Robotic Paradigms and Control Architectures Jan Faigl Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Lecture 02 B4M36UIR – Artificial Intelligence in Robotics Jan Faigl, 2017 B4M36UIR – Lecture 02: Robotic Paradigms 1 / 46
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Robotic Paradigms and Control Architectures · 2017-12-06 · Robotic Paradigms and Control Architectures JanFaigl Department of Computer Science FacultyofElectricalEngineering...
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Robotic Paradigms and ControlArchitectures
Jan Faigl
Department of Computer ScienceFaculty of Electrical Engineering
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
RobotA robot perceive an environment using sensors to control its actuators
Sensor Controller
Actuators
The main parts of the robot correspond to the primitives of robotics:Sense, Plan, and ActThe primitives form a control architecture that is called roboticparadigm
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Robotic Paradigms
Primitives of robotics are: Sense, Plan, and ActRobotic paradigms – define relationship between the primitivesThree fundamental paradigms have proposed
Hierarchical paradigm – purely deliberative system
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Disadvantages of Hierarchical ModelDisadvantages are related to planning – Computational requirements
Planning can be very slow and the “global world” representation hasto contain all information needed for planning
Sensing and acting are always disconnected
The “global world” representation has to be up to dateThe world model used by the planner has to be frequently updatedto achieve a sufficient accuracy for the particular task
A general problem solver needs many facts about the world to searchfor a solutionSearching for a solution in huge search space is quickly computation-ally intractable and this problem is related to the frame problem
Even simple actions need to reason over all (irrelevant) details
Frame problem – a problem of representing the real-word situa-tions to be computationally tractable
Decomposition of the world model into parts thatbest fit the type of actions
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Examples of Hierarchical Models
Despite of drawbacks of the hierarchical paradigm, it has been de-ployed in various systemsAn example are Nested Hierarchical Controller and NIST RealtimeControl System
It has been used until 1980 when the focus has been changedon the reactive paradigm
The development of hierarchical models further exhibit additionaladvancements, e.g., to address the frame problemThey also provide a way how to organize the particular blocks ofthe control architectureFinally, the hierarchical model represents an architecture that sup-port evolution and learning systems towards fully autonomous con-trol
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
NIST Real-time Control System (RCS)
Motivated to create a guide for manufactures for addingintelligence to their robotsIt is based on NHC and the main feature it introduces is a set ofmodels for sensory perceptionIt introduces preprocessing step between the sensory perceptionand a world modelThe sensor preprocessing is called as feature extraction
E.g., extraction of the relevant information for creating a model ofthe environment such as salient objects utilized for localization
It also introduced the so called Value Judgment moduleAfter planing, it simulates the plan to ensure its feasibility
Then, the plan is passed to Behavior Generation module toconvert the plans into actions that are performed (ACT).
The “behavior” is further utilized in reactive and hybrid architectures
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Hierarchical Paradigm – SummaryHierarchical paradigm represents deliberative architecture also calledsense-plan-actThe robot control is decomposed into functional modules that aresequentially executed
The output of sense module is input of the plan module, etc
Centralized representation and reasoningMay need extensive and computationally demanding reasoningEncourage open loop execution of the generated plansSeveral architectures have been proposed, e.g., using STRIP plannerin Shakey, Nested Hierarchical Controller (NHC), NIST RealtimeControl System (RCS)
NIST – National Institute of Standards and Technology
Despite of the drawbacks, hierarchical architectures tend to supportthe evolution of intelligence from semi-autonomous control to fullyautonomous control
Navlab (1996), 90% of autonomous steering from Washington DC to Los Angeles
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Reactive Paradigm
The reactive paradigm is a connection of sensing with acting
SENSE ACT
It is biological inspired as humans and animals provide an evidenceof intelligent behavior in an open world, and thus it may be possibleto over come the close world assumptionInsects, fish, and other “simple” animals exhibit intelligent behaviorwithout virtually no brainThere must be same mechanism that avoid the frame problemFor a further discussion, we need some terms that to discuss prop-erties of “intelligence” of various entity
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Agent and Computational-Level Theory
Agent is a self-contained and independent entityIt can interact with the world to make changes and sense the worldIt has self-awareness
The reactive paradigm is influenced by Computational-Level Theo-riesD. Marr a neurophysiologist working computer vision techniques inspired by biological vision processes
Computational Level – What? and Why?What is the goal of the computation and why it is relevant?
Algorithmic level – How?Focus on the process rather the implementation
How to implement the computational theory? What is the rep-resentation of input and output? What is the algorithm for thetransformation of input to output?
Physical level – How to implement the process?How to physically realize the representation and algorithm?
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Reflexive Behaviors
Reflexive behaviors are fast “hardwired” if there is sense, it producethe actionIt can categorized into three types1. Reflexes – the response lasts only as long as the stimulus
The response is proportional to the intensity of the stimulus
2. Taxes – the response to stimulus results in a movement towards oraway of the stimulus,
E.g., moving to light, warm, etc.
3. Fixed-Action Patterns – the response continues for a longer dura-tion than the stimulus
The categories are not mutually exclusiveAn animal may keep its orientation to the last sensed location of thefood source (taxis) even when it loses the “sight” of it (fixed-actionpatterns)
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Releasing Behavior – When to Stop/Suppress the BehaviorThe internal state and/or motivation may release the behavior
Being hungry results in looking for food
Behaviors can be sequenced into complex behaviorInnate releasing mechanism is a way to specify when a behaviorgets turned on and offThe releaser acts as a control signal to activate a behavior
If the behavior is not released, it does not respond to sensoryinputs and it does not produce the motor outputs
Patternof motoraction
SensorInput Behavior
Releaser
The releaser filters the perception
Notice, the releasers can be compound, i.e., a multiple conditionshave to be satisfied to release the behavior
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Concurrent Behaviors
Behaviors can execute concurrently and independently which mayresults into different interactions
Equilibrium – the behaviors seems to balance each other outE.g., Undecided behaviour of squirrel whether to go for a food or rather run
avoiding humanDominance of one – winner takes all as only one behavior canexecute and not both simultaneouslyCancellation – the behaviors cancel each other outE.g., one behavior going to light and the second behavior going out the light
It is not known how different mechanisms for conflicting behaviorsare employedHowever, it is important to be aware how the behaviors will interactin a robotic system
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Behaviors Summary
Behavior is fundamental element in biological intelligence and is alsofundamental component of intelligence in robotic systemsComplex actions can be decomposed into independent behaviorswhich couple sensing and actingBehaviors are inherently parallel and distributedStraightforward activation mechanisms (e.g., boolean) may be usedto simplify control and coordination of behaviorsPerception filters may be used to simply sensing that is relevant tothe behavior (action-oriented perception)Direct perception reduces computational complexity of sensing
Allows actions without memory, inference or interpretation
Behaviors are independent, but the output from one behaviorCan be combined with another to produce the outputMay serve to inhibit another behavior
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Reactive ParadigmReactive paradigm originates from dissatisfaction with hierarchicalparadigm (S-P-A) and it is influenced by ethology
ActuatorsSensors
Build map
Explore
Wander
Avoid Collisions
Sense Act
Contrary to S-P-A, which exhibit horizontal decomposition, thereactive paradigm (S-A) provides vertical decomposition
Behaviors are layered, where lower layers are “survival” behaviorsUpper layers may reuse the lower, inhibit them, or create paralleltracks of more advanced behaviors
If an upper layer fails, the bottom layers would still operate
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Multiple, Concurrent Behaviors
Strictly speaking, one behavior does not know what another behav-ior is doing or perceiving
Behavior
Behavior
Behavior
SENSE ACT
Mechanisms for handling simultaneously active multiple behaviorsare needed for complex reactive architecturesTwo main representative methods have been proposed in literature
Subsumption architecture proposed by Rodney BrooksPotential fields methodology studied by Ronald Arkin, David Pay-ton, et al.
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
An Overview of Subsumption ArchitectureSubsumption architecture has been deployed in many robots thatexhibit walk, collision avoidance, etc. without the “move-think-move-think” pauses of ShakeyBehaviors are released in a stimulus-response wayModules are organized into layers of competence1. Modules at higher layer can override
(subsume) the output from the behaviorsof the lower layerWinner-take-all – the winner is the higher layer
Level 0Sensors Actuators
Level 2
Level 1
Level 3
2. Internal states are avoidedA good behavioral design minimizes the internal states, that can be,e.g., used in releasing behavior
3. A task is accomplished by activating the appropriate layer thatactivities a lower layer and so on
In practice, the subsumption-based system is not easily taskableIt needs to be reprogrammed for a different task
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Hybrid Paradigm
The main drawback of the reactive-based architectures is a lack ofplanning and reasoning about the world
E.g., a robot cannot plan an optimal trajectory
Hybrid architecture combines the hierarchical (deliberative)paradigm with the reactive paradigm Beginning of the 1990’s
SENSE
PLAN
ACT
Hybrid architecture can be described as Plan, then Sense-ActPlanning covers a long time horizon and it uses global world modelSense-Act covers the reactive (real-time) part of the control
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Characteristics of Reactive Paradigm in Hybrid Paradigm
Hybrid paradigm is an extension of the Reactive paradigmThe term behavior in hybrid paradigm includes reflexive, innate, andlearned behaviors In reactive paradigm, it connotes purely reflexive behaviors
Behaviors are also sequenced over timed and more complex emer-gent behaviors can occurBehavioural management – planning which behavior to use re-quires information outside the particular model (a global knowledge)
Reactive behavior works without any outside knowledge
Performance monitor evaluates if the robot is making progress toits goal, e.g., whether the robot is moving or stucked
In order to monitor the progress, the program has to know whichbehavior the robot is trying to accomplish
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Components of Hybrid Deliberative/Reactive Paradigm
Sequencer – generates a set of behaviors to accomplish a subtaskResource Manager – allocates resources to behaviors, e.g., a se-lection of the suitable sensors
In reactive architectures, resources for behaviors are usually hardcoded.
Cartographer – creates, stores, and maintains map or spatial in-formation, a global world model and knowledge representation
It can be a map but not necessarily
Mission Planner – interacts with the operator and transform thecommands into the robot term
Construct a mission plan, e.g., consisting of navigation to some placewhere a further action is taken
Performance Monitoring and Problem Solving – it is a sort ofself-awareness that allows the robot to monitor its progress
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Existing Hybrid Architectures
Managerial architectures use agents for high level planning at thetop, then there are agents for plan refinement to the reactive be-haviors at the lowest level
E.g., Autonomous Robot Architecture and Sensor Fusion Effects
State-Hierarchy architectures organize activity by scope of timeknowledge E.g., 3-Tiered architectures
Model-Oriented architectures concentrate on symbolic manipulationaround the global world E.g., Saphira
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Example of Reactive Collision Avoidance
Biologically inspired reactive architecture with vision sensor and CPGNotice, all is hardwired into the program and the robot goes ’just’ahead with avoiding intercepting obstacles
CPG-based locomotion control can beparametrized to steer the robot mo-tion to left or right to avoid collisionswith approaching objects
Avoiding collisions with obstacles andintercepting objects can be basedon the visual perception inspired bythe Lobula Giant Movement Detector(LGMD)
LGMD is a neural network detectingapproaching objects
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
A Control Schema for a Mobile RobotA general control schema for a mobile robot consists of Perception Mod-ule, Localization and Mapping Module, Path Planning Module, andMotion Control Module
Actuatorscommands
PathExecution
Acting
PathPlanning
Missioncommands
"Position", Global Map
Path
Rawdata
InformationExtraction andInterpretation
Sensing
LocalizationMap Building
Environment ModelLocal Map
Real Environment
KnowledgeData Base
Perception Motion Control
In B4M36UIR, we focus on Path Planning ModuleJan Faigl, 2017 B4M36UIR – Lecture 02: Robotic Paradigms 40 / 46
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Motion Control
An important part of navigation is execution of the planned pathMotion control module is responsible in path realization
Position control – aims to navigate the robot to the desired locationPath-Following – the controller aims to navigate the robot alongthe given pathTrajectory-Tracking – it differs from the path-following in that thecontroller forces the robot to reach and follow a time parametrizedreference (path) E.g., a geometric path with an associated timing law
The controller can be realized as one of two typesFeedback controllerFeedforward controller
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
FeedBack Controller
The difference between the goal pose and the distance traveled sofar is the error used to control the motorsThe controller commands the motors (actuators) which change thereal robot poseSensors, such as encoders for a wheeled robot, provide the informa-tion about the traveled distance
Sensors Actuators
ControllerMotor commands
Input
Output"Current Pose"
+
-"Goal Pose"Feedback"Distance Traveled"
Notice, the robot may stuck, but it is not necessarilydetected by the encoders
Robotics Paradigms Hierarchical Paradigm Reactive Paradigm Hybrid Paradigm Example of Collision Avoidance Robot Control
Feed-Forward Controller
In feed-forward controller, there is not a feedback from the real wordexecution of the performed actionsInstead of that, a model of the robot is employed in calculation ofthe expected effect of the performed action
Model
Motor commands
Input
Output"Current Pose"
+"Goal Pose" ActuatorsController+
Feedforward
In this case, we fully rely on the assumption that theactuators will performed as expected