vlad estivill-castro (2016)vladestivill-castro.net/teaching/robotics.d/... · vlad estivill-castro...
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Vlad Estivill-Castro (2016)Robots for People
--- A project for intelligent integrated systems
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What is the courseabout?
◗ textbook◗ Introduction to Autonomous Mobile
Robots
◗ second editionRoland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza
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The three key questions in Mobile Robotics ◗ Where am I ?◗ Where am I going ?◗ How do I get there ?
• To answer these questions the robot has• to have a model of the environment (given or
autonomously built)• perceive and analyze the environment• find its position/situation within the environment• plan and execute the movement
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This course will deal with Locomotion and Navigation◗ Perception ◗ Localization and Mapping ◗ Planning◗ Motion Generation
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Course Content◗ Introduction◗ Locomotion (little)◗ Mobile Robot Kinematics◗ Perception◗ Mobile Robot Localization and Mapping◗ Planning and Navigation
◗ - a spin to useful techniques for Humanoid Robots, RoboCup, Nao
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General Control Scheme for Mobile Robot Systems◗ (Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza )
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Real WorldEnvironment
Sensing
InformationExtraction
LocalizationMap Building
Perc
eptio
n
CognitionPath Planning
Acting
PathExecution
Mot
or C
ontro
l
Raw data
“position”Global Map
map
Actuator Commands
Environment ModelLocal Map
MissionCommands
KnowledgeData Base
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Control loop◗ dynamically changing◗ no compact model available◗ many sources of uncertainties
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Real WorldEnvironment
Sensing
InformationExtraction
LocalizationMap Building
Perc
eptio
n
CognitionPath Planning
Acting
PathExecution
Mot
or C
ontro
l
Raw data
“position”Global Map
map
Actuator Commands
Environment ModelLocal Map
MissionCommands
KnowledgeData Base
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Two approaches◗ Classical AI
• complete modeling• function based
◗ New AI• sparse or no model• behavior based
◗ Solution• combine approaches
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LocalizationMap Building
Perception
CognitionPath Planning
Motor Control
sensors
actuators
LocalizationMap Building
Perception
CognitionPath Planning
Motor Control
sensors
actuators
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The Key for Autonomous Navigation◗ Environment Representation
• continuous metric -> x, y Θ• Discrete metric -> metric grid• Discrete Topological-> topological grid
◗ Environment Modeling• Raw sensor data
• large volume, low distinctiveness• Low level features
• medium volume of data, some distinctiveness• High level features
• low volume data high distinctiveness
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Environment representation◗ Odometry
• follow directions/commands like Navigator• follow a treasure
◗ Modified Environments• very specific/encoded information
◗ Feature based• Landmarks, Affordances of the environment
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Human navigation
◗ Topological and conceptual maps• the dimensions of Australia, and what is close,
north, south• number of buildings vs name of buildings
◗ Relations at different scales
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The Map Categories1. recognizable landmarks2. topological maps (graph model)
• games like snakes and ladders
3. metric topological maps• edges have weight equal to distance
4. Full metric maps• nodes are geo-positioned
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Understanding --- Probabilistic reasoning (Bayesian inference) ◗ Reasoning in the presence of uncertainties
and incomplete information◗ Combining preliminary information and
models with learning from observed data
◗ P(A|B) = P(B|A) P(A)/P(B)• the probability that the new situation is A once
I observed B is related to the chances of noticing B when in situation A and the ratio of oberving A with the chances of observing B
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Reducing Uncertainties◗ Where can I be?
◗ Anywhere
◗ Observe a door
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Reducing Uncertainties◗ Observe a door
◗ Walk a bit
◗ Observe a door again
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Metric Navigation: Probabilistic Position Estimation (Kalman Filter)
◗ Continuous, recursive and very compact• ACT PHASE
• state prediction via a motion model– obtain x(k+1 | k) and P(k+1 | k) given x(k|k) P(k|k)
• SEE PHASE• Balance the advice of where I am (result of the
motion model) and the advice of where I am (result of the sensor)
• Depending on how much I trust each
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State prediction: odometry◗ Incrementally (dead reckoning)
• There is a discrepancy into the real trajectory and the belief of where I have been
• DRIFT• cumulative error
◗ Under no error, current position is accurate
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Methods for Localization :Quantitative Metric Approach
1. A priori, know your environment• in graph, map
2. Extract features (line segments, landmarks)3. Match them with location in map4. Estimate your position
• Kalman filter, Markov filters (particle filters)• Balance observations with your belief• issues:
• estimation of uncertainties• weights to prior statistics
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Belief Representation◗ Well known probability models
• mixture of multi-variate normal distribution
◗ Descriptive statistics• Histograms
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Probabilistic SLAM◗ Simultaneous localization and mapping
(SLAM) • first characteristic question, What does the
world look like?• second characteristic question, Where am I?
◗ The robotics community claims to have solved the SLAM problem• lots of variations on the assumptions / sensors/ motions• issues of complexity• quality
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Summary• Mobile robotics
• the most challenging type of robotics– robots are anywhere in the world
• Many architectures, some well accepted but found insufficient, new approaches discussed
• Large body of techniques• feature extraction• sensor fusion• mapping, tracking and/or localization
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