2005 Boston Conference[1].

Use of Cooperative Unmanned Air and Ground Vehicles for Detection and Disposal of Simulated Mines Erica Zawodny Mac Arthur, Donald Mac Arthur, Carl Crane University of Florida, Dep of Mechanical & Aerospace Engineering Gainesville, Florida 32611 ABSTRACT The objective of this research is to extend the sensing capabilities of amulti-vehicle ground system by incorporating the environmental perception abilities of unmanned aerial vehicles. The aerial vehicle used in this research is a Miniature Aircraft Gas Xcell RC helicopter. It is outfied with asensor payload containing stereo vision cameras, GPS, and adigital compass. Geo-referenced images are gathered using the above sensors that will be used in this research to create amap of the operating region. The ground vehicle used in this research is an automated Suzuki Mini-Quad ATV. It has the following onboard sensors: single-vision camera, laser range device, digital compass, GPS, and an encoder. The ground vehicle will use the above sensors and the map provided by the helicopter to traverse the region, locate and isolate simulated land mines. The base station consists of a laptop that provides acommunication link between the aerial and ground vehicle systems. It also provides the operator with system operation information and statistics. All communication between the vehicles and the base station is performed using JAUS (Joint Architecture for Unmanned Systems) messages. The JAUS architecture is employed as ameans to organize inter-vehicle and intra- vehicle communication and system component hierarchy. The purpose of JAUS is to provide interoperability between various unmanned systems and subsystems for both military and commercial applications. JAUS seeks to achieve this through the development of functionally cohesive building blocks called components whose interface messages are clearly defined. The JAUS architecture allows for alayered control strategy which has specific message sets for each layer of control. Implementation of the JAUS architecture allows for ease of software development for amulti-vehicle system. This experiment will demonstrate how an air-ground vehicle system can be used to cooperatively locate and dispose of simulated mines. Keywords: cooperative, helicopter, autonomous, ground vehicle, UAV-UGV collaboration 1. INTRODUCTION Recently unmanned aerial vehicles (UAVs) have been used more extensively in military operations. The improved perception abilities of UAVs compared with unmanned ground vehicles (UGVs) make them more aractive for surveying and reconnaissance applications. Acombined UAV/UGV multiple vehicle system can provide aerial imagery, perception, and target tracking along with ground target manipulation and inspection capabilities. This experiment was conducted to demonstrate the application of a UAV/UGV system for simulated mine disposal operations. The experiment was conducted by surveying the target area with the UAV and creating amap of the area. The aerial map was transmied to the base station and post-processed to extract the locations of the targets and develop waypoints for the ground vehicle to navigate. The ground vehicle then proceeded to each of the targets, which simulated the validatation, and disposal of the ordnance. Results include the aerial map, processed images of the extracted ordnances, and the ground vehicle's ability to navigate to the target points. 2. MATHEMATICS Figure 2: Relation between apoint in the camera and global reference frames Dividing both sides of (5) by ZC and substituting ZW = 0 (assuming the elevation of the camera is evaluated as the above ground level and the uxo location exists on the ZW = 0 global plane) results in (6 Ze Ze Ze Ze Z- Ze Ze Ze Ze Z Z 33 32 31 Rv Ru RP Zn n Coz GC + + ' = (11) ( ) Cox Gn nn n Coz GG PR v R u R Rv Ru RP X+ + + ZYZY Z Zz ZZ Z Z> + + ' = 13 12 11 33 32 31 (12) ( ) Coy Gn nn n Coz GG PR v R u R Rv Ru RP Y+ + + ZYZY Z Zz ZZ Z Z> + + ' = 23 22 21 33 32 31 (13) Equations (12, 13) provide the global coordinates of the UXO. These coordinates were then used as waypoints for UGV navigation.2.2. A* Waypoint Path Planning Once aset of waypoints was provided by the UAV, the UGV was programmed to visit every waypoint as if to simulate the automated recovery/disposal process of the UXOs. The recovery/disposal process was optimized by ordering the waypoints in amanner that would minimize the total distance traveled by the UGV. This problem was similar to the traveling salesman optimization problem in which aset of cities must all be visited once while minimizing the total distance traveled. An A* search algorithm was implemented in order to solve this problem. The A* search algorithm operates by creating adecision graph and traversing the graph from node to node until the goal is reached. For the problem of waypoint order optimization, the current path distance estimated distance to the final waypoint hE , and the estimated total distance fE was evaluated for each node by ' = glenh of straight line segments of all predecessor waypoints (14) hE = (minimum distance of any two waypoints (successors & current waypoints i- of successors) (15) hg fE + = . (16) The requirement for the A* algorithm of the admissibility of the hE heuristic is fulfilled due to the fact that there exists no path from the current node nto agoal node with adistance less than hE . Therefore the heuristic provides the minimum bound as required by the A* algorithm and guarantees optimality should apath exis3. EXPERIMENTAL TESTBED3.1. Unmanned Ground Vehicle Specifications The unmanned ground vehicle platform was an automated all terrain vehicle. It was developed using astock two cycle gasoline powered Suzuki Mini-Quad ATV i 2) . The vehicle's existing steering was equipped with an Animatics servo motor with integrated encoder, controller, and amplifier. Throle and brake cables were actuated using two RC style servo motors. In addition, the vehicle was equipped with onboard and RF remote kill switches, 1KW gas powered Honda generator, UPS power backup system, and power regulation/conditioning electronics. The UGV was equipped with two computing nodes. The POS computing node interfaced to the GPS, digital compass, and encoder to provide the position and orientation information to the system. The SSC computing node handled high level communication with the base station as well as vehicle control and automation tasks. 3.1.1. System Architecture The UGV system consists of abase station (laptop computer the vehicle, and all of its onboard sensors and computers. Autonomous vehicle navigation is realized using acombination of the following sensors: Garmin GPS 16, PNI digital compass, and an incremental encoder. Filtering of all of these sensors provides the vehicle with repeatable and accurate navigation to two meters. The UGV receives all of its commands from the base station. All communications are via wireless Etherne 3.2. Unmanned Aerial Vehicle Specifications The unmanned aerial vehicle was composed of a Miniature Aircraft Gas Xcell RC helicopter. The helicopter had asix foot rotor diameter and can lift approximately 15 pounds. Acustom built sensor/electronics payload was integrated into the airframe of the aircraft and provided image data, position, and orientation information. 3.2.1. System Architecture The aircraft sensor payload was designed to be completely modular. The system had self contained power, computation, data storage, and communication components. It had custom Lithium Polymer baery packs which provided power to all of the onboard components. The system had a Garmin GPS 16A commercial 5Hz WAAS gps which provided the global position of the aircraf and a PNI digital compass which provided the heading and aitude of the aircraf The system was also equipped with a Videre Stereovision System which allowed for high resolution stereo and monocular images to be collected. The system was also equipped with dual flash memory drives which provided robust data storage even in the aircraft's dynamic vibration environmen During testin the data was collected in sync where the images, GPS position, and orientation were all collected at the same instant and stored for post processin The system was also equipped with awireless Ethernet communication which allowed for streaming video and status information to be viewed at the base-station. Figure 2: Tail Gator and Heli Gator Platforms4. EXPERIMENTAL SETUP4.1. Waypoint Surveying In order to evaluate the performance of the UAV/UGV system, the waypoints were surveyed using a Novatel RT-2 differential GPS. This system provided two centimeter accuracy or beer when provided with abase station correction signal. Accurate surveying of the visited waypoints provided abaseline for comparison of the results obtained from the helicopter and the corresponding path the ground vehicle traversed. The UXOs were simulated to resemble BLU-97 ordnance. Aerial photographs i 3) of the ordnance along with the camera position and orientation were collected. Using the transformation described previously the global coordinates of the UXOs were calculated. The calculated UXO positions were compared with the precision survey data. Figure 3: Aerial photograph of all UXOs 4.2. Local Map Alocal map of the operating region was generated using the precision survey data. This local map i4) provided abaseline for all of the position comparisons throughout this paper. 3280320 3280325 3280330 3280335 3280340 3280345 3280350 3280355 3280360 368955 368960 368965 368970 368975 368980 368985 368990 368995 369000 Easting (m) No rthi ng ( m) Diff. Waypoints Boundaries Figure 4: Local map generated with Novatel differential GPS The UGV is able to navigate within several meters of the waypoints, however, is limited due to the vehicle kinematics. Further work involves awaypoint sorting algorithm that accounts for the turning radius of the vehicle.6. CONCLUSION This paper has presented the research involving UAV/UGV collaboration for simulated mine disposal. The results obtained demonstrate that the UAV system can be used to determine the UXO positions using aerial imagery. The experiment then showed that a UGV could then navigate to the UXO positions based on the UAV data. This system utilizes the perception abilities of UAV systems and has demonstrated apossible application in aheterogeneous multiple vehicle system. The results show that the UGV is greatly affected by its inherent kinematic constraints. Due to its Ackermann steering system, the path that the UGV can traverse is limited by the minimum turning radius. The analysis did not include kinematic constraints into the waypoint sorting algorithm. Also the UGV control algorithm was waypoint based versus acontinuous path based approach. Future research will incorporate UGV kinematic and dynamic constraints into the vehicle control strategies. In addition, the optimal path search will consist of aseries of continuous path segments rather than discreet waypoin Both of these changes to the current approach hope to improve overall system performance. ACKNOWLEDGEMENTS This research was conducted in collaboration with the Tyndall Air Force Base Robotics Research Laboratory in Panama City, Florida. REFERENCES 1. Bougue JCamera Calibration Toolbox for MATLAB(R), hp:www.vision.caltech.edu/bouguetj/calib_doc 2. Crane, Duffy, JKinematic Analysis of Robot Manipulators, Cambridge University Press, 1998. 3. Faugeras, OLuon QThe Geometry of Multiple Images, The MIT Press, 2001. 4. Nilsson, NArtificial Intelligence: ANew Synthesis, San Francisco: Morgan Kaufmann, 1998. 2.1. Camera Model and Geo-Positioning For this application aprecise camera model and an image to global coordinate transformation were developed. This involved finding the intrinsic and extrinsic camera parameters of the camera system aached to the aerial vehicle. The intrinsic camera parameters were determined using acamera calibration toolbox for MATLAB(R) 1 . Arelation between the normalized pixel coordinates and coordinates in the projective coordinate plane was used: Ze Ze Z- ZeZe Z Z 5. RESULTS The data collected compares the positioning ability of the UGV and the ability of the UAV sensor system to accurately calculate the UXO positions. While both the UGV and UAV use WAAS enabled GPS there is some inherent error due to vehicle motion and environmental affects. The UGV's control feedback was based on waypoint to waypoint control versus apath following control algorithm. The UGV was commanded to come within aspecified threshold of a waypoint before switching to the next waypoint i 5) . The UGV consistently traveled within three meters or less of each of the desired waypoints which is within the error envelope of typical WAAS GPS accuracy. 3280320 3280325 3280330 3280335 3280340 3280345 3280350 3280355 3280360 368955 368960 368965 368970 368975 368980 368985 368990 368995 369000 Easting (m) No rthing ( m) Diff. Waypoints Boundaries UGV Figure 5: Acomparison of the UGV's path to the differential waypoints The UAV calculates the waypoints based on its sensors and these points are compared with the surveyed waypoints. There is an offset in the UAV's data due to the GPS being used and due to error in the transformation from image coordinates to global coordinates i 6) . In addition, the waypoints that the UAV determined were given directly to the UGV once they were sorted i 7) . 3280320 3280325 3280330 3280335 3280340 3280345 3280350 3280355 3280360 368955 368960 368965 368970 368975 368980 368985 368990 368995 369000 Easting (m ) No rthi ng ( m) Waypoints Boundaries UAV 3280320 3280325 3280330 3280335 3280340 3280345 3280350 3280355 368960 368965 368970 368975 368980 368985 368990 368995 Easting (m) No rt hing ( m) UAV UGV Figure 6: UAV vs. Differential GPS Figure 7: UAV waypoints vs. UGV path


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