486 T.M. Howard and A. Kelly 4.1 Rocky 7 Rate Kinematics, Actuator Dynamics, and Wheel Slip The Rocky 7 rover has eight actuators: a steering actuator on each of the front two wheels and six drivable wheels. In order to get a realistic model of actuator dynamics, a first-order lag was assumed. v c out ( t )= α 1 dt τ [ v c in ( t ) − v c out ( t − τ )] + v c out ( t − τ )(17) ψ 1 , 2 out ( t )= α 2 dt τ [ ψ 1 , 2 in ( t ) − ψ 1 , 2 out ( t − τ )] + ψ 1 , 2 out ( t − τ )(18) Thewheelslip model assumes that only afractionofthe requestedvelocityis achieved. Inverse rate kinematics generate theachieved V out , Ω out due to the actuatordynamics and wheel slip model. 4.2 Rocky7Suspension Kinematics Just as in the general solution, the suspension model provides amapping of thelinear and angular velocities estimated by the actuatordynamics and wheel slip models from the body frame to the world frame,except nowthe sus- pension model is known. Taking advantageofthe fact thatthere aresix con- trols (attitude(φ , θ ), altitude ( z ), and the three rocker-bogie angles(ρ , β 1 , β 2 )) and six constraints ( z c 1 - z c 6 ), the same numerical methodused in the inverse trajectory generation solverisapplied. An errorvector ∆z =[∆z c 1 , ···, ∆z c 6 ] T is formed from the difference between the elevationofthe terrain andthe contact pointofeachofthe sixwheels.The initial guessofcontrol is adju- sted by the product of theinverse Jacobianofthe forwardkinematics of the contact points with respect to the world frame andthe elevation errorvector untilthe terminalconditions aremet. 4.3 Rocky7Kinetic Motion Model Since the Rocky7roverisaterrain-following mobile robot, the kinetic model follows the form of equation (13). The attitude and altitude are determined from the suspension model for agiven pose. 4.4 Rocky7TrajectoryKinematics The forward model of trajectory generationfor theRocky 7roverisgenerated by integrating equation (13). Trajectory Generation on Rough Terrain Considering Actuator Dynamics487 5 Implementation 5.1 Forward Solution of Trajectory Kinematics In order to determine the terminal pose ( x f , y f , ψ f ) of the rover, the system dynamics described in equations (14)-(16) must be integrated with respect to time. These state equations are coupled and nonlinear. We have found simple Euler integration to be sufficient: x t + ∆t = x t + v x out t + ∆t cos( θ ( x t ,y t ,ψ t ))cos ( ψ t ) ∆t (19) y t + ∆t = y t + v x out t + ∆t cos( θ ( x t ,y t ,ψ t ))sin ( ψ t ) ∆t (20) ψ t + ∆t = ψ t + cos( φ ( x t ,y t ,ψ t )) cos ( θ ( x t ,y t ,ψ t )) ω z out t + ∆t ∆t (21) ω z out t + ∆t = f ( ω z in t + ∆t )(22) v x out t + ∆t = g ( v x in t + ∆t )(23) Noticethatthe output linearand angular velocities of the rate kinematics are used in this mo del. In th is metho d, ac onfiguration at eac hn ew po se mu st be found. The algorithm canbespedupdramaticallybyusing theprevious con- figuration at time t − ∆t as the seed for the configurationatthe current time t . It is essential that ∆t be small enough to accurately model the integral of the system dynamics and capturethe terrain alongthe path. Forexample, in tra jectory generation ove rt he scale of af ew ro ve rl engths, 12-15 iterations maybeenoughtoaccurately model the system dynamics butthesemay still miss small terrain disturbances that mayinfluencethe path enoughtomatter. 5.2 Tr aj ectory Generation Using In ve rse Tr aj ectory Kinematics Utilizing the forward model of trajectory kinematics developed in the pre- vious section, along with the controls and methods of section 3.1 and 3.2,an inverse trajectory kinematics solverwhichaccounts for both rough terrain and actuatordynamics is obtained. 5.3 Initialization/Termination Sincethe numerical methodused in this work does notguarantee global con- vergence, aheuristic whichplaces the terminal posture of theinitialguess nearthe goal postureisrequired. It wasfound thatthe solutiontothe two- dimensionalt ra jectory generation problemp laces the terminal po sture of the initial guess within 15%ofthe goal on avariety of interesting terrains.This is close enough to ensure convergenceinmost applications. In thecase of 488 T.M. Howard and A. Kelly an exceptional terrain disturbance which incurs a large terminal posture per- turbation, line searches or scaling of the change of parameters ( ∆p) can be implemented to prevent overshoot and divergence from the solution. The set of termination conditions used for the trajectory generation algo- rithm were similar to those in [1], stopping iterations when [ ∆x f , ∆y f , ∆ψ f , ∆ω f ] T =[0. 001m , 0 . 001m , 0 . 01rad , 0 . 01 rad sec ] T .Likewise, the suspension model required millimeter residuals in ∆z c i . 6R esults 6.1 Rough TerrainTrajectoryGeneration Example This section demonstrates the need of rough terrain trajectory generation by examining an example situation. In this example, the Rocky7platform is asked to findacontinuous trajectory between two postures as seen in Figure4. The relativeterminal posture[x f , y f , ψ f , ω f ] T is equal to [3. 0 m ,5. 0 m , π 2 . 0 ra d , 0 . 0 rad sec ] T .Inorder to isolatethe effectofneglecting the influence of attitude in the trajectory generator, rate kinematics are ignored in this example. Fig. 4. Example Rough Trajectory Generation: This figure shows an example tra- jectory generation problem, where a continuous path is desired between an initial posture inside a crater and the final posture just over the lip of the crater. First,the two-dimensional continuous curvaturepath is solvedtomillime- ter accuracy and fed into the three-dimensional forward solution. The two- dimensionalsolution incurs aterminal position error of 6.2%(45.3cm) of the entire arclength of the solution. The three-dimensional trajectory generator finds anew path that is continuous in angular velocity, with an initial and final velocityof1m/sec, formilimeter accuracy in only 3iterations. Figure 5shows the difference between the two-dimensional system model and three- dimensional system mo del paths and ho wt he latter generatest he correct trajectory. Trajectory Generation on Rough Terrain Considering Actuator Dynamics489 Fig. 5. Trajectory Generation Solution: This figure shows the result of neglecting attitude in the forward model (two-dimensional solution) and the new solution based on a three-dimensional system model. By neglecting attitude, the terminal position is off by 6.2% when compared to the total arclength of the solution. 6.2 General Results To ev aluate the need,p erformance, and be ha vior of this algorithm, seve ral thousand tests were run to understand ratesofconvergenceand rangeof errors to expect. One behavior that wasrecognized is that even though error would increase dramaticallywith rougher terrain, the number of iterations required to meet the terminationconditions did not. The numerical method that we areusing attempts to remove all errorinasingle iteration, so this behavior suggests that thefirst order approximation is agood one. 7C onclusions In the context of mobile robots whichmust alreadyexpend significanteffort to understand terrain complexity, the use of aflat terrain assumptionintra- jectory generation is difficulttojustify.However, as thepaper hasshown, the use of terrain information requiresacertain amountofeffort to develop a more complex generation algorithm. While space wasnot available to address acomputation comparison,the additionalcomputation for3Dmodels is not as ignificantfactor in practice. Avery general algorithm has been presented whichcan generate conti- nuouspathsfor mobilerobots obliged to driveoverrough terrain while subject to additional nonidealities suchaswheelslip and actuatordelays. The essen- tial problem is to invert amodel of howparameterized controlinputs,terrain shap e, terrain in teractiona nd actuatord ynamic mo dels determine the termi- nal state of avehicle at all futuretimes. Anumerical technique wasadopted due to the assumedinabilitytoexpress terrain shapeinclosed form. However, once anumerical approachisadopted, it alsomeans that anyforward model 490 T.M. Howard and A. Kelly can be inverted to produce continuous controls subject only to the capacity of the numerical linearization to converge. In principle, a full Lagrangian dy- namics model can be inverted using our technique, for example. The Rocky 7 prototype rover was used to illustrate the application of the general models of suspension and rate kinematics to a specific robot. For any vehicle, only forward rate kinematics and forward suspension models are needed to use the rest of the algorithm. Our results suggest there is much to gain and little to lose by moving to fully 3D models. Such predictive models lead to improved performance by removing as much model error as possible at planning time — so that path following controls are used only to compensate for truly unpredictable disturbances. While the algorithm has been presented in the context of planning com- putations, it promises to be equally valuable for the generation of corrective trajectories in feedback path following controls. Future work will assess the value of the algorithms for this purpose in the hope of developing short term path followers which maximally exploit the model and terrain information which can be assumed to be present in most present and future mobile ro- bots. Acknowledgment This research was conducted at the Robotics Institute of CMU under contract to NASA/JPL as part of the Mars Technology Program. References 1. Nagy, B., Kelly, A., ”Trajectory Generation for Car-Like Robots Using Cubic Curvature Polynomials”, Field and Service Robots 2001 (FSR 01), Helsinki, Finland - June 11, 2001. 2. Simeon, T., Dacre-Wright, B., ”A Practical Motion Planner for All-terrain Mo- bile Robots”. 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems, Yokohama, Japan - July 26-30, 1993. 3. Iagnemma, K., Shibly, H., Rzepniewski, A., Dubowsky, S., ”Planning and Con- trol Algorithms for Enhanced Rough-Terrain Rover Mobility”. International Symposium on Artificial Intelligence and Robotics and Automation in Space 2001 (i-SAIRAS 01), St-Hubert, Quebec, Canada - June 18-22, 2001. 4. H Delingette, M. Herbert, and K Ikeuchi, ”Trajectory Generation with Cur- vature Constraint based on Energy Minimization”, Proc, IROS, pp 206-211, Osaka, Japan, 1991. 5. Shin, D.H., Singh, S., ”Path Generation for Robot Vehicles Using Composite Clothoid Segments” tech. report CMU-RI-TR-90-31, Robotics Institute, Car- negie Mellon University, December, 1990. 6. Tarokh, M., McDermott, G., Hung, J. ”Kinematics and Control of Rocky 7 Mars Rover.” Tech Report. August 1998. 7. Volpe, R., ”Navigation Results from Desert Field Tests of the Rocky 7 Mars Rover Prototype.” International Journal of Robotics Research, 1998. Results in Combined Route Traversal and Collision Avoidance Stephan Roth, Bradley Hamner, Sanjiv Singh, and Myung Hwangbo Carnegie Mellon University. 5000 Forbes Ave., Pittsburgh, PA 15213, USA. { stephan+@cs.cmu.edu, bhamner@andrew.cmu.edu,ssingh@ri.cmu.edu, myung@andrew.cmu.edu} Summary. This paper presents an outdoor mobile robot capable of high-speed navigation in outdoor environments. Here we consider the problem of a robot that has to follow a designated path at high speeds over undulating terrain. It must also be perceptive and agile enough to avoid small obstacles. Collision avoidance is a key problem and it is necessary to use sensing modalities that are able to operate robustly in a wide variety of conditions. We report on the sensing and control necessary for this application and the results obtained to date. Keywords: Outdoor mobile robot, path following, collision avoidance 1I nt ro duction While the use of mobile robots in indoor environments is becoming common, the outdoors stillpresentchallenges beyond the state of the art. This is be- cause the en vironmen t( we ather,t errain, ligh ting conditions) can po se serious issues in perception and control. Additionally,while indoor environments can be instrumented to provide positioning, this is generally not possible outdoors at large scale. Ev en GPS signals are degraded in the presence of ve getation, builtstructures andterrain features. In the most general version of the prob- lem, arobot is given coarselyspecified via points and must find its way to the goal usingi ts ow ns ensors and an yp riori information ove rn atural terrain. Suchscenarios, relevantinplanetary explorationand military reconnaissance are the most challenging because of the manyhazards –side slopes, negative obstacles and obstacles hidden under vegetation –that must be detected. A variantofthis problem is for arobot to followapath that is nominally clear of obstacles but not guaranteed to be so. Suchacase is necessary for outdoor patrolling applicationswhere amobile robot must traveloverpotentially great distances without relying on structures uc ha sb eacons and lane markings. In addition to avoiding obstacles, it is important thatthe vehicle stayonthe designated route as much as possible. P. Corke and S. Sukkarieh (Eds.): Field and Service Robotics, STAR 25, pp. 491–504, 2006. © Springer-Verlag Berlin Heidelberg 2006 492 S. Roth et al. Perception is typically the bottleneck in outdoor navigation, especially at speeds higher than a few meters/sec. This is primarily because perception of small obstacles (as small at 15 cm high) at or beyond the stopping distance ahead of the robot is typically only possible using laser ranging. Laser ranging produces detailed shape of the terrain but is limited in sampling and scanning speed. Here we report on the perception and guidance that we have developed for an outdoor patrolling robot (Figure 1) that uses two low-cost laser scanners to develop an understanding of the world around it. In specific, we report on methods of obstacle detection and collision avoidance for this robot while it travels at speeds at up to 5 m/s. Fig. 1. Grizzlyisanavigationtest-bedbuilt upon acommercially available All TerrainVehicle (ATV). It uses twolaserscanners to perceive the shapeofthe world. The vehicleisequippedwith differential GPS and asix-axis inertial measurement unit that providesaccurate attitude. 2Related Work There has been agreat deal of attention paid to parts of the problem of au- tonomouso pe ration in semi-structured en vironment ss uc ha si np orts[ 6], un- derground mines [9], and highways[3]. In some of these cases, theenvironment can be controlled enough that obstacle detection can be simplified to ensuring that the machines are capable of stopping for peopleorvehiclesized obstacles. Autonomous machines operating in natural environments, however, must be abletodetect several differenttypes of obstaclesincluding side slopes and negativeobstacles. This is accomplished by using sensors that can determine the shap eo ft he wo rld around them. Stereo vision [11], colors egmen tation [1], radar [8] and laser range finders [5] have all been used for obstacle detec- tion. Unfortunately,passive visionsuffers from lighting, color constancy, and Results in Combined Route Traversal and Collision Avoidance 493 dynamic range effects that cause false positives and false negatives. Radar is good for large obstacles, but localization is an issue due to wide beam widths. Single axis laser scanners only provide information in one direction, and can be confounded by unmeasured pitching motion and mis-registration. Two axis scanners are also used, which provide more information, but are very costly. Several systems have demonstrated off road navigation. The Demo III XUV drives off-road and reaches speeds up to 10 meters per second. The speeds are high, but the testing environments are rolling meadows with few obstacles. Obstacles are given a clearance which is wider than the clearance afforded by extreme routes. When clearance is not available, the algorithm plans slower speeds [5]. Sandstorm, a robot developed for desert racing, has driven extreme routes at speeds up to 22 meters per second, but makes an assumption that it is traveling on slowly varying roads. If an obstacle is en- countered in the center of a road, the path cannot change rapidly enough to prevent collision [4]. Our work is related to several previous research themes. The first con- nection is to the research in autonomous mobile robots for exploration in planetary environments [10][11] that uses traversability analysis to find ob- stacles that a vehicle could encounter. The second connection is to a method of scanning the environment by sweeping a single-axis laser scanner [2] that allows detection of obstacles even when the vehicle is translating and pitch- ing. A third connection is to a method of collision avoidance that is based on models of human navigation in between discrete obstacles [7]. 3Approach Herew ed iscusst he tw om ain parts of our approac h–o bstacle detection and collision avoidance. 3.1ObstacleDetection Fo rh igh sp eed na vigation, the sensors requiredd ep end on the ve hicle’s sp eed, stopping distance and minimum obstacle size. At higher speeds, where stop- ping distances are greater, the obstacles must be detected at agreater dis- tance. In order to detect smaller obstacles, the measurement densit yo ft he sensor must be correspondingly greater. Our goalistoenable the vehicle to travelatspeeds of up to 5m/s while detectingobstacles as small as 20cm × 20cm. In other work with lowerspeed vehicles moving at 2m/s [2],wefind that asingle sweepinglaser is sufficientfor detectingobstacles. The sweeping laser system consists of asingle Sicklaser turned so it is scanning avertical plane. Amotor mechanicallysweeps the vertical plane backand forth, thus building a3 -D map of the terrain in fron to ft he ve hicle. Ho we ve r, for the higher speed obstacle detection in this application, we find that the sweeping laser alone cannotprovide asufficientdensityoflaser measurementstodetect 494 S. Roth et al. small obstacles at higher speeds. Accordingly, a second fixed laser is deemed necessary (Figure 2). Fig.2. Configurationoflasers scannersonthe vehicle. The fixedlaserconcentrates its scans 10m in frontofthe vehicle, giving an early detection system. The sweeping laser concentrates itsdata closertothe vehicle, giving the ability to trackobstacles that are closer to the vehicle. The additiono fas econd fixed laser pro vides sev eral adv an tages ove rt he single sweeping laser. Primarily,the fixed laser is pointed 10minfrontofthe vehicle and increases the densityoflaser dataatpoints far from thefront of thev ehicl e. No ws maller obstacles are detected at ad istance sufficien tf or safe avoidance.The sweeping laser system concentrates its data closer to the vehicle, so obstacles nearer the vehicle are tracked. Asecond advantage of the two laser system is that they collect orthogonal sets of data.T he sw eeping laser is best suited for detecting pitchtypeobstacles, while the fixed laser is best suited for detecting roll type obstacles. The twolaser systems complement eac ho ther by pe rforming be st for these tw od ifferent ty pe so fo bstacles. The addition of asecond laser by itself is not enough to guarantee detecting obstaclesinall cases. When following curved paths, we find it is not enough to simply sw eep the laser in afi xed range.I ti sn ecessary to bias the sw eeping laser so it points into turns. Figure4shows arepresentation of the number of laser hits that would be received by a15cm × 15cm obstaclelocateda distance greater than the ve hicle’s stopping distance from the fron to ft he vehicle. Areasofred indicate ahigh number ( > 60) of hits,and areasofblue indicate alowernumber(10-20).The first picture shows the number of hits when the laser is sw ept be tw een afi xed 20 degree range cen tered ab out the front of thevehicle. It is clear from the figure that thereissufficientlaser datatodetect ob- stacles along the straightsection. However, along the turn the number of hits decreases dramatically .T he lo we rd ensit yo fl aser datai ncreases the ch ances thatanobstacle will not be detected while the vehicle is turning. Figure 4(b) Results in Combined Route Traversal and Collision Avoidance 495 shows the number of hits when the sweeping laser is biased to point into the turn. Compared to the unbiased case, the number of laser hits on the obstacle greatly increases in the area where the vehicle is turning. With data from two lasers, we use two obstacle detection algorithms: a traversability analysis and a line scan gradient analysis. In the traversability analysis, data from both lasers is used to produce a point cloud of the terrain in front of the vehicle. Vehicle-sized planar patches are fit to the point cloud data, and the fitted data gives three measures useful in identifying obstacles: plane orientation (roll, pitch), roughness (the residual of the fit) and the height of data points above the plane. These measured values are used to classify areas as untraversable or clear. While the traversability analysis is a simple way of detecting obstacles, it can produce false positives due to inaccurate calibration of the two lasers and/or incorrect synchronization with positioning. To supplement the traversability analysis, the slope of segments of individual line scans from the sweeping laser is also calculated as in [2]. If the slope of a scan segment is above a given threshold, it is tagged as a gradient obstacle. Because the gradient analysis uses piecewise segments of an individual line scan, it is not susceptible to misregistration as the traversability analysis can be. Fig. 3. Overhead view of laser data from from the twoscanners.Data overawindow of time are registeredtoacommon reference frame and obstacles are found by analyzing traversabilityand gradient of the individual line scans. To classify an object as atrue obstacle, both the gradientand traversabil- ityanalyses must agree. The combination of the twoobstacle detection algo- rithms compe nsates for the we aknesses of thet wo individuala lgorithms and dramatically reduces the falseobstacle detection rate. Becausethe gradient analysis looks at onlyanindividual line scan from the sweeping laser, it cannot takeadvantage of integrating multiple scans overtime likethe traversability analysis can. Ho we ve r, by onlyu sing single line scans, the gradien ta nalysis is relatively immune to mis-registration problems that plague the traversability analysis. [...]... Japan, Vol.22, No.5, pp.55 4-5 57 4 Uluc Saranli et al., RHex: A Simple and Highly Mobile Hexapod Robot, IJRR, Vol 20, No.7, 2001, pp.61 6-6 31 5 Jorge G Cham et al., Fast and Robust: Hexapedal Robots via Shape Deposition Manufacturing, IJRR, Vol.21, No.1 0-1 1, 2002, pp.86 9-8 82 6 Kazuhito Yokoi et al., Experimental Study of Humanoid Robot HRP-1S, IJRR, Vol.23, No. 4-5 , 2004, pp.35 1-3 62 7 Tetsuo Tawara et... P Corke and S Sukkarieh (Eds.): Field and Service Robotics, STAR 25, pp 517–528, 2006 © Springer-Verlag Berlin Heidelberg 2006 518 S Hutangkabodee et al The researches on off-road vehicle and its characteristics have been carried out since Bekker first pioneered in this field [2, 3] The empirical relationships of vehicle terrain interactions for both tracked and wheeled vehicles have been modelled and. .. Kenichi Tokuda3 , and Satoshi Tadokoro4 1 2 3 4 Graduate School of Information Sciences, Tohoku Univ 6-6 -0 1 Aramaki Aza Aoba, Aoba-ku, Sendai Japan okamoto@rm.is.tohoku.ac.jp Graduate school of Science and Technology, Kobe Univ 1-1 Rokkodai, Nada, Kobe 65 7-8 501 Japan k konishi@r.cs.kobe-u.ac.jp Dept Opto-Mechatoronics, Wakayama Univ 930 Sakaedani, Wakayama-city 64 0-8 510 Japan tokuda@rescue-robot.org Graduate... actuator per a leg and compliant elements to its hips or legs and cancel the unevenness of the ground and careful control One of the most difficult P Corke and S Sukkarieh (Eds.): Field and Service Robotics, STAR 25, pp 505–516, 2006 © Springer-Verlag Berlin Heidelberg 2006 506 S Okamoto et al conditions for quadruped vehicles on rough terrain is obstacles laid on the ground or unsafe and breakable footholds... Batavia and S Singh Obstacle detection using adaptive color segmentation and color homography In Proceedings of the International Conference on Robotics and Automation IEEE, May 2001 504 S Roth et al 2 P H Batavia and S Singh Obstacle detection in smooth, high-curvature terrain In Proceedings of the International Conference on Robotics and Automation, Washington, D.C., 2002 3 T Jochem C Thorpe and D... al., Morph: A Desktop-Class Humanoid Capable of Acrobatic Behavior, IJFF, Vol.23, No.1 0-1 1, 2004, pp.109 7-1 103 8 Masaru Ogata and Shigeo Hirose, Study on Ankle Mechanism for Walking Robots, Proc ROBOMEC2004, p.3 3, June, 2004 9 Tatsuyoshi Kano and Masahiko Yamamoto et al., Development of a Quadruped Walking Robot with Parallel Link, Proc ROBOMEC2004, p.7 1, June, 2004 10 Kenichi Tokuda and Takafumi Toda... vehicle In IEEE Transactions on Robotics and Automation, 1999 10 et al S Singh Recent progress in local and global traversability for planetary rovers In Proceedings of the IEEE International Conference on Robotics and Automation, April 2000 11 C Urmson and M.B Dias Vision based navigation for sun-synchronous exploration In Proceedings of the International Conference on Robotics and Automation, May 2002 Adaptation... AIM2003, July 2003, pp.44 7-4 53 Multi-solution Problem for Track-Terrain Interaction Dynamics and Lumped Soil Parameter Identification S Hutangkabodee, Y.H Zweiri, L.D Seneviratne, and K Althoefer King’s College London, Strand, London WC2R 2LS, UK {suksun.hutangkabodee, yahya.zweiri, lakmal.seneviratne, k.althoefer}@kcl.ac.uk Summary A technique for identifying lumped soil parameters on-line while traversing... autonomous guided vehicle for cargo handling applications International Journal of Robotics Research, 15, 1996 7 B Fajen and W Warren Behavioral dynamics of steering, obstacle avoidance, and route selection Journal of Experimental Psychology: Human Perception and Performance, 29(2), 2003 8 D Langer and T Jochem Fusing radar and vision for detecting, classifying, and avoiding roadway obstacles In Proceedings... to a slight slope series graph of r (distance between COF and the center of the sole) and θ (degree of the angle) helps to understand the algorithm and foot’s reactions to estimated COF An experimental example is shown in Fig .13 Fig 13 time series graph of r, θ, the experimental result of active adaptation to a slope 514 S Okamoto et al In Fig .13, at the stroke of ground contact(t=0), only the joint . great distances without relying on structures uc ha sb eacons and lane markings. In addition to avoiding obstacles, it is important thatthe vehicle stayonthe designated route as much as possible. P. Corke and S. Sukkarieh (Eds.): Field and Service. with lowerspeed vehicles moving at 2m /s [2],wefind that asingle sweepinglaser is sufficientfor detectingobstacles. The sweeping laser system consists of asingle Sicklaser turned so it is scanning. developmentofthe rescue robots. Quadrupedvehicles have higher capabilityofclimbingover steps andobstacles than other proposed rescue robots, such as crawler-type [1 ][2]orsnake-shaped[3] robots.