機(jī)器人在自然地形下爬行【中文6940字】【PDF+中文WORD】,中文6940字,PDF+中文WORD,機(jī)器人,自然,地形,爬行,中文,6940,PDF,WORD Climbing Robots in Natural TerrainTimothy Bretl,Teresa Miller,and Stephen RockJean-Claude LatombeAerospace Robotics LabRobotics LaboratoryDepartment of Aeronautics and AstronauticsComputer Science DepartmentStanford University,Stanford,CA 94305Stanford University,Stanford,CA 94305tbretl,tgmiller,rocksun-valley.stanford.edulatombecs.stanford.eduKeywordsMotion planning,climbing,robotics,legged robots,high-risk access,natural terrain.AbstractThis paper presents a general framework for plan-ning the quasi-static motion of climbing robots.Theframework is instantiated to compute climbing motionsof a three-limbed robot in vertical natural terrain.Anexample resulting path through a large simulatedenvironment is presented.The planning problem is oneof five fundamental challenges to the development ofreal robotic systems able to climb real natural terrain.Each of the four other areashardware design,control,sensing,and graspingis also discussed.1 IntroductionThe work described in this paper is part of an effortto develop critical technologies that will enable thedesign and implementation of an autonomous robotable to climb vertical natural terrain.To our knowl-edge,this capability has not been demonstratedpreviously for robotic systems.Prior approaches havedealt with artificial terrain,either using special“grasps”(e.g.,pegs,magnets)adapted to the terrainssurface or exploiting specific properties or features ofthe terrain(e.g.,ducts and pipes)1-12.Developing this capability will further our under-standing of how humans perform such complex tasksas climbing and scrambling in rugged terrain.Thismay prove useful in the future development ofsophisticated robotic systems that will either aid orreplace humans in the performance of aggressive tasksin difficult terrain.Examples include robotic systemsfor such military and civilian uses as search-and-rescue,reconnaissance,and planetary exploration.Many issues need to be addressed before real robotscan climb real,vertical,natural terrain.This paperconsiders five of the most fundamental of these issues:hardware design,control,sensing,planning,andgrasping.One of these issues in particular,the motion-planning problem,is described in more detail.Ageneral framework for climbing robots is presentedand this framework is instantiated to compute climbingmotions of the three-limbed robot shown in Figure 1.Simulation results are shown for the robot in anexample vertical environment.2 MotivationThe results of research in this area will benefit anumber of applications and have implications forseveral related research areas.2.1ApplicationsThis paper is motivated by a need for robotic sys-tems capable of providing remote access to high-risknatural environments.There are many terrestrial applications for thesesystems,such as search-and-rescue,cave exploration,human assistance for rock and mountain climbing,andtactical urban missions.Each of these applicationsrequires climbing,descending,or traversing steepslopes and broken terrain,and thus involves consider-able human risk.Several space applications could also benefit fromthese aggressive robotic systems.For example,sites onMars with potentially high science value have beenidentified on cliff faces 13.Often,it is neitherpractical nor feasible for flying robots to access theseFig 1.A three-limbed climbing robot moving vertically on naturalsurfaces.locations.Therefore,to reach these sites,robots mustclimb,descend,or traverse steep slopes.Future goalsfor exploration on other planetary bodies may requireaccess to equally rugged terrain.2.2ImplicationsIn addition to furthering the development of aclimbing robot for vertical natural terrain,the results ofresearch in this area could provide fundamental insightinto several related research areas.For example,thisstudy could lead to the development of better strategiesfor robotic walking or dexterous manipulation.Humanclimbers often comment on an increase in balance andan expanded range of movement in everyday activityas they become more proficient at the sport.Thisenhanced mobility is often referred to as“discoveringnew degrees of freedom,”and is related to the idea ofdiscovering useful new modes of mobility for ex-tremely complicated humanoid robots or digital actors.Also,the development of planning algorithms forclimbing robots could lead to a better set of criteria forthe design of these types of robots.These algorithmscould be applied to candidate designs in simulation todetermine the capabilities of the resulting robots,andthus to select a design.3 Fundamental IssuesThere are five fundamental issues involved inclimbing steep natural terrain:hardware design,control,sensing,grasping,and planning.A substantialamount of work needs to be done in each of these areasin order to develop a real climbing robot.This sectiondescribes the challenges involved in the first four ofthese areas;the planning problem will be discussed inmore detail in Section 4.3.1Hardware DesignA good hardware design can increase the perform-ance of the robot,and often can make each of the otherfundamental issues easier to deal with.However,pastuse of hardware solutions in maintaining equilibriumgenerally resulted in a fundamental limitation on theterrain that could be traversed.Wheeled robotic systems have been used to ascendand traverse natural slopes of up to 50 degrees,todescend slopes of up to 75 degrees,and to climb oversmall obstacles in rough terrain.These systems eitheruse some form of active or rocker-bogie suspension asin 12,14-16,or use rappelling as in 1.Similarresults have been obtained using legged rappellingrobots 3,17 and a snake-like robot 4.The terrain that these rovers can traverse robustly isimpressive,but none of the existing systems has beenshown to be capable of climbing natural slopes of 90degrees or higher.Wheeled rovers and snake-likerobots have an inherent grasping limitation thatprevents their use in ascending sustained near-verticalor descending sustained past-vertical natural slopes.Existing legged robotic systems do not have thislimitation,but still have bypassed the issue of main-taining contact with the slope by using rappel tethers.Reliance on these tethers prohibits initial cliff ascent,and limits the slope grade on cliff descent to below 90degrees.A wide variety of robots capable of climbing verticalartificial surfaces is available.Most of these robotsexploit some property of the surface for easy grasping.For example,some of these robots use suction cups orpermanent magnets to avoid slipping 5-8.Others takeadvantage of features such as balcony handrails 9 orpoles 10.However,the surface properties that areexploited by these robots generally are not available innatural terrain.In contrast,the simpler hardware designs used by 2,11 had no such limitations.It is expected thatsolutions to the planning problem such as the onepresented in this paper will allow basic natural verticalterrain to be climbed by similar systems,in addition tothe ducts and pipes climbed by existing systems,andwill suggest design modifications for better perform-ance.Future studies could address the use of other types oftools for grasping vertical natural surfaces,such astools for drilling bolts or placing other types of gear inrock.The use of these tools would allow morechallenging climbs to be accomplished,in the sameway that“aid”helps human climbers 18,19.However,these tools bring an increase in weight andcomplexity,slowing movement and limiting potentialapplications.3.2ControlThere are three primary components of the controlproblem for a climbing robot:maintenance of equilib-rium,endpoint slip control,and endpoint force control.These three components are tightly related.In order tomaintain balance,both the location of the center ofmass of the robot and the forces from contacts withnatural features must be controlled.Control of slip atthese contacts is directly related to the direction andmagnitude of the contact forces.Existing control techniques such as those based onthe operational space formulation 20 could form abaseline approach to the design of a control architec-ture for a climbing robot.However,these techniquescould be extended in a number of different ways toachieve better performance.For example,futureresearch might address the design of an endpoint slipcontroller that is stable with respect to the curvature ofa contact surface,rather than with respect to a pointcontact only.3.3SensingFor control and grasping,the robot must be capableof sensing the orientation of its body with respect tothe gravity vector,the location of its center of mass,the relative location of contact surfaces from its limbendpoints,and the forces that it is exerting at contactswith natural features.For planning,the robot mustadditionally be able to locate new holds and generate adescription of their properties,possibly requiring ameasurement of levels of slip at contact points.Sensorintegration,in order to acquire and use this informationwith algorithms for control,grasping,and planning,isa challenging problem.Existing engineering solutions are available whichcan lead to the development of a baseline approach ineach case.For example,sensors such as those de-scribed in 21,22 can provide basic endpoint forceand slip measurements,an inertial unit and magneticcompass can provide position information,an on-boardvision system can provide a rough characterization ofhold locations and properties,and encoders canprovide the location of the center of mass.However,the improvement of each of these sensorsin terms ofperformance,mass reduction,or cost reduc-tionpresents an open area for research.Although the performance of the planning frame-work that will be presented in Section 4 would beimproved with better sensor information,it does notdepend on a perfect model of the environment a priori.Since the framework leads to fast,online implementa-tion,plans can be updated to incorporate new sensorinformation as it becomes available.3.4GraspingThe performance of a climbing robot is dependenton its ability to grasp“holds,”or features on a steepnatural surface.It has already been noted that special-ized grasping schemes,relying on specific propertiesof the surface such as very smooth textures,pegs,orhandles,cannot be used for grasping arbitrary naturalfeatures.The problems involved in grasping naturalholds will be examined further in this section.Traditionally grasp research has been interested ineither picking up an object or holding it immobile(alsocalled“fixturing.”)Research in this subject dates as farback as 1876 it was shown that a planar object couldbe immobilized using a minimum of four frictionlesspoint constraints 23.Good overviews of more recentwork can be found in 24,25.In this field an impor-tant concept is“force-closure,”defined as a grasp that“can resist all object motions provided that the end-effector can apply sufficiently large forces at theunilateral contacts.”25 Nearly all research on graspshas focused on selecting,characterizing,and optimiz-ing grasps that have the property of force-closure.However,for the task of climbing a grasp need notachieve force-closure to be a useful grasp.Forexample,a robot may find a shelf-like hold veryeffective for pulling itself up,even though this graspwould be completely unable to resist forces exerted inother directions.For this reason,the techniques forselecting,characterizing,and optimizing grasps mustbe expanded significantly to apply to climbing robots.Characterization involves examining the directionand magnitudes of forces and torques(also calledwrenches)that can be exerted by the grasp.Forexample,for one-finger grasps on point holds,anadequate representation of this information is a frictioncone,which will be used for the planning algorithmdescribed in Section 4.The idea of characterization also encompasses a“quality factor.”Measures of grasp quality have beenresearched extensively and are well reviewed in 26.This work lists eight dexterity measures that includeminimization of joint angle deviations and maximiza-tion of the smallest singular value of the grasp matrix.Other relevant research has been done using theconcept of the wrench space.Using this concept,quality is defined as the largest wrench space ball thatcan fit within the unit grasp wrench space 27.Thevolume of the grasp wrench space,or of morespecialized task ellipsoids,could be used as a qualitymeasure 28.These ideas have been expanded toinclude limiting maximum contact force and applied ina grasp simulator to compute optimal grasps withvarious hands in 3D 29,30.However,the concept of grasp quality is ill definedfor grasps that do not provide force-closure.Depend-ing on the direction that a climber wishes to go,different grasps may be of higher quality.Furthermore,grasp quality generally includes a concept of securityor stability,and this too is ill defined for non-force-(a)(b)(c)(d)Fig.2.Four different human climbing grasps,the(a)open grip,(b)crimp,(c)finger-lock,and(d)hand jam.closure grasps.Again,depending on the direction ofapplied forces,the security of a grasp may change.Theconcept of hold quality must be defined before usefuloptimization is possible.Also,an efficient way oftransmitting this information to a controller or planneris necessary to accomplish the climbing task.A qualitative classification of different types ofgrasps already exists in the literature for humanclimbers 19,31.In this classification,grasps are firstbroken into two categories,those meant for pockets,edges,and other imperfections on otherwise unbrokenvertical rock faces,and those meant for sustainedvertical cracks.Several examples of different face andcrack grasps are shown in Figure 2.The literaturegives a rough idea of the quality and use of each typeof grasp in terms of criteria such as a perceived levelof security,the amount of torque that can be exerted ona hold,and the amount of friction at the“power point.”Not only is this expert intuition qualitative,but alsoit is clear that human climbers need to performadditional grasp planning for specific cases.As put byLong,“There are as many different kinds of holds asthere are ways to grab them 31.”However,thisintuition can be used as a starting point for determiningmeaningful quantitative criteria for grasp selection andoptimization.A comparison of the climbing literature with pastwork on robotic grasp planning reveals several otherfundamental differences between the two applicationsthat may become important in future research.Forexample,many climbing holds are very small,so thefingers used in a climbing grasp often have largediameters relative to the object to be grasped.Litera-ture on robotic grasping primarily considers the casewhere the fingers have small diameters relative to theobject.In addition,some climbing grasps,as men-tioned above and shown in Figure 2,are based onjamming fingers in a crack.This technique is verydifferent from one a robot might use to pick up anobject,and requires a high degree of flexibility andsmall degrees-of-freedom in order to“un-jam”thefingers.Clearly,continued work on climbing robotseventually will lead to the consideration of a wealth ofnew issues in grasping.4 PlanningThe planning problem is the fifth fundamentalchallenge for climbing robots in natural terrain.Detailsof the motion-planning framework presented in thissection are given in 32.4.1ChallengesThe planning problem for a climbing robot consistsof generating a trajectory that moves the robot througha vertical environment while maintaining equilibrium.This problem is challenging even for human climb-ers!Climbing is described by Long as a“singular(a)(b)(c)Fig.3.Three different human climbing“moves,”the(a)back-step,(b)stem,and(c)high-step.challenge,where each route up the rock is a mentaland physical problem-solving design whose sequenceand solution are unique.Every climb is different 31.”Much of the sequence for a particular route might becomposed of one of a variety of different types of“moves,”such as a back-step,stem,mantel,high-step,counterbalance,counterforce,lie-back,down-pressure,or under-cling.Some of these moves are shown inFigure 3.Each“move”is a learned technique formaintaining balance that may seem counterintuitive.Inaddition to these heuristics,movement through a largenumber of other very specific body positions might benecessary to progress towards the top of a climb.The importance of planning a sequence of movesbefore actually climbing is emphasized by Graydonand Hanson 19,who recommend that climbers“identify and examine difficult sections before theyget to them,make a plan,and then move through themquickly.”The human motivation for this approach isprimarily to minimize the effort required for eachmove and to conserve energy,since most people havehard strength and endurance limits.The planning problem for a climbing robot is quitesimilar.The robot likely will be equipped withactuators that can exert high torques only for shortamounts of time,so planning a sequence of movesbefore climbing is important for a robotic system aswell.Likewise,a climbing robot will be subject to thesame hard equilibrium constraints,and will need toselect between a similarly wide range of possiblemotions.Therefore,the development of a planningalgorithm for an autonomous climbing robot is a verychallenging problem.4.2Related WorkThe search space for a climbing robot is a hybridspace,involving both continuous and discrete actions.Many different methods are available for motionplanning through continuous spaces,including celldecomposition,potential field,and roadmap algo-rithms 33.Discrete actions can be included in thesemethods directly,for example at the level of nodeexpansion in roadmap algorithms,but this approachgenerally leads to a slow implementation that isspecific to a particular system.Previous work on motion planning for legged robotshas developed tools for addressing these hybrid searchspaces for some systems.This work can be categorizedby whether or not the planning is done offline,in orderto generate a reactive gait,or online,in order to allownon-gaited motion specific to a sensed environment.Gaited planners generate a predefined walkingpattern offline,assuming a fairly regular environment.This pattern is used with a set of heuristics or behav-iors to control the robot online based on current sensorinput.Gaited planning was used by 2,11,forexample,to design patterns for climbing pipes andducts.Other methods such as 34 are based on thenotion of support triangles for maintaining equilib-rium.Stability criteria such as the zero-moment-pointhave been used to design optimal walking gaits 35.Dynamic gaiting and bounding also have beendemonstrated 36-38.Recent work 39,40 hasattempted to provide unifying mathematical tools forgait generation.Each of these planning algorithmswould be very effective in portions of a naturalclimbing environment with a sustained feature such asa long vertical crack of nearly uniform width.How-ever,something more is needed for irregular environ-ments such as the one studied in this paper,where thesurfaces on which the robot climbs are angled andplaced arbitrarily.Non-gaited planners use sensed information aboutthe environment to create feasible motion plans online.Most previous work on non-gaited motion planning forlegged robots has focused on a particular systemmodel,the spider robot.The limbs of a spider robot areassumed to be massless,which leads to elegantrepresentations of their free space for quasi-staticmotion based on support triangles 41-43.Thesemethods have been extended to planning dynamicmotions over rough terrain 44,45.The analysis usedin these methods breaks down,however,whenconsidering robots that do not satisfy the spider-robotassumption.For example,additional techniques werenecessary in 46,47 to plan non-gaited walkingmotions for humanoids,which clearly do not satisfythis assumption.To address the high number ofdegrees of freedom and the high branching factor ofthe discrete search through