications for UAV Wingspan (in.) Length (in.) Weight (lbs) Power (w) Rang e(min ) Payload (lbs) Max speed (mph) The intended waypoint flight path was constructed in Google Earth (Google Inc., Mountain View, CA) software. Using the line path tool, a waypoint navigation flight path with airspeed, altitude parameters, camera trigger location and time interval was created for autonomous navigation system, AttoPilot v. 1.8 beta (AttoPilot LLC, Gilbert, AZ). UAV II was manually controlled during initial hand launch and then switched to autonomous mode before the first waypoint. Canon SD 950 IS 12.1 mega pixel camera was used for still image capture that was auto-triggered by Attopilot according to presets. 2.3 Navigation and Stability System Calibration During calibration flights, UAV II had high roll and pitch oscillations that required gain settings adjustments to improve mosaic image quality. Table 3 lists the flight conditions while capturing images. During calibration flight runs, the flight parameter log file is updated at 5Hz frequency and pitch, roll, speed, heading, data fields as well as other log data was used for precision adjustment of gain settings. The gain value (0 100) for PID controller was user set (Table 4). Among the seven servo settings, camera stabilization servos were not used while rudder servo was disabled to suit UAV II wing design. Table 3: Flight Parameters during UAV II Calibration run to acquire still images Photo Number Pitch (deg.) Roll (deg.) Altitude (ft.) Camera trigger error (ft.) Heading (deg.) Speed (mph) * * Refers to < 3.5 degrees maximum allowed pitch or roll for photo collection. A maximum allowed pitch or roll angle of 3.5 degrees for each image was used to determine if that image would be usable for the creation of the final mosaic image. Pitch or roll angles above 3.5 degrees tend to create large distortion of the mosaic image and degrade its usability and accuracy. It s clear that only one image met the minimum requirements requiring further gain adjustments for oscillation dampening. Roll and pitch oscillations are evident in log files and review of log files is used to make PID adjustments. (Figure 2a and 2b). 407

5 Pitch and roll before adjustments s e 20 re g10 e D Time (0.2 s Intervals) pitch Roll Pitch and roll after adjustments s e 20 re g 10 e D l 0 a-10 im c-20 e D Time (0.2 s Intervals) Figure 2: Pitch & Roll after adjustments - executes a 90 deg. turn into a 450 yd. straight line path after PID fine tuning. a. P and D for Roll - 45 & 2. Pitch P and D for Pitch - 33 and 0, and b. P and D for Roll - 34 & 10. Pitch P and D for Pitch - 58 and 18 pitch roll UAV II flight stability requires adjustment of automatic servo response time using Proportional-Integral-Derivative, PID controller gain setting. Integral gain values were autogenerated based on user set proportional and derivative gain settings that enabled UAV II react to its dynamic environment by producing controlled pitch, roll and speed changes for path tracking. UAV II autonomous stabilization was achieved with thermopile sensors that send analog feedback signal to the Attopilot system, operating in 5.5 and 15 microns infrared spectrum based on the difference between sky and ground temperature. The Attopilot system also has an absolute barometric pressure sensor and pitot tube airspeed sensor as a feedback loop to increase precision and minimize failure. UAV I and UAV II path tracking performance could be evaluated using cross track error parameter which is defined as the difference between intended and the actual path or mean actual distance in ft. of separation between the intended and actual path. In the case of manual flight, a set of reference points or ground markers were utilized as the intended path while the actual path was recorded using an onboard GPS system. In the event of autonomous mapping, a true geo-referenced intended path was loaded to the UAV II system prior to launch which was compared to the actual path tracked by the GPS system. 408

6 Table 4: Attopilot Gain Settings Servo Proportional Gain Settings Derivative Gain Settings Control Parameter Aileron (Roll) Elevator (Pitch) * Throttle 4 Disabled Disabled Rudder (Yaw) 5 Not Used Not Used 6 Not Used Not Used Camera Stabilization Camera Stabilization * Camera Trigger * For Throttle: manufacturer recommended 0 due to lack of precision in sensor that determines rate of change of air speed with respect to time and For Camera Trigger: D gain setting was not required 2.4. Way Point Navigation Path Set Up GPS was utilized for geo-referencing and waypoint tracking, mounted on top of UAV II with clear sky view receiving radio signals from satellites for navigation, maintaining ground speed, compass heading, and altitude. Google Earth software was utilized for waypoint generation with geographical coordinates and altitude using feature generation tools. Under optimal conditions, the intended waypoints for flight path generated in Google Earth software for autonomous navigation results up to 1 yd. accuracy with respect to actual flight. A handheld GPS locator was also utilized for assistance in waypoint placement and ground checking. Figure 3. Intended way point navigation flight path of UAV II 409

7 Typically, Google Earth line tool was used to create the intended flight path in the area of interest (Figure 3). After creating a Keyhole Markup Language, KML format file with geocoordinates, it was post processed using an executable Attopilot Software and the resulting text file was appended with airspeed and camera triggers for each waypoint before loading to Attopilot system onboard UAV II. 3. Results and discussion 3.1 UAV I Manual Control UAV I with a GPS receiver and a wireless video camera/transmitter were utilized for flight path simulation in a small parcel area, specifically an alfalfa field (Figure. 4). The GPS receiver on board operating at 1 Hz. frequency in the east-west direction collected actual flight path geographical coordinate at the rate of 1 point per second as overlaid on a Google earth image of alfalfa field (Figure. 4). Flying 400 ft. above ground at 30 mph, UAV I captured a data geographical coordinate using GPS receiver every 20 yd. at 1 s interval indicating feasible spatial data collection using sensors. The total time elapsed to cover 66 acres with 1 Hz. frequency data collection was only 20 min including launch, set up and flight path acquisition. UAV I was successful in maneuvering turns as tight as 20 yd radius, which is good spatial resolution for sensor data on relatively small land parcels like agricultural fields. Video feed aided manual navigation purposes from a bird s eye view for ease of control by the flight operator at ground. Manual Path (GPS Recording) Figure 4. UAV I manual flight path captured by on board GPS receiver at 1 Hz. frequency while flying at 30 mph & 400 ft above ground flight path over Google Earth Image. Figure 5. Mosaic image of 36-acre alfalfa field captured manually. Mosaic created from approximately 20 photos. Large distortion and misalignment is evident. 410

8 UAV I with a still image camera on board, manually triggered image data collection every 10 s resulting in 0.1 Hz. frequency while flying 450 ft above ground covering 36 acres in 12 min. Twenty usable images were used for building a mosaic image (Figure 5) of the alfalfa field, which were geo-referenced and stitched using remote sensing post processing using PT GUI software. The mosaic image generated could spot areas of variability with respect to soil, vegetation and weed outbreaks. However, mosaic image creation was bound to high degree of distortion due to variation in bank angle caused by pitch and roll. This explains the manual control stability was difficult in high wind turbulence as executing smooth equal turning radius was not achievable to high degree of precision of +/- 2.5 deg. along the intended route due to constant crosswind corrections, lack of aircraft attitude feedback and pilot error. Further difficulty resulted from attempting to hold course while maintaining pitch and roll near 0 deg. during image capture. Essentially, due to high degree of pitch and roll during each image capture, mosaic generated had degraded quality in the alfalfa field. The cross track error between the actual flight path and intended flight path using reference ground markers along the path of data acquisition is an estimated average of 55 ft excluding the turns. The extent of this estimated error is highly variable based on pilot skill level as well as weather conditions. A second test with UAV I with a still image camera on board, manually triggered image data collection every 10 s resulting in 0.1 Hz. frequency while flying 120 acres in less than 12 min. Ten usable images were used for building a mosaic image (Figure 6) of the Main Fork River, Three Rivers, CA using the PT GUI software. Distortion of the mosaic image collected on this application was less due to reduced wind and a simpler straight path not requiring turns (Figure 6). Figure 6. Singe pass mosaic image of Main Fork Riverbed, Three Rivers, CA 3.2 UAV II Autonomous Control A multi pass waypoint intended path was developed for Town Arena, Three Rivers, CA for property/event management (Table 5). The flight length was 2500 ft per pass and 278 ft between passes while capturing digital images every 295 ft. While the total flight path is 9500 ft, the actual photo coverage length is only 4500 ft due to turns involved. 411

9 Table 5: Multi-pass mosaic image collection parameters for Town Arena, Three Rivers, CA Relative Altitude (ft) Speed (mph) Total flight path (ft) Camera trigger distance (ft) Major length and Width of Flight path (ft) Distance of total flight path used for photo collection (ft) & UAV II was hand launched from near the target location and manually piloted to 984 ft. above ground and then switched to autonomous mode. The UAV maintained preset heading, airspeeds, and altitudes while navigating the course and collected images every 295 ft along intended waypoint flight path. (Figure 7) A total of fifteen images were collected over the Town Arena, Three Rivers, CA. Figure 7. UAV II Intended (Lighter) vs. Actual (Darker) Flight path over Town Arena, Three Rivers, CA Table 6: Roll, pitch and heading of image captured during multi pass test run of Town Arena, Three Rives, CA Image Pitch (deg.) Roll (deg.) Heading (deg.) Actual Air Speed Actual Altitude (ft) (mph) (NW) (NW) * (NW) * (NW) (NW) * (SE) * (SE) * (SE) * (SE)

10 10 * (SE) (NW) (NW) * (NW) * (NW) * (NW) Average NA * Refers to < 3.5 deg. Cut Off for better image quality UAV II intended and actual flight paths during image capturing showed consistent performance along the intended flight path shown in white. The intended flight path is loaded to the UAV at launch and is executed immediately upon switching to autonomous flight. The intended path is the exact course the UAV should attempt to follow. Ideally the actual path should not deviate from the intended path however due to a necessary turn radius a noticeable deviation for intended path is expected whenever the UAV must change direction to stay on course. The cross track error between the actual flight path and intended flight path along the path of image capture is an average 8.23 ft excluding the turns as no image was captured during turns enabling accurate mosaic image acquisition. Table 6 lists UAV II image collection parameters of 7 min data acquisition including take off and landing. Figure 8. Mosaic image of Arena, Three Rivers, CA created using the 10 images listed in Table 6. Of the 15 images shown in table 6 only 10 images were used for the mosaic image creation (Figure. 8) of Town Arena, Three Rivers, CA. These 10 images were selected because they had a roll and pitch angle equal to or less than 3.5 degrees. Due to the low roll and pitch error and the ability to maintain target altitude and heading a mosaic image with minimal distortion was rapidly collected. An average 0.64 deg. pitch, 3.35 deg. roll, 42 mph actual air speed against intended 41 mph and 1034 ft. actual altitude against intended 984 ft. were achieved. UAV II collected about ten different mosaic images and video from applications such as property/event management, cemetery, golf course, arena, agricultural field and ranges both during calibration runs, testing and image/video data collection. 4. Conclusion UAVs were developed to working prototypes, one of which, UAV I with manual control was utilized for feasibility study and the other, UAV II with autonomous control capable of GPS 413

11 based waypoint navigation with on board cameras that collected high resolution images and video for decision making. Remote sensing post processing software was used to georeference and stitch individual images to mosaics. The mosaic images and video data collected in various applications including facility sites, ranges, agricultural fields and other terrains were utilized for management decision making by discipline experts. UAV II showed consistent results with an average cross track error of 8.23 ft and provides a viable system for autonomous aerial image acquisition. UAV II average calibration parameters were +/-0.64 deg. for pitch, +/-3.35 deg. for roll in speed range of mph airspeed enabling a stable platform for waypoint path tracking with intended target speed and altitude. UAV I and II with on board sensing systems successfully collected about 150 different runs for mosaic images, videos and data collection including property/event management, agricultural fields and ranges during calibration runs, testing and data collection. The total flying time (excluding field set up, ground testing, calibration and troubleshooting) during this research with UAVs were 3 hr in manual mode and 12.5 hrs in autonomous mode, respectively. Acknowledgements Partial support for this research was provided by Dr. Jim Yager (Impact-Ag, Fresno, CA), Mr. Gino Fargovsa (Fargovsa Farms, West Fresno), and Department of Industrial Technology (California State University, Fresno). Partial funding was provided by our industry sponsor Digi-Star, Fort Atkinson, WI. 6. References 1. Beard, R., Kingston, D., Quigley, M., Snyder, D., Christiansen, R., Walt, J., McLain, T., and Goodrich, M.A. (2005), Autonomous Vehicle Technologies for Small Fixed- Wing UAVs. Journal of Aerospace Computing, Information and Communication, 2, pp Bone, E., and Bolkcom, C. (2003), Unmanned Aerial Vehicles: Background and Issues for Congress. Report for Congress. pp Doherty, P.; Granlund, G.; Kuchcinski, K.; Nordberg, K.;Sandewall, E.; Skarman, E.; and Wiklund, J. (2000), WITAS unmanned aerial vehicle project. the 14th European Conference on Artificial Intelligence Almond Lifecycle and Growth. (2010). Almond Board of California, Modesto, CA, USA. 4. Hardin P.J and M. W. Jackson (2005), An Unmanned Aerial Vehicle for Rangeland Photography. Rangeland Ecology & Management 58(4), pp Herwitz, S. R., Johnson, L. F., Dunagand, S. E., Higgins, R. G., Sullivand, D.V., Zhengc, J., Lobitzc, B. M., Leunge, J. G., Gallmeyere, B. A., Aoyagi, M., Slye R. E., and Brass J. A. (2004), Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Computers and electronics in agriculture, Pp Hirokawa, R., Kubo, D. Suzuki, T. Suzuki, S. Meguro, J. (2007). Real-time hazard map generation using small unmanned aerial vehicle. Proceedings of the Society of Instrument and Control Engineers Annual Conference, pp

12 7. Logan, M, Vranas, T, Motter, M, Shams, Q, and Pollock, D. (2005). Technology challenges in small UAV development. InfoTech at Aerospace: Advancing Contemporary Aerospace Technologies and Their Integration, American Institute of Aeronautics and Astronautics, 3, pp Park, J,and Ro, K. (2004). A prototype design, test and evaluation of a small unmanned aerial vehicle for short-range operations. Unmanned-Unlimited" Technical Conference, Workshop, American Institute of Aeronautics and Astronautics, 2, pp Wang, T. M., Lei, X, Linag, J, and Pei, B. (2008). A small unmanned aerial vehicle for oil-gas field surveillance. Proceedings of the 7th International Conference on Machine Learning and Cybernetics, Institute of Electrical and Electronics Engineering, 4, pp