喜歡這套資料就充值下載吧。。。資源目錄里展示的都可在線預(yù)覽哦。。。下載后都有，，請(qǐng)放心下載，，文件全都包含在內(nèi)，圖紙為CAD格式可編輯，【有疑問咨詢QQ:414951605 或 1304139763】 IEEE TRANSACTIONS ON ROBOTICS, VOL. 24, NO. 5, OCTOBER 20081199Real-Time Adaptive Motion Planning (RAMP)of Mobile Manipulators in Dynamic EnvironmentsWith Unforeseen ChangesJohn Vannoy and Jing Xiao, Senior Member, IEEEAbstractThis paper introduces a novel and general real-timeadaptive motion planning (RAMP) approach suitable for plan-ning trajectories of high-DOF or redundant robots, such as mobilemanipulators, in dynamic environments with moving obstacles ofunknown trajectories. The RAMP approach enables simultaneouspath and trajectory planning and simultaneous planning and exe-cution of motion in real time. It facilitates real-time optimizationof trajectories under various optimization criteria, such as min-imizing energy and time and maximizing manipulability. It alsoaccommodates partially specified task goals of robots easily. Theapproach exploits redundancy in redundant robots (such as lo-comotion versus manipulation in a mobile manipulator) throughloose coupling of robot configuration variables to best achieve ob-stacleavoidanceandoptimizationobjectives.TheRAMPapproachhasbeenimplementedandtestedinsimulationoveradiversesetoftask environments, including environments with multiple mobilemanipulators. The results (and also the accompanying video) showthat the RAMP planner, with its high efficiency and flexibility, notonly handles a single mobile manipulator well in dynamic environ-ments with various obstacles of unknown motions in addition tostatic obstacles, but can also readily and effectively plan motionsfor each mobile manipulator in an environment shared by multiplemobile manipulators and other moving obstacles.Index TermsAdaptive, dynamic obstacles of unknown motion,loose coupling, mobile manipulators, partially specified goal, realtime, redundant robots, trajectory optimization.I. INTRODUCTIONMOTION PLANNING is a fundamental problem inrobotics1,2concernedwithdevisingadesirablemo-tion for a robot to reach a goal. Motion planning for high-DOFarticulated manipulators or mobile manipulators is more chal-lenging than for mobile robots because the high-dimensionalconfiguration space of a robot has little or no resemblance tothe physical space that the robot works in, and how to constructManuscriptreceivedMay16,2007;revisedDecember13,2007andMarch5,2008. First published October 10, 2008; current version published October 31,2008. This paper was recommended for publication by Associate EditorK. Yamane and Editor L. Parker upon evaluation of the reviewers comments.A preliminary part of this paper was presented at the IEEE International Con-ference on Intelligent Robots and Systems, Sendai, Japan, 2004.The authors are with the Intelligent, Multimedia and Interactive Systems(IMI) Laboratory, Department of Computer Science, University of NorthCarolina at Charlotte, Charlotte, NC 28223 USA (e-mail: ; xiaouncc.edu).Thispaperhassupplementarydownloadablematerialavailableathttp:/ieeexplore.ieee.org, provided by the authors: a video showing the real-time planning and execution of mobile manipulator motion by our RAMPalgorithm. This video is 14 MB in size.Color versions of one or more of the figures in this paper are available onlineat http:/ieeexplore.ieee.org.Digital Object Identifier 10.1109/TRO.2008.2003277a configuration space higher than three dimensions efficientlyremains a largely unsolved problem.A. Related Research on Motion PlanningRandomized algorithms, such as the popular probabilisticroadmap (PRM) method 3 and rapidly exploring random tree(RRT) method 4, are found to be very effective in findinga collision-free path for a robot with high DOFs offline be-cause such algorithms avoid building the robots configurationspace explicitly by sampling the configuration space. The PRMmethod has inspired considerable work on improving samplingand roadmap construction 2, including a recent paper 5 onproducing compact roadmaps to better capture the different ho-motopic path groups. By building a tree rather than a graph, theRRTmethodismoresuitableforgeneratingapathinoneshotorgeneratingatrajectorydirectlyandthusmoresuitableforonlineoperation 6. Both methods have seen many variants 2.There are also methods for path planning based on ge-netic algorithms (GAs), or more broadly, evolutionary com-putation 7, 8, which are general frameworks of randomizedsearch subject to user-defined optimization criteria. Such op-timization techniques have been used widely and successfullyin many application domains 8, 9 to tackle NP-hard opti-mization problems. There are two major ways of applications.One straightforward way is to map a problem into the formsuitable for a standard, off-the-shelf GA, solve it by runningthe GA, and then, map the results back to the application do-main. This one-size-fit-all approach is often not effective be-cause it forces artificial transformation of a problem into some-thing else that is confined in the format of a standard GA butmaylosecertainimportantnatureoftheoriginalproblem.SomeGA-based path planning methods 10, 11 adopt such an ap-proach, where C-space is discretized into a grid, and a path is interms of a fixed-length sequence of grid points. As the standardGA operates on fixed-length bit strings, search is often veryslow.A more effective approach is to adopt the general idea ofevolutionary computation to solve a problem in a more naturalandsuitablerepresentation.Thepathplanningmethodsreportedin 1214 belong to such a customized approach. A real-timepathplanningmethodisreportedin12for2DOFpointmobilerobots, which is extended in 13 for 3 DOF point flying robotswith specific constraints. A multiresolution path representationis proposed in 14 for path planning. However, all evolution-ary algorithms have a number of parameters that must be setappropriately, which is often not a trivial task.1552-3098/$25.00 2008 IEEE1200IEEE TRANSACTIONS ON ROBOTICS, VOL. 24, NO. 5, OCTOBER 2008Unlike path planning, motion planning has to produce anexecutable trajectory for a robot in configurationtime space,or CT-space, and not merely a geometrical path. A commonapproach is to conduct trajectory planning on the basis of a pathgenerated by a path planner. A notable framework is the elasticstrip method 15, which can deform a trajectory for a robotlocallytoavoidmovingobstaclesinsideacollision-free“tunnel”that connects the initial and goal locations of the robot in a 3-Dworkspace. Such a “tunnel” is generated from a decomposition-based path planning strategy 16. The other approach is toconduct path and trajectory planning simultaneously. However,most effort in this category is focused on offline algorithmsassumingthattheenvironmentiscompletelyknownbeforehand,i.e., static objects are known, and moving objects are knownwithknowntrajectories1720.Asfordealingwithunknownmoving obstacles, only recently some methods were introducedfor mobile robots 21, 22.The combination of mobility and manipulation capabilitymakes a mobile manipulator applicable to a much wider rangeof tasks than a fixed-base manipulator or a mobile robot. For amobile manipulator, a task goal state is often partially specifiedas either a configuration of the end-effector, which we call aplace-to-place task, or a desired path (or trajectory) of the end-effector, which we call a contour-following task, and the targetlocation/path of the base is often unspecified.Here, a major issue of motion planning is the coordination ofthe mobile base and the manipulator. This issue, as it involvesredundancy resolution, presents both challenges and opportu-nities. There exists a rich literature addressing this issue frommany aspects. Some researchers treat the manipulator and themobile base together as a redundant robot in planning its pathfor place-to-place tasks 2325. Some focused on planning asequence of “commutation configurations” for the mobile basewhen the robot was to perform a sequence of tasks 26, 27subject to various constraints and optimization criteria. Othersfocused on coordinating the control of the mobile base and themanipulator in a contour-following task 28, 29 by tryingto position the mobile base to maximize manipulability. Manyconsidered nonholonomic constraints.While most of the existing work assumes known environ-ments with known obstacles for a mobile manipulator, a fewresearchers considered local collision avoidance of unknown,moving obstacles online. One method 30 used RRT as a localplanner to update a roadmap originally generated by PRM todeal with moving obstacles. For contour-following tasks, an ef-ficient method 31 allows the base to adjust its path to avoid amoving obstacle if possible while keeping the end-effector fol-lowing a contour, such as a straight line. Another method 29allowed the base to pause in order to let an unexpected obsta-cle pass while the arm continued its contour-following motionunder an event-based control scheme. Other methods includeone based on potential field 32 to avoid unknown obstaclesand one based on a neuro-fuzzy controller 33 to modify thebase motion locally to avoid a moving obstacle stably. Thereis also an online planner for the special purpose of planningthe motions of two robot arms getting parts from a conveyerbelt 34.However, we are not aware of any existing work that can planmotions of high-DOF robots globally among many unknowndynamic obstacles.B. Our Problem and ApproachPlanning high-DOF robot motion in such an environmentof many unknown dynamic obstacles poses special challenges.First, planning has to be done in real time, cannot be done of-fline, and cannot be based on a certain prebuilt map because theenvironment is constantly changing in unforeseen ways, i.e.,the configuration space obstacles are unknown and changing.Examples of such environments include a large public squarefull of people moving in different ways, a warehouse full ofbusy-moving robots and human workers, and so on. Such anenvironment is very different from static or largely static envi-ronments or known dynamic environments (i.e., with other ob-ject trajectories known), where motion planning can reasonablyrely on exploring C-space (for known static environments) orCT-space(forknowndynamicenvironments)offline(suchasbyPRM). The elastic strip method provides the flexibility to makesmall adjustments of a robot motion to avoid unknown motionsof obstacles, if the underlying topology of the C-space does notchange. For an environment with changing C-space topologyin unknown ways, a planned path/trajectory can be invalidatedcompletelyatanytime,andthus,real-timeadaptiveglobalplan-ning capability is required for making drastic changes of robotmotion. Planning and execution of motion should be simulta-neous and based on sensing so that planning has to be very fastand always able to adapt to changes of the environment.By nature, to tackle motion planning in an unknown dynamicenvironment cannot result in a complete planning algorithm.That is, no algorithm can guarantee success in such an unknownenvironment. We can only strive for a rational algorithm thatserves as the “best driver of a high-DOF robot, but even thebest driver cannot guarantee to be accident-free if other thingsin the environment are not under his/her control.This paper addresses the problem of real-time simultaneouspath and trajectory planning of high-DOF robots, such asmobile manipulators, performing general place-to-place tasksin a dynamic environment with obstacle motions unknown.The obstacle motions can obstruct either the base or the armor both of a mobile manipulator. We introduce a unique andgeneral real-time adaptive motion planning (RAMP) approach.Our RAMP approach is built upon both the idea of randomizedplanning and that of the anytime, parallel, and optimizedplanning of evolutionary computation, while avoiding thedrawbacks. The result is a unique and original approacheffective for the concerned problem.The RAMP approach has the following characteristics.1) Whole trajectories are represented at once in CT-spaceand constantly improved during simultaneous plan-ning and execution, unlike algorithms that build apath/trajectory sequentially (or incrementally) so that awholepath/trajectorycanbecomeavailableonlyattheendof the planning process. Our anytime planner can providea valid trajectory quickly and continue to produce betterVANNOY AND XIAO: REAL-TIME ADAPTIVE MOTION PLANNING (RAMP) OF MOBILE MANIPULATORS IN DYNAMIC ENVIRONMENTS1201trajectories at any later time to suit the need of real-timeglobal planning.2) Different optimization criteria (such as minimizing en-ergy and time and optimizing manipulability) can beaccommodated flexibly and easily in a seamless fash-ion. Optimization is done directly in the original, con-tinuous CT-space rather than being confined to a certainlimited graph or roadmap. Trajectories are planned andoptimized directly rather than conditional to the results ofpath planning.3) Our planner is intrinsically parallel with multiple diversetrajectories present all the time to allow instant, and ifnecessary, drastic adjustment of robot motion to adapt tonewly sensed changes in the environment. This is differ-ent from planners capable of only local trajectory adjust-ment based on a known set of homotopic paths. It is alsodifferent from sequential planners, such as anytime A*search 35, which also requires building a discrete statespace for searcha limitation that our planner does nothave.4) Trajectory search and evaluation (of its optimality) areconstantly adaptive to changes but built upon the resultsof previous search (i.e., knowledge accumulated) to beefficient for real-time processing.5) As planning and execution (i.e., robot motion followingthe planned result so far) are simultaneous, partially feasi-ble trajectories are allowed, and the robot may follow thefeasible part of such a trajectory (if it is the current best)and switch to a better trajectory to avoid the infeasiblepart.6) With multiple trajectories from our planner, each trajec-tory can end at a different goal location in a goal region,i.e.,partiallyspecified goals,ratherthan asinglegoalcon-figuration.7) Our planner represents a trajectory for a redundant robot,such as a mobile manipulator, as loosely coupled trajec-tories of redundant variables to take advantage of the re-dundancy in order to best achieve obstacle avoidance andvarious optimization objectives.The rest of the paper is organized as follows. Section II pro-vides an overview of our RAMP approach; Sections III and IVdescribe problem representation and initialization; Section Voutlines our optimization criteria for trajectory evaluation anddescribes the strategies for evaluation. Sections VI and VII de-scribe the strategies to alter trajectories to produce better ones.Section VIII describes how the RAMP planner can create andpreserve a diverse set of trajectories. Section IX provides im-plementation and experimentation results and discusses perfor-mance of the planner. Section X concludes the paper.II. OVERVIEW OF THERAMP APPROACHOnebasicpremiseofourapproachisthattheplanningprocessand the execution of motion are interweaving to enable simul-taneous robot motion planning and execution. This is achievedthrough our anytime planning algorithm that always maintainsa set of complete trajectories in the CT-space of the robot calleda population. The feasibility and optimality of each trajectory,called fitness, is evaluated through an evaluation function cod-ing the optimization criteria. Feasibility refers to collision-freeandsingularity-free.Bothinfeasibleandfeasibletrajectoriesareallowed in a population. Feasible trajectories are considered fit-ter than infeasible trajectories. Within each type, trajectories arecompared for optimality in fitness.Theinitialpopulationisacombinationofrandomlygeneratedand deliberately seeded trajectories. Deliberately seeded trajec-tories include ones constructed to represent distinct subpopula-tionsinordertoachievecertaindiversityinthepopulation.Iftheenvironment contains known static obstacles, trajectories basedon preplanned feasible paths with respect to the known staticobstacles can also be included. See Section IV for more details.Once the initial population is formed, it is then improved to afitterpopulationthroughiterationsofimprovements,calledgen-erations. In each generation, a trajectory is randomly selectedandalteredbyarandomlyselectedmodificationoperatoramonga number of different modification operators, and the resultingtrajectory may be used to replace a trajectory that is not thefittest to form a new generation. The fittest trajectory is alwayskept in the population and can only improve from generation togeneration. Each generation is also called a planning cycle.To improve the fitness of the initial population, a number ofinitial planning cycles may be run based on the initial sensinginformation of the environment before the robot begins execut-ing the fittest trajectory. The robot need not wait for a feasibletrajectory to emerge; if no feasible trajectory is available, therobot will begin moving along the fittest in feasible trajectorywhilecontinuing the search forafitter,and hopefully willlocatea feasible trajectory before it comes within a distance thresholdD of the first predicted collision or singularity of the executedtrajectory. This strategy makes sense because: 1) the presentlypredicted infeasible trajectory may become feasible later andvice versa; 2) as to be described later, our planner makes therobot switch to a better trajectory if one is available, and thus,before the infeasible part of the currently followed trajectory isencountered, the robot may already switch to a better trajectory;3) the strategy allows limited sensing, in which the robot maynot sense an obstacle until getting closer; and 4) it provides ameasure of safety in trajectory evaluation (see Section V).As the robot moves, planning continues to improve the popu-lation of trajectories until the next control cycle, when the robotcan switch to a fitter trajectory so that it always follows the besttrajectory. For that purpose, each trajectory is always updatedto start from the current robot configuration with the currentvelocity when a new control cycle begins. For the trajectory thatis being followed, this means that the executed portion of thetrajectory is dropped from the trajectory, while for every othertrajectory, it means that only the starting configuration and ve-locity are changedthe rest of the knot points on the trajectory(see Section III) remain intact. Note that each control cycle heredoes not necessarily have to be a servo cycle of the low-levelcontroller. Our control cycle, which is high level for controllingthe rate of adaptation, can be longer than a servo cycle to ensurethat within a control cycle, there can be more than one planningcycle. This is because adaptation is guided by planning.1202IEEE TRANSACTIONS ON ROBOTICS, VOL. 24, NO. 5, OCTOBER 2008Fig. 1.Relationship among planning, control, and sensing cycles.Changes in a dynamic environment are sensed and fed to theplanner in each sensing cycle, which lead to updated fitnessvalues of trajectories in the subsequent planning cycles, andunknown motions of moving obstacles are predicted in fitnessevaluation of robot trajectories. The presence of a diverse popu-lationofever-improvingtrajectoriesenablestherobottoquicklyadapt to changes in the environment. It does so by following thefittest trajectory under each circumstance: when the current tra-jectory that the robot follows becomes worse or can no longerbe followed due to imminent collision (i.e., the threshold D isreached), the robot may not need to stop its motion and replanfromscratch;rathertheplanneroftenmerelyneedstoswitchtherobot to a feasible or better trajectory in the population swiftlyin a seamless fashion. The chosen trajectory can be of a verydifferent homotopic group from the previous one to deal withdrastic and large changes.In the case when the robot reaches D of the current trajectorybut finds n