3D Obstacle Avoidance in Adversarial Environments for Unmanned Aerial Vehicles

Transcription

1 AIAA Guidance, Navigation, and Contol Confeence and Exhibit - August 6, Keystone, Coloado AIAA 6-65 D Obstacle Avoidance in Advesaial Envionments fo Unmanned Aeial Vehicles M. Stewat Geye * and Eic N. Johnson Geogia Institute of Technology, Atlanta, GA, As unmanned aeial vehicles (UAVs) ae consideed fo a wide vaiety of militay and commecial applications, the ability to navigate autonomously in unknown and hazadous envionments is inceasingly vital to the effectiveness of UAVs. Reliable and efficient obstacle detection is a fundamental peequisite to pefoming autonomous navigation in an unknown envionment. Taditional two-dimensional (plana) obstacle detection techniques, though computationally fiendly, ae often insufficient fo safe navigation though complex envionments in which commanded tajectoies ae simultaneously esticted vetically and hoizontally by multiple buildings o by inceases in teain elevation. To this end, a pan/tiltmounted lase angefinde is exploed as a means of identifying and chaacteizing potential obstacles in thee dimensions (D). Fom GPS position data and inetial senso measuements, the filteed lase angefinde data ae tansfomed into local inetial coodinates and compiled into a dynamic thee-dimensional gid-based mapping of the specified domain. Utilizing the gid-based map data, path planning algoithms geneate the necessay obstacle avoidance tajectoies. The Geogia Tech GTMax UAV helicopte and simulation envionment povide a suitable test-bed fo veification of the poposed obstacle detection and avoidance methodology. M F i = {x i,y i,z i } F b = {x b,y b,z b } F = {x,y,z } q = [q,q,q,q ] T R o = [X o, Y o, Z o ] T = [x, y, z] T R = [X, Y, Z] T θ Ψ i j Nomenclatue Calibated lase angefinde measuement Inetial efeence fame Body efeence fame Lase angefinde efeence fame Attitude quatenions of F b elative to F i Position vecto of the vehicle cente of mass measued fom the datum Position of the lase angefinde measuement elative to the vehicle cente of mass Position of the lase angefinde measuement elative to the datum Lase angefinde tilt angle Lase angefinde pan angle gid-based map ow index gid-based map column index I. Intoduction NMANNED aeial vehicles (UAVs) have become inceasingly useful platfoms fo a vaiety of militay and U civilian applications. UAVs enjoy distinct advantages ove conventional full-scale aicaft in the fom of educed size and weight, lowe opeating costs, inceased maneuveability, and the lack of pilot safety constaints associated with manned opeations. Combat suppot, emegency seach and escue, and aeial suveillance ae paticula applications which benefit geatly fom the use of unmanned aeial vehicles. In many such applications, UAVs must pefom complicated tasks in the absence of an on-site human opeato. Ideally, the envionment aound a specified vehicle flight path is well known. With knowledge of the envionment a pioi, GPS waypoints can be geneated so as to cicumvent known obstacles; howeve, fo navigation in unknown envionments, additional * Gaduate Reseach Assistant, Aeospace Engineeing, Atlanta GA, Student Membe AIAA, sgeye@gatech.edu. Lockheed Matin Assistant Pofesso of Avionics Integation, Aeospace Engineeing, Atlanta GA, Membe AIAA, Eic.Johnson@aeospace.gatech.edu. Copyight 6 by by the Authos. Published by the, Inc., with pemission.

2 capabilities ae equied. Without a human opeato in the loop to peceive and navigate aound potential hazads, the vehicle is susceptible to damage o total loss. The concept of eal-time autonomous navigation with obstacle avoidance equies a sophisticated obstacle detection system to povide updated and accuate obstacle data to path planning algoithms. The obstacle avoidance poblem has been investigated extensively by the obotics community in a two-dimensional (D) famewok epesentative of the lateal natue of maneuves in obot navigation. These studies suggest the use of passive sensing in the fom of a steeo camea fo obstacle detection. - As images ae captued by the camea, image pocessing algoithms use combinations of textue, geomety, and colo ecognition to identify potential obstacles within the image. While sufficient fo gound obot applications, many of these vision-based methods ae deficient fo high-speed platfoms due to thei significant computational equiements. It follows that the notion of using machine vision as a stand-alone means of obstacle detection is somewhat limited by the cuent state-of-the-at of computing powe. While a substantial amount of wok has been devoted to addessing the computational deficiencies of existing vision techniques, othe eseaches have taken diffeent appoaches to solving the obstacle detection poblem. One such appoach involves the use of a lase anging device as an altenative and/o supplement to compute vision. Recently, the DARPA Gand Challenge has spawned a consideable amount of obstacle avoidance eseach with paticula attention paid to High Mobility Multipupose Wheeled Vehicles (HMMWV). -7 HMMWVs, much like UAVs, must negotiate hazadous envionments at high speeds with a high degee of autonomy. In tun, much of the theoy developed fo DARPA Gand Challenge vehicles is adaptable to UAV platfoms. It is impotant, howeve, to point out some of the incompatibilities between HMMWV and UAV obstacle detection measues. The majoity of HMMWV senso systems use a LADAR Range Imaging Camea as the pimay obstacle detection tool. These LADAR systems ae geneally positioned so that the two-dimensional lase band intesects the gound plane at a specified distance in font of the vehicle. In this configuation, an obstacle is defined by a collection of suface points which ae highe than some efeence gound plane. 5 This chaacteization is unsuitable fo UAV applications whee the notion of a efeence gound plane fo obstacle definition is not elevant. In addition, the gound vehicle navigation poblem is inheently two-dimensional by natue, wheeas the UAV guidance poblem intoduces an additional degee of feedom. Fo the most pat, HMMWV motion is constained to a (D) plane paallel to the local gound the motion is fowad o backwad and left o ight to avoid obstacles. On the othe hand, UAV navigation solutions include fowad o backwad, left o ight, and up o down motion. Theefoe, D obstacle mapping methods associated with HMMWV navigation must be augmented fo applications involving D navigation. The eseach pesented in this pape builds upon pevious wok in the field of unmanned vehicle obstacle avoidance. In paticula, a single-beam lase is pesented as low-cost altenative to moe expensive fast scan LADAR systems. Results fom peliminay flight tests ae pesented as veification of the lase angefinde as an effective obstacle detection device. In addition, vaious lase angefinde scanning schemes ae discussed in conjunction with gid-based mapping methods conducive to thee-dimensional obstacle avoidance and path planning. Finally, the elevance and continuance of this eseach is descibed in Section IX. II. Expeimentation The effectiveness of the poposed obstacle detection scheme is detemined fom simulation and flight test esults. Due in lage pat to its low-speed flight capabilities and maneuveability, the GTMax UAV helicopte is a suitable test-bed fo obstacle avoidance expeimentation. In ode to simulate a tue hazadous envionment, D man-made obstacles ae constucted and appopiately positioned at the Geogia Tech UAV test site. The GTMax is commanded to fly seies of tajectoies in close poximity to these obstacles while pefoming active detection and dynamic mapping of the hazads. To supplement the eal-wold flight test esults, vaious othe obstacle schemes ae ceated in the simulation envionment. Vitual tajectoies ae commanded and followed by the simulated GTMax helicopte while the vitual obstacles ae detected and mapped. Accuate modeling of the pan/tilt module, the lase angefinde, and the helicopte dynamics within the simulation envionment povides a eliable means of collecting a vaiety of data without the time and cost associated with flight tests. Ultimately, the effectiveness of the poposed obstacle detection method is detemined by whethe o not obstacles ae eliably detected and mapped in eal-time so as to allow fo continuous tajectoy tacking and/o obstacle avoidance. The following text povides an oveview of the test vehicle and simulation envionment used in expeimentation. In addition, peliminay flight test data ae pesented. These peliminay esults, in pat, seve to validate the lase angefinde-pan/tilt combination as an effective platfom fo obstacle detection.

3 A. Vehicle and Hadwae Desciption The Geogia Institute of Technology GTMax helicopte, as seen in Figue, is the pimay vehicle fo expeimentation. In its nominal configuation, the GTMax eseach UAV is a modified Yamaha R-Max RPH equipped with dual flight computes, a diffeential GPS eceive, an inetial measuement unit (IMU), and a -axis magnetomete. An adaptive neual-netwok feedback contolle and a sixteen-state extended Kalman filte ae used to accomplish guidance and contol. 8 Fo this paticula expeiment, a pan/tilt appaatus is mounted to the fowad fame of the GTMax (it should be noted that vibation isolatos ae incopoated into the mounting hub to attenuate vibations caused by the engine and oto otation). To allow fo ange of motion of the senso, the Figue. GTMax Reseach UAV. lase angefinde device is mounted into the pan/tilt appaatus as seen in Figue. Mounting the angefinde to the pan/tilt allows the helicopte to maintain constant attitude while peipheal obstacle detection occus. The lase angefinde itself is an Opti-Logic RS model. The lase angefinde opeates at Hz and is ated fo measuements of up to yads. The angefinde outputs the measued distance eadings and intefaces with the onboad flight compute though an RS- compatible pot. B. Simulation Envionment The simulation envionment, developed by the Geogia Tech UAV Laboatoy, is pogammed pimaily in C/C++ and is designed to un on both Windows and Linux machines. The simulation includes the aicaft model, the aicaft inteface model (Yamaha Attitude Contol System o YACS), and senso models fo the IMU, GPS, sona altimete, magnetomete, YACS, camea, and lase angefinde. Incopoated into the senso models ae eos, mounting location and oientation, time delays, and digital intefaces. The GTMax helicopte model has six degees of feedom as well as engine, fuel, landing gea, pan/tilt, and oto dynamics. The aicaft inteface model simulates the sevo inteface functionality and the RS- seial inteface. Figue illustates the D gaphics window which, among Figue. GTMax simulation envionment. othe functions, displays the aicaft, the local teain, commanded tajectoies, flight paths, and the aicaft systems status panel. 9 The simulation envionment allows fo the eal-time display of all simulated onboad data, including the vitual lase angefinde measuements. Obstacles such as the one seen in Figue can be ceated and manually positioned in the simulation envionment. The poposed obstacle detection and avoidance algoithms ae then able to be tested on these vitual obstacle clustes. Assuming sufficient accuacy of the vehicle and senso simulation models, esults fom both the simulation and the flight test ae adequate fo evaluating the pefomance of the poposed obstacle detection system. C. Peliminay Expeimental Results A seies of peliminay expeiments wee undetaken to evaluate the functionality of the pan/tilt and lase angefinde systems. Specifically, the goal of these expeiments was to detemine the obstacle detection capabilities of the cuent setup and monito the effects of helicopte vibation and auxiliay noise on lase angefinde measuements. To accomplish these objectives, the GTMax was set in a hove at oughly feet above gound level, and the pan/tilt was pogammed to sweep though a seies of gid coodinates ovelaid onto the gound

4 below. Mounted to the pan/tilt appaatus of the GTMax helicopte, the lase angefinde collected ange measuements of the undelying teain at Hz. The ange measuements wee filteed to emove eoneous eadings and conveted into a local inetial coodinate fame efeenced fom the diffeential GPS towe. Fom lase angefinde data, a D teain mapping of the Geogia Tech UAV test site was ceated, as illustated by Figue. The ed-coloed obstacles depicted in Figue coespond to the test site tent and gound contol tuck, while the less ponounced oange peaks coespond to cas paked at the site. These onsite obstacles adequately epesent the type of hazads which might be encounteed in uban seach and escue missions. The esults of these tests indicate that highesolution obstacle detection is viable fo obstacles up to feet away fom the aeial vehicle. At this distance, the on-site hazads ae eadily visible and well defined as indicated by the map in Figue. In addition, the undulation and teain featues thoughout the map ae visible with elatively high esolution. The helicopte vibations and system noise did not significantly coupt the data fo the most pat, filteing poved to be adequate fo the emoval of noise in Figue. D obstacle map fom lase angefinde data. the data. III. Obstacle Detection Fo gound vehicle applications, an obstacle is often defined as any object which extends above the gound plane by a distance which is geate than some pe-defined theshold. It follows that gound vehicles ae allowed to continue though o ove those objects which ae smalle than the obstacle theshold. This definition vaies slightly fom the moe stingent definition of an obstacle equied fo aeial vehicle applications. Fo UAVs, obstacles must be defined as any objects which potude onto the pojected vehicle flight path so as to come into contact with the aeial vehicle. This is an impotant point to conside when adapting aspects of gound vehicle obstacle detection to aeial vehicles. Wheeas contact with shubbey and othe objects might cause little o no damage to a gound vehicle, even small debis could cause substantial damage to an aeial vehicle if allowed to impact the oto blades of a helicopte o the popelle of a fixed-wing aicaft. Fo this eason, thoough scanning of the suounding envionment is paamount to ensuing the safety of aeial vehicles. The following text intoduces vaious lase angefinde scanning schemes and descibes the pocess of tansfoming ange measuements into meaningful data. A. Lase Scanning Schemes The nominal scanning scheme exhibited in Figue povides a thoough scan of the specified domain, whee the bold snaking patten epesents the lase angefinde scanning tajectoy. Fo fowad flight, the scan domain is actively ecenteed about the vehicle velocity vecto (indicated by the dak oval in Figue ) to ensue that obstacles along the vehicle flight path ae eliably detected. In conjunction with the gidbased mapping appoach to be discussed in Section IV, collections of data ae obtained fom Figue. Nominal lase scanning scheme. each gid cell. The thooughness of this paticula scanning scheme, howeve, does not come without tade-off. One disadvantage of this scheme lies in the amount of time it takes to tavese the entie domain gid-wise. This limitation could affect oveall system pefomance since the maximum allowable autonomous flight speed is limited by the speed of obstacle detection opeations. Additionally, though the gid cells immediately suounding the velocity vecto ae often of pimay inteest (since

5 obstacles showing up in these gid cells epesent imminent collision hazads), the nominal lase scanning scheme poduces evenly distibuted data points athe than focusing on specific aeas of inteest. Depending on the flight envionment, altenative scanning schemes could pove beneficial. Figue 5 povides examples of vaious cicula and elliptical scanning tajectoies designed to speed up the obstacle detection pocess and focus moe on obstacles which lie along, o in close poximity to the vehicle flight path (indicated by the shaded oval in Figue 5). The tajectoies displayed in Figue 5 can be used individually o in paallel to povide appopiate scan coveage fo the flight envionment. One such hybid scheme might consist of seveal cycles along the innemost elliptical tajectoy combined with intemittent switching to the oute-most tajectoy to check fo lage obstacles which may have enteed into the domain peiphey. In paticula, these scanning schemes cove a significant amount of the domain in a shot peiod of time without geneating copious amounts of data pe cycle. Instead of sequencing though the entie gid with data points Figue 5. Vaious scanning schemes. collected fom each cell, moe eadings can be taken in aeas of inteest. Though not as thoough as the nominal scanning scheme, the tajectoies diagammed in Figue 5 allow fo smoothe opeation of the obstacle detection system. B. Coodinate Tansfomation In ode to constuct a meaningful obstacle map, lase angefinde measuements ae expessed in a common inetial fame. Fo convenience, the on-site diffeential GPS towe is chosen as the oigin of the local inetial fame. Similaly, the vehicle cente of mass is chosen as the oigin of the aicaft body fame. The lase angefinde fame, denoted by subscipt, is efeenced fom the pan/tilt cente with the x axis along the lase s optical axis. Though a seies of successive coodinate tansfomations, lase angefinde measuements ae conveted into the local inetial efeence fame: [ ] T R = M () L b cosψ = sinψ sinψ cosψ T cosθ sinθ sinθ cosθ T () L ib q + q = ( qq ( qq q q q q ) + q q ) q ( q q ( q q q + q q ) + q q q q ) q ( q ( q q q q q q ) q + q q ) + q T () L i = L L () ib b R = R + L R (5) o Equation () epesents the aw lase angefinde measuements expessed in the fame. L b maps fom the lase angefinde fame to the vehicle body fame in the fom of otation sequences though the pan (Ψ ) and tilt (θ ) angles. The otation sequence, L ib, convets vecto components fom the body fame to the local inetial fame. Finally, lase angefinde measuements ae tansfomed into the local inetial fame though the otation matix L i as illustated in Equation (). In this fomulation, the offset between the oigin of the angefinde fame and the i 5

6 vehicle cente of mass is assumed to be negligible. This assumption seves to educe the numbe of matix multiplications equied fo coodinate tansfomations. IV. Obstacle Mapping In ode to extact eal-time tends fom the obstacle data, lase angefinde measuements ae systematically chaacteized and updated in a dynamic database. An adaptation of Kelly s gid obstacle map system fo gound vehicles is used fo epesenting and cataloging potential obstacles. 7 The Wold Model Repesentation foms the backbone of the gid-based map system; howeve, in the gound vehicle model, the wold is pojected onto a gound plane which does not allow fo obstacle epesentation in thee dimensions. In ode to facilitate D obstacle avoidance fo UAV applications, the notion of the gound plane gid-based map is shifted fom the gound plane to a plane pojected in font of the aeial vehicle and nomal to the velocity vecto (o the heading vecto in the case of stationay flight). The subsequent text descibes the gid-based mapping scheme in detail. A. Gid-Based Map A modified vesion of Kelly s gid obstacle map povides a systematic means of stoing lase angefinde data. As ange measuements ae etuned fom the lase angefinde, they ae stoed in a matix data stuctue which mios the physical gid-based map pojection plane. Each element of the matix contains the ange measuement location in Catesian coodinates, its measued distance fom the vehicle, and an associated confidence ating. Within the map pojection plane, a window though which the vehicle can safely pass is defined. The cells encompassed by this window, and the cells immediately suounding the window, ae of paticula inteest. Any obstacle within this theshold epesents an imminent collision hazad. With this in mind, two citeia ae employed in detemining whethe a gid cell (o pixel) contains an obstacle. The pimay obstacle citeion consists of a check of whethe an obstacle cell in the pojected vehicle window is within a pe-defined distance fom the vehicle. Such a scenaio indicates that an obstacle lies in the vehicle flight path and is within close ange of the vehicle. In this case, the associated cells eceive votes as containing obstacles. A second obstacle detection citeion involves compaing neighboing pixel data. Standad gadient seach algoithms povide a means of identifying neighboing gid cells which contain obstacles. If the diffeence between angefinde measuements in adjacent gid cells, i.e. (i, j ), exceeds a pe-defined value, this cell eceives a vote as containing an obstacle. The obstacle map data is continually updated in a simila manne as the lase angefinde sweeps out the suounding teain. B. Map Scolling As cited peviously, fo fowad flight, the scan domain is actively e-centeed about the vehicle velocity vecto to ensue that obstacles along the vehicle flight path ae eliably detected. This method is advantageous in that it minimizes gid elocation the matix data stuctue is expanded to include new senso data while pevious pixel data emains stoed in its oiginal map cell. In the event that the matix data stuctue becomes too lage (i.e. the vehicle changes heading completely) a new data stuctue is initialized and an obstacle map is built fo the new domain. C. Confidence Algoithm The dynamic aspect of the gid-based map is based on the concept of confidence-based mapping. In this scheme, the cell confidence inceases o deceases linealy when updated by a angefinde measuement. When a map cell eceives a vote fo containing an obstacle, the cell s confidence level is inceased by a pe-defined constant. When the confidence exceeds a cetain theshold, the map cell is maked as an obstacle cell. If a map cell eceives a conflicting vote, its confidence level deceases. This system seves to educe the amount of false obstacle identifications Figue 6. Obstacle map hazad scenaio. as well as phase out dynamic obstacles which may have moved away fom pevious cell locations. As the obstacle detection and map builde pocesses ae caied out, clustes of gid cells ae eventually maked as no-fly zones. Figue 6 illustates a possible obstacle scenaio whee the ed cells indicate no-fly zones due to 6

7 tees in the uppe left- and ight-hand cones, and shubbey along the lowe ight potion of the scan domain. Fom the obstacle map, path planning algoithms ae then used to stee the vehicle towad aeas which ae sufficiently clea of no-fly zones. V. D Obstacle Avoidance Though often insufficient fo navigation in complex obstacle-ich envionments, the development of twodimensional obstacle avoidance schemes povides a logical stating point fo solving the D avoidance poblem. Wheeas hoizontal avoidance maneuves ae often pefeable fo fixed-wing vehicles, vetical avoidance maneuves ae moe appopiate fo VTOL (vetical take-off and landing) vehicles due to thei inheent advantage in vetical thust capability. As such, the poposed D avoidance scheme consides avoidance maneuves in the vetical plane, though these stategies ae equally applicable to flight in the hoizontal plane. In addition, the following D avoidance methods ae pedicated upon single point lase angefinde measuements athe than the full scale obstacle gid map detailed in Section IV. A. Flight Modes The poposed D avoidance scheme consists of thee fundamental flight modes: a nomal flight mode, an obstacle avoidance mode, and a etun-topath mode. The nomal flight mode efes to the uninteupted tacking of use-commanded waypoints o velocities as the lase angefinde seaches fo obstacles along the vehicle s pedicted path. If the obstacle avoidance mode is tiggeed by a detected obstacle along the pedicted vehicle tajectoy, the use-commanded path is pe-empted by an avoidance path geneated fom lase angefinde obstacle data. Once the detected obstacle is detemined to have been successfully avoided, the etun-to-path flight mode initiates the eacquisition of the oiginal use-commanded path. Figue 7 depicts the decision tee fo the vaious obstacle avoidance modes. The state of the detection flag paamete acts as the pimay dive Figue 7. Avoidance mode logic tee. of flight mode. When a detected obstacle passes within the specified eminent collision bounday duing the cuent o pevious time step, the nomal flight mode is inteupted by the avoidance mode. If no additional obstacles ae detected along the pedicted vehicle path afte the avoidance tajectoy is flown, the etun-to-path mode is initiated and the oiginal use-commanded tajectoy becomes the pimay flight path once again. B. D Avoidance Tajectoy The D avoidance method is based on the stategic manipulation of the commanded vehicle path in the event of peceived obstuctions along the cuent vehicle tajectoy. Fo navigation in advesaial envionments whee pefomance and speed ae at a pemium, avoidance tajectoies must be both smooth and computationally fiendly. With these specifications in mind, Bézie cuves povide an effective means fo avoidance tajectoy geneation. Specifically, because Bézie cuves ae contained within the envelope of thei contol points, these points can be gaphically displayed and used to manipulate the cuve intuitively. Futhemoe, tanslations, scaling, and otations can be applied on Bézie cuves, making them eadily extendable to thee-dimensional path planning. The geneal fom of the paametic Bézie cuve is: Figue 8. Cubic Bézie cuve. 7

8 B n ( u) Pb ( u), u [, = i= i n, i ] (6) whee n is the ode of the Bézie cuve, P i epesents the i th contol point, u is the paametic vaiable, and b n, i n i i n i ( u) = t ( t) (7) Fo smooth tajectoy geneation applications, cubic Bézie cuves such as the one illustated in Figue 8 ae highly effective given thei elatively low computational equiements. Fo n =, the matix fom of the Bézie cuve equation is B ( u) = ( u, u, u, ) 6 P P P P (8) The fou contol points P, P, P, and P ae selected so as to geneate a smooth tajectoy which effectively avoids the detected obstacle. Keeping in mind that the thid-ode Bézie cuve cosses points P and P but not points P and P, it is convenient to position P at the cuent vehicle location and it is citical to position P at a point veified as safe fo the vehicle to fly. Refeing to Figue 8, P denotes the cente of P P and is computed solely fo efeence puposes. Additionally, P is placed at P = P P P P P P + v v (9) whee v is the velocity vecto of the vehicle. This selection of P allows fo sufficient emegent esponse of the vehicle. Finally, P is positioned at the intesection between P P and the hoizontal line paallel to v and coss P. Fo each instance that an obstacle is detected along the geneated avoidance path, the position of the safe point, P, is incemented vetically by a pe-defined value, and P, P, and P ae e-computed accodingly. Afte a sufficient numbe of vetical incements, P is positioned safely above the detected obstacle, and the coesponding avoidance Bézie cuve is flown by the vehicle. VI. D Obstacle Avoidance Fo navigation in and aound clutteed obstacle-ich envionments, the poposed D obstacle avoidance methods ae often insufficient. Cetain scenaios may, fo instance, dictate the need to simultaneously navigate above and aound a goup of obstacles. Fo such scenaios, the pinciples of the poposed D avoidance methods must be expanded to encompass the thee-dimensional domain. A. Flight Modes The pimay diffeence between the poposed two- and thee-dimensional obstacle avoidance methods is the incopoation of the gid-based obstacle map into the D avoidance scheme. The poposed thee-dimensional avoidance scheme consists of fou flight modes: a nomal flight mode, a data acquisition mode, an obstacle avoidance mode, and a etun-to-path mode. The definitions of the nomal flight, obstacle avoidance, and etun-topath modes ae the same as those of thei two-dimensional countepats; howeve, whee the D avoidance stategy initiated a mandatoy climb upon encounteing an obstacle, the data acquisition mode of the D avoidance scheme seeks a definitive avoidance oute by appopiately expanding the obstacle map scan domain athe than embaking 8

9 upon an abitay avoidance tajectoy. The data acquisition mode is initiated when the gid-based obstacle map consists of moe than a pe-defined numbe obstacle cell clustes. Fo such a scenaio, the cuent scan domain is deemed unsafe fo passage of the vehicle, and the scan domain is successively expanded until the extended obstacle map domain yields a feasible avoidance oute. Once an appopiate avoidance oute is detemined, the obstacle avoidance mode is tiggeed, and the obstacle is successfully avoided as outlined in the following section. B. D Avoidance Tajectoy As was peviously stated, Bézie cuves ae eadily extendable to thee-dimensional path planning and, theefoe, fom the backbone of the D avoidance tajectoy. Wheeas the D scheme employs only single point lase angefinde data to coectly place the Bézie contol points, the D avoidance scheme utilizes the gid-based obstacle map discussed in Section IV to detemine the safest location fo P. The location of P is detemined fom obstacle map seach algoithms designed to calculate the point on the obstacle gid which minimizes susceptibility to obstacle Figue 9. Diffeence between D and D methods. collisions while maximizing tajectoy efficiency. Once the P contol point location is detemined fom the obstacle map, the emaining contol points ae calculated in the same manne as wee the two-dimensional contol points (see Equation 8 and accompanying text); howeve, the new Bézie cuve is no longe esticted to the vetical plane. Because P can be placed at any point within the obstacle gid, the avoidance Bézie cuve has an additional degee of feedom allowing fo moe efficient obstacle avoidance. Figue 9 illustates the diffeence between the poposed D and D avoidance methods. Wheeas the plana estictions on the D avoidance method would esult in a tajectoy which inefficiently goes all the way ove the detected building (indicated in yellow), the D avoidance method yields in a moe pactical avoidance path as illustated by the geen cuve in Figue 9. VII. Results Vaious simulation and flight tests wee conducted to veify the poposed obstacle detection and avoidance methods. The subsequent text details the esults of obstacle detection, D avoidance, and D avoidance expeimentation. A. Obstacle Detection Figue depicts the nominal setup used to test both the gid-wise and concentic lase angefinde scanning schemes. Fo this paticula setup, the vitual GTMax UAV pefomed stationay scans of a building at distance of oughly feet. Figues and, espectively, display esults fom the gid-wise and concentic lase angefinde obstacle scan tests. The ed field-of-view window seen in Figue epesents the gid-wise scan domain, while the individual blue data points depict the lase angefinde measuements by which the building, o obstacle, is detected. Similaly in Figue, the blue data points show whee, and to what poficiency, the concentic scanning scheme detects the building obstacle. Refeing to Figue, it can be seen that the Figue. Nominal setup fo obstacle detection testing. gid-wise scan scheme povides a thoough mapping of the scan domain insofa as the detected obstacle is eadily identifiable with distinct boundaies. On the othe hand, the poficiency of the gid-wise scan is not without consequence as the time to execute one cycle of the gidwise scan is nealy twice the time to complete a single concentic scan cycle; howeve, as evidenced by Figue, lage aeas of the obstacle emain undetected as a esult of the limited scope of the concentic scan scheme. 9

10 Figue. Gid scan of nominal building. Figue. Concentic scan of nominal building. B. D Obstacle Avoidance The following section pesents esults fom two-dimensional obstacle avoidance testing. The baseline mission fo the D avoidance simulation consists of a vehicle in a constant velocity fowad flight mode Figue. Single building D avoidance Figue. Multiple building D avoidance Figue 5. Avoid two sequenced buildings Figue 6. Avoid thee sequenced buildings.

11 Refeing to the vaious avoidance scenaios depicted in Figues -6, the blue cuve epesents the avoidance tajectoy flown by the vehicle while the yellow path indicates the oiginal commanded tajectoy. The ed-coloed taces seen in Figue highlight the lase angefinde ay and the coesponding obstacle detection points. In addition, these taces seve to illustate the successive vetical incementing of the P contol point until the contol point is safely above the obstacle. Futhemoe, the ed taces that appea on top of the building in Figue depict the vehicle tying to etun to the oiginal path once the avoidance tajectoy has been successfully flown. Fom Figues -6, it can be said that the poposed D avoidance scheme, while somewhat limited in scope, is effective fo simple obstacle avoidance. C. D Obstacle Avoidance Figues 7 and 8 depict the thee-dimensional obstacle scenaio and avoidance path flown by the GTMax UAV in simulation. Figue 7 shows the obstacle encounteed by the UAV while Figue 8 offes a view of the UAV fom behind the detected obstacle, o building. Detemined fom the gid-based obstacle map seen in the lowe ighthand cone of Figue 7, the Bézie cuve contol point, P, is safely positioned in the obstacle-fee notch of the Figue 7. D avoidance scenaio. Figue 8. D avoidance tajectoy. obstuctive building. Fom this point, the emaining Bézie contol points ae computed to poduce the yellow theedimensional avoidance tajectoy seen in Figue 8. Fom Figue 8, it is clea that the vehicle simultaneously navigates aound and ove the obstuctive stuctue athe than inefficiently attempting to climb ove the highest point of the building. Futhemoe, it can be seen that the geneated avoidance cuve is elatively smooth enabling efficient flight along the tajectoy. VIII. Conclusions Though not as efficient as the concentic obstacle scan, the gid-based obstacle scan poved the moe eliable method fo obstacle detection. In addition, simple pointing of the lase angefinde along the vehicle velocity vecto poved an effective means of detecting obstacles duing flight. Onboad vibations and pitching motions of the vehicle seved to effectively petub the lase angefinde beam enough to collect obstacle data fom a athe boad field of view. The poposed D avoidance methods wee poven effective in thei own ight; howeve, testing indicated that the ability to smatly geneate avoidance tajectoies with the thee-dimensional avoidance appoach epesents a distinct advantage ove the D avoidance schemes which ae less obust and tend to beak down in complex obstacle-ich envionments. IX. Futue Wok A poposal fo futue wok includes moe compehensive testing both in simulation and flight tests. In addition, methods will be developed to account fo moe complicated obstacle scenaios, such as non-convex obstacles. A final poposal fo futue wok involves the development of a full-scale inetial obstacle map of the suounding envionment that intefaces with the local gid-based obstacle map to futhe coodinate smat path planning in, and aound, obstacle-ich envionments.

12 Acknowledgments The authos would like to acknowledge contibutions of the following people to the wok pesented in this pape: Henik Chistophesen, Claus Chistmann, Jason Fine, Manabu Kimua, Wayne Pickell, Nimod Rooz, and Allen Wu. This wok is suppoted by the Active-Vision Contol Systems (AVCS) Multi-Univesity Reseach Initiative (MURI) Pogam unde contact #F96--- and by the Softwae Enabled Contol (SEC) Pogam unde contacts #65-98-C- and #65-99-C-5. Refeences Cheng, V. H. L., and Lam, T., Automatic Guidance and Contol fo Helicopte Obstacle Avoidance, AIAA Jounal of Guidance, Contol, and Dynamics, Vol. 7, No. 6, 99, pp Zhu, Z., Lin, X., Shi, D., and Xu, G., A Single Camea Steeo System fo Obstacle Detection, Wold Multiconfeence on Systemics, Cybenetics and Infomatics - th Intenational Confeence on Infomation Systems Analysis and Synthesis, Vol., -6 July, 998, pp. -7. Cho Y. C., and Cho H. S., A Steeo Vision-Based Obstacle Detection Method fo Mobile Robot Navigation, Robotica, Vol., 99, pp. -6. Manduchi, R., Castano, A., Talukde, A., and Matthies, L., Obstacle Detection and Teain Classification fo Autonomous Off-Road Navigation, Autonomous Robots, Vol. 8, 5, pp Talukde, A., Manduchi, R., Rankin, A., and Matthies, L., Fast and Reliable Obstacle Detection and Segmentation fo Coss-County Navigation, IEEE Intelligent Vehicles Symposium, Vesailles, Fance,. 6 Chang, T., et. al., Concealment and Obstacle Detection fo Autonomous Diving, Poceedings of the Intenational Association of Science and Technology fo Development Robotics and Applications Confeence, Santa Babaa, CA, Kelly, A., An Intelligent, Pedictive Contol Appoach to the High-Speed Coss-County Autonomous Navigation Poblem, Ph.D. Dissetation, Robotics, Canegie Melon Univesity, Pittsbugh, PA, Johnson, E. N., and Schage, D. P., System Integation and Opeation of a Reseach Unmanned Aeial Vehicle, AIAA Jounal of Aeospace Computing, Infomation, and Communication, Vol., No.,, pp Wu, A. D., Johnson, E. N., and Pocto, A. A., Vision-Aided Inetial Navigation fo Flight Contol, AIAA Jounal of Aeospace Computing, Infomation, and Communication, Vol., No. 9, 5, pp Hong, T., Abams, M., Chang, T., and Shneie, M. O., An Intelligent Wold Model fo Autonomous Off-Road Diving, Compute Vision and Image Undestanding,. Hong, T., Legowik, S., and Nashman, M., Obstacle Detection and Mapping System, NISTIR 6, August 998. Salomon, D., Compute Modeling & Geometic Modeling, Spinge-Velag New Yok, Inc., New Yok, NY, pp