Presentation on theme: "Slides that go with the book"— Presentation transcript:
1Slides that go with the book Intelligent Robotics and Autonomous Agents seriesThe MIT PressMassachusetts Institute of TechnologyCambridge, Massachusetts 02142ISBN X
2Autonomous Mobile Robots 1Autonomous Mobile RobotsThe three key questions in Mobile RoboticsWhere am I ?Where am I going ?How do I get there ?To answer these questions the robot has tohave a model of the environment (given or autonomously built)perceive and analyze the environmentfind its position within the environmentplan and execute the movementThis course will deal with Locomotion and Navigation (Perception, Localization, Planning and motion generation)
3Content of the Course Introduction Locomotion Mobile Robot Kinematics 1Content of the CourseIntroductionLocomotionMobile Robot KinematicsPerceptionMobile Robot LocalizationPlanning and NavigationOther Aspects of Autonomous Mobile SystemsApplications
5Goal of today’s lecture (1/14) Introduce the basic problems of mobile roboticsthe basic questionsexamples and it’s challengesIntroduce some basic terminologyEnvironment representation and modelingIntroduce the key challenges of mobile robot navigationLocalization and map-buildingSome examples/videos showing the state-of-the-art
6From Manipulators to Mobile Robots 1From Manipulators to Mobile Robots
7General Control Scheme for Mobile Robot Systems 1General Control Scheme for Mobile Robot SystemsKnowledge,MissionData BaseCommandsLocalizationCognition"Position"Map BuildingPath PlanningGlobal MapEnvironment ModelPathLocal MapInformationPathExtractionExecutionPerceptionRaw dataActuator CommandsMotion ControlSensingActingReal WorldEnvironment
8Applications of Mobile Robots 1Applications of Mobile RobotsIndoor OutdoorStructured Environments Unstructured Environments
9Automatic Guided Vehicles 1Automatic Guided VehiclesNewest generation of Automatic Guided Vehicle of VOLVO used to transport motor blocks from on assembly station to an other. It is guided by an electrical wire installed in the floor but it is also able to leave the wire to avoid obstacles. There are over 4000 AGV only at VOLVO’s plants.
101HelpmateHELPMATE is a mobile robot used in hospitals for transportation tasks. It has various on board sensors for autonomous navigation in the corridors. The main sensor for localization is a camera looking to the ceiling. It can detect the lamps on the ceiling as reference (landmark).
111BR700 Cleaning RobotBR 700 cleaning robot developed and sold by Kärcher Inc., Germany. Its navigation system is based on a very sophisticated sonar system and a gyro.
12ROV Tiburon Underwater Robot 1ROV Tiburon Underwater RobotPicture of robot ROV Tiburon for underwater archaeology (teleoperated)- used by MBARI for deep-sea research, this UAV provides autonomous hovering capabilities for the human operator.
131The PioneerPicture of Pioneer, the teleoperated robot that is supposed to explore the Sarcophagus at Chernobyl
141The PioneerPIONEER 1 is a modular mobile robot offering various options like a gripper or an on board camera. It is equipped with a sophisticated navigation library developed at Stanford Research Institute (SRI).
151The B21 RobotB21 of Real World Interface is a sophisticated mobile robot with up to three Intel Pentium processors on board. It has all different kinds of on board sensors for high performance navigation tasks.
161The Khepera RobotKHEPERA is a small mobile robot for research and education. It sizes only about 60 mm in diameter. Additional modules with cameras, grippers and much more are available. More then 700 units have already been sold (end of 1998). K-Team.html
171Forester RobotPulstech developed the first ‘industrial like’ walking robot. It is designed moving wood out of the forest. The leg coordination is automated, but navigation is still done by the human operator on the robot.
18Robots for Tube Inspection 1Robots for Tube InspectionHÄCHER robots for sewage tube inspection and reparation. These systems are still fully teleoperated.EPFL / SEDIREP: Ventilation inspection robot
19Sojourner, First Robot on Mars 1Sojourner, First Robot on MarsThe mobile robot Sojourner was used during the Pathfinder mission to explore the mars in summer It was nearly fully teleoperated from earth. However, some on board sensors allowed for obstacle detection. nasa.gov/telerobotic s_page/telerobotics. shtm
20NOMAD, Carnegie Mellon / NASA http://img.arc.nasa.gov/Nomad/ 1NOMAD, Carnegie Mellon / NASA
22Toy Robot Aibo from Sony 1Toy Robot Aibo from SonySizelength about 25 cmSensorscolor camerastereo microphoneImage of Aibo
23General Control Scheme for Mobile Robot Systems 1General Control Scheme for Mobile Robot SystemsKnowledge,MissionData BaseCommandsLocalizationCognition"Position"Map BuildingPath PlanningGlobal MapEnvironment ModelPathLocal MapInformationPathExtractionExecutionPerceptionRaw dataActuator CommandsMotion ControlSensingActingReal WorldEnvironment
24Control Architectures / Strategies 1Control Architectures / StrategiesControl Loopdynamically changingno compact model availablemany sources of uncertaintyTwo ApproachesClassical AIcomplete modelingfunction basedhorizontal decompositionNew AI, ALsparse or no modelingbehavior basedvertical decompositionbottom up"Position"Global MapPerceptionMotion ControlCognitionReal WorldEnvironmentLocalizationPathEnvironment ModelLocal Map
25Two Approaches 1 Classical AI (model based navigation) complete modelingfunction basedhorizontal decompositionNew AI, AL (behavior based navigation)sparse or no modelingbehavior basedvertical decompositionbottom upPossible SolutionCombine Approaches
26Mixed Approach Depicted into the General Control Scheme 1Mixed Approach Depicted into the General Control SchemePerceptionMotion ControlCognitionLocalizationReal World EnvironmentPerception toActionObstacleAvoidancePositionFeedbackPathEnvironment ModelLocal Map
27Environment Representation 1Environment Representation and Modeling: The Key for Autonomous NavigationEnvironment RepresentationContinuos Metric -> x,y,qDiscrete Metric -> metric gridDiscrete Topological -> topological gridEnvironment ModelingRaw sensor data, e.g. laser range data, grayscale imageslarge volume of data, low distinctivenessmakes use of all acquired informationLow level features, e.g. line other geometric featuresmedium volume of data, average distinctivenessfilters out the useful information, still ambiguitiesHigh level features, e.g. doors, a car, the Eiffel towerlow volume of data, high distinctivenessfilters out the useful information, few/no ambiguities, not enough information
28Environment Representation and Modeling: How we do it! 1Environment Representation and Modeling: How we do it!Odometrynot applicableModified Environmentsexpensive, inflexibleFeature-based Navigationstill a challenge for artificial systemsCorridorcrossingElevator doorEntranceHow to find a treasureCourtesy K. ArrasLanding at nightEiffel Tower
29Environment Representation: The Map Categories 1Environment Representation: The Map CategoriesRecognizable LocationsTopological MapsCourtesy K. ArrasMetric Topological MapsFully Metric Maps (continuos or discrete)
301Environment Models: Continuous <-> Discrete ; Raw data <-> FeaturesContinuosposition in x,y,qDiscretemetric gridtopological gridRaw Dataas perceived by sensorA feature (or natural landmark) is an environmental structure which is static, always perceptible with the current sensor system and locally unique.Examplesgeometric elements (lines, walls, column ..)a railway stationa riverthe Eiffel Towera human beingfixed starsskyscraper
31Human Navigation: Topological with imprecise metric information 1Human Navigation: Topological with imprecise metric informationCourtesy K. Arras
32Methods for Navigation: Approaches with Limitations 1Methods for Navigation: Approaches with LimitationsIncrementally(dead reckoning)Odometric or initial sensors (gyro)not applicableModifying the environments(artificial landmarks / beacons)Inductive or optical tracks (AGV)Reflectors or bar codesexpensive, inflexibleCourtesy K. Arras
33Methods for Localization: The Quantitative Metric Approach 1Methods for Localization: The Quantitative Metric Approach1. A priori Map: Graph, metric2. Feature Extraction (e.g. line segments)3. Matching:Find correspondenceof features4. Position Estimation:e.g. Kalman filter, Markovrepresentation of uncertaintiesoptimal weighting acc. to a priori statisticsCourtesy K. Arras
34Gaining Information through motion: (Multi-hypotheses tracking) 1Believe stateCourtesy S. Thrun, W. Burgard
35Grid-Based Metric Approach 1Grid-Based Metric ApproachGrid Map of the Smithsonian’s National Museum of American History in Washington DC. (Courtesy of Wolfram Burger et al.)Grid: ~ 400 x 320 = 128’000 pointsCourtesy S. Thrun, W. Burgard
36Methods for Localization: The Quantitative Topological Approach 1Methods for Localization: The Quantitative Topological Approach1. A priori Map: Graphlocally uniquepointsedges2. Method for determining the local uniquenesse.g. striking changes on raw data level or highly distinctive features3. Library of driving behaviorse.g. wall or midline following, blind step, enter door, application specific behaviorsExample: Video-based navigation with natural landmarksCourtesy of [Lanser et al. 1996]
37Map Building: How to Establish a Map 1Map Building: How to Establish a Map1. By Hand2. Automatically: Map BuildingThe robot learns its environmentMotivation:- by hand: hard and costly- dynamically changing environment- different look due to different perception3. Basic Requirements of a Map:a way to incorporate newly sensed information into the existing world modelinformation and procedures for estimating the robot’s positioninformation to do path planning and other navigation task (e.g. obstacle avoidance)Measure of Quality of a maptopological correctnessmetrical correctnessBut: Most environments are a mixture of predictable and unpredictable features hybrid approachmodel-based vs. behaviour-basedpredictabilityCourtesy K. Arras
38Map Building: The Problems 1Map Building: The Problems1. Map Maintaining: Keeping track of changes in the environmente.g. disappearingcupboard- e.g. measure of belief of each environment feature2. Representation and Reduction of Uncertaintyposition of robot -> position of wallposition of wall -> position of robotprobability densities for feature positionsadditional exploration strategiesCourtesy K. Arras
39Map Building: Exploration and Graph Construction 1Map Building: Exploration and Graph Construction1. Exploration- provides correct topology- must recognize already visited location- backtracking for unexplored openings2. Graph ConstructionWhere to put the nodes?Topology-based: at distinctive locationsMetric-based: where features disappear or get visibleCourtesy K. Arras
40Control of Mobile Robots 1Control of Mobile RobotsMost functions for save navigation are ’local’ not involving localization nor cognitionLocalization and global path planning è slower update rate, only when neededThis approach is pretty similar to what human beings do.Raw dataEnvironment ModelLocal Map"Position"Global MapActuator CommandsSensingActingInformationExtractionPathExecutionCognitionPath PlanningKnowledge,Data BaseMissionCommandsReal WorldEnvironmentLocalizationMap BuildingMotion ControlPerceptiongloballocal