(c) KSIS Politechnika Poznanska

Transcription

1 Fundamentals of Autonomous Systems Control architectures in robotics Dariusz Pazderski 1 1 Katedra Sterowania i In»ynierii Systemów, Politechnika Pozna«ska 9th March 2016

2 Introduction Robotic paradigms In robotics, a robotic paradigm is a mental model of how a robot operates. A robotic paradigm can be described by the relationship between the three primitives of robotics: SENSE-PLAN-ACT. SENSE takes information (from sensors) and produces output for other components to use. PLAN takes information (from sensors or other functional components plus its world knowledge) and produces tasks to perform. ACT functional components which carry out the tasks (typically associated with output.)

3 Introduction Robot-environment Figure: Interaction robot-environment

4 Introduction Fundamental robot paradigms The Hierarchical (Deliberative) Paradigm The Reactive Paradigm The Hybrid Paradigm

5 Introduction Evaluation of the control architectures (1) Goal oriented: capability of the control system to provide means to accomplish multiple goals. Flexibility: Ability of adding new sub-systems or making any modications and additions to a system functions without disrupting the established functionality. Ease of application: Refers to ease of an architecture to be understood, developed, tested and debugged. Reactivity: Ability of a system to respond and adapt to the sudden changes in the environment. Optimal operation: Capability of a system to obtain optimal cost function in motion criteria such as distance, time, oscillation, etc.

6 Introduction Evaluation of the control architectures (2) Task learning: Ability of the system to learn through a teach mode or operation to carry out specic tasks. Robustness: Capability of a system to handle sudden changes, imperfect inputs, and unexpected malfunctions. Planning: A set of partially ordered tasks for the robot to perform and work on a problem at the highest level of abstraction possible so as to make its problem space as small as possible until a plan is nished. Eciency: Contains the capabilities and performance of a system to maximize individual utility and cooperation of subtasks to generate an optimized and smooth trajectory.

7 The Hierarchical (Deliberative) Paradigm General description Outline 1 Introduction 2 The Hierarchical (Deliberative) Paradigm General description Shakey robot and STRIPS planner 3 Reactive paradigm General description Motor Schema Subsumption architecture 4 The Hybrid Paradigm

8 The Hierarchical (Deliberative) Paradigm General description Introduction (1) Figure: Hierarchical structure SPA

9 The Hierarchical (Deliberative) Paradigm General description Introduction (2) The Hierarchical Paradigm is historically the oldest method of organizing intelligence in mainstream robotics. Prevalent from 1967 to Processing (intelligence) revolves around an internal representation of the world in which the robot acts. This world view might contain a physical model of the world, as well as the machine's opinion of it (dangerous areas, for example). The robot operates via top-down reasoning/control. Based on introspective view of how people think. This view might apply to some aspects of human reasoning, but it is unlikely to be an psychologist's view of how a sentient being navigates through an obstacle course.

10 The Hierarchical (Deliberative) Paradigm General description Advantages Goal oriented Predictable (deterministic) Clear and sound reasoning (optimization is possible)

11 The Hierarchical (Deliberative) Paradigm General description Disadvantages Computationally expensive (cost of world modelling both in terms of creating the model and analysing it at each step) Frame problem: actions can have many eects Requires exact knowledge of the world Symbol grounding problem (the planning is done inside the world model and not in the real world, an important goal is to reduce the dierence between both) Discretization

12 The Hierarchical (Deliberative) Paradigm Shakey robot and STRIPS planner Outline 1 Introduction 2 The Hierarchical (Deliberative) Paradigm General description Shakey robot and STRIPS planner 3 Reactive paradigm General description Motor Schema Subsumption architecture 4 The Hybrid Paradigm

13 The Hierarchical (Deliberative) Paradigm Shakey robot and STRIPS planner Shakey Shakey was developed from approximately 1966 through 1972 The project was funded by the Defence Advanced Research Projects Agency DARPA and it was executed in Articial Intelligence Center of Stanford Research Institute) Programmed with LISP using STRIPS (Stanford Research Institute Problem Solver). Most notable results of the project include the A* search algorithm, the Hough transform, and the visibility graph method.

14 The Hierarchical (Deliberative) Paradigm Shakey robot and STRIPS planner STRIPS STRIPS is a classical planning language, representing plan components as states, goals, and actions, allowing algorithms to parse the logical structure of the planning problem to provide a solution (a sequence of actions). STRIPS instance is composed of: an initial state the specication of the goal states situation which the planner is trying to reach a set of actions: preconditions (what must be established before the action is performed) postconditions (what is established after the action is performed)

15 The Hierarchical (Deliberative) Paradigm NASREM the standard deliberative control architecture Introduction The NASA/NBS Standard Reference Model Architecture for the Space Station Telerobot Control System designed in 1989 Denes a logical computing architecture for telerobotics. Incorporates many AI concepts: goal decomposition, hierarchical planning, model driven image analysis, blackboard systems and expert systems. It is hierarchically and horizontally decomposed.

16 The Hierarchical (Deliberative) Paradigm NASREM the standard deliberative control architecture Structure (1) Figure: The general structure of control system

17 The Hierarchical (Deliberative) Paradigm NASREM the standard deliberative control architecture Hierarchical layers Layer 1 coordinates are transformed and outputs are servoed Layer 2 mechanical dynamics is computed Layer 3 obstacles are observed and avoided Layer 4 tasks on objects are transformed into movements of eectors Layer 5 tasks on group of objects are sequenced and scheduled Layer 6 objects are batched into groups, resources are assigned to worksites, and parts and tools are routed and scheduled between worksites.

18 The Hierarchical (Deliberative) Paradigm NASREM the standard deliberative control architecture Horizontal decomposition Task Decomposition Plans and executes the decomposition of high level goals into low level actions. Decomposition is both: temporal - into sequential actions along the time line and spatial - into concurrent actions by dierent subsystems World Modelling Consisting of: model = best estimate and evaluation of the history, current, and possible future states of the world. knowledge base: Contains state variables, maps, lists of objects and events and their attributes. Sensory Processing Reads sensor values and provides the ltered and processed values to the world model.

19 The Hierarchical (Deliberative) Paradigm NASREM the standard deliberative control architecture The timing of the tasks (1) At each hierarchical level planner modules decompose task commands into strings of 2-10 planned subtasks for execution. strings of sensed events are summarized, integrated, and "chunked" into single events at the next higher level. Time horizon increases by an order of magnitude at each level of the hierarchy -> exponential increase in time.

20 The Hierarchical (Deliberative) Paradigm NASREM the standard deliberative control architecture The timing of the tasks (2) Figure: An example a timing diagram for all six leveles for taks decomposition and sensory processing.

21 The Hierarchical (Deliberative) Paradigm NASREM the standard deliberative control architecture Hierarchical planning The principle of hierarchical planning: Higher levels in the hierarchies create subgoals for the lower levels. The exponentially increase of time with the number of hierarchy steps. Figure: Three levels if real-time planning illustrating the shrinking planning horizon and greater detail at successively lower levels of the hierarchy.

22 Reactive paradigm General description Outline 1 Introduction 2 The Hierarchical (Deliberative) Paradigm General description Shakey robot and STRIPS planner 3 Reactive paradigm General description Motor Schema Subsumption architecture 4 The Hybrid Paradigm

23 Reactive paradigm General description Introduction (1) Figure: Reactive structure SPA (no P!)

24 Reactive paradigm General description Introduction (2) From early 80's in XX century scientists began to investigate models of animal intelligence from the biological and cognitive sciences to get insight into what was missing in robotics. The principles of animal intelligence are extremely important. Animals live in an open world, and we would like to overcome the closed world assumption in robotics. Concept of agent as an abstract intelligent system: The agent is self-contained and independent. It has its own brains and can interact with the world to make changes or to sense what is happening. It has self-awareness.

25 Reactive paradigm General description Computational theory In order to design an agent one can consider computational theory proposed by Marr a neurophysiologist who tried to recast biological vision processes into new techniques for computer vision. The levels in a computational theory can be greatly simplied as: Level 1: Existence proof of what can/should be done Level 2: Decomposition of what into inputs, outputs, and transformations Level 3: How to implement the process

26 Reactive paradigm General description Behaviour (1) A behaviour is a mapping of sensory inputs to a pattern of motor actions which then are used to achieve a task. Reexive behaviours are stimulus-response (S-R). They imply no need for any type of cognition: if you sense it, you do it. Reactive behaviours are learned, and then consolidated to where they can be executed without conscious thought. Conscious behaviours are deliberative (assembling a robot kit, stringing together previously developed behaviors, etc.). In robotics reactive behaviour = reexive behaviour!

27 Reactive paradigm General description Behaviour (2) Ethologists have been able to identify three form of reective behaviours: Reexes the magnitude of response is directly proportional to the intensity of the stimulus and as long as the stimulus lasts. Taxes the response is an orientation towards of away from the stimulus. Fixed Action Patterns the response lasts for a longer time than the stimulus and where the behaviour can be inuenced from multiple stimuli rather than form a simple stimulus.

28 Reactive paradigm General description Controlling behaviours Innate Releasing Mechanisms (IRMs) act as control switches for realising behaviours and provide low level coordination mechanism for triggering and inhibiting basic behaviours. Competing behaviours control mechanisms: Equilibrium (the behaviours seem to balance each other out) Dominance of one (winner take all) Cancellation (the behaviours cancel each other out)

29 Reactive paradigm General description Properties (1) The robot has multiple instances of Sense-Act couplings. These couplings are concurrent behaviours, which take the local sensing data and compute the best action to take independently of what the other processes are doing. The robot will do a combination of behaviours. No models: The world is its own, best model Behaviours are implemented as circuits (hardware) or as low computational complexity algorithms (software). No memory required. Behaviours are pure stimulus-response reexes.

30 Reactive paradigm General description Properties (2) Their ability to build a navigation system in an incremental way of layer upon layer. Their quick reaction to the unknown and dynamic environment. They do not require modelling and storing the whole model of the environment. There is less computation and shorter delay between perception and action. And they are more robust and reliable which means in case of a behaviour unit failure, the other units continue the tasks.

31 Reactive paradigm Motor Schema Outline 1 Introduction 2 The Hierarchical (Deliberative) Paradigm General description Shakey robot and STRIPS planner 3 Reactive paradigm General description Motor Schema Subsumption architecture 4 The Hybrid Paradigm

32 Reactive paradigm Motor Schema In psychology and cognitive science, a schema describes an organized pattern of thought or behavior that organizes categories of information and the relationships among them. It can also be described as a mental structure of preconceived ideas, a framework representing some aspect of the world, or a system of organizing and perceiving new information. In 1989 Arkin addressed the implication of schema theory for autonomous robots: Schemas provides large grain modularity for expressing the relationship between motor control and perception. Schemas act concurrently as individual distributed agents in a cooperative yet competing manner and thus are readily mappable onto distributed architectures. Schemas provides a set of behavioural primitives by which more complex behaviours can be constructed. Cognitive and neuroscientic support exists to support this approach. It can be update by adding new models.

33 Reactive paradigm Motor Schema Particular properties of the method Behavioural responses are all represented in a single uniform format: vectors generated using a potential eld approach (a continuous response encoding) Coordination is achieved through cooperative means by vector (weighted) addition. No predened hierarchy exists for coordination. The behaviours are congured at run-time based on the robot's intentions, capabilities, and environment constraints. The structure can be dynamically changed. Pure arbitration is not used. Each behaviour can contribute in varying degrees to the robot's overall response. Perceptual uncertainty can be reected in the behaviours response by allowing it to serve as an input with behavioural computation. Each motor schema has as output an action vector

34 Reactive paradigm Motor Schema Figure: Perception-action schema relationships (PS Perceptual Schema, PSS Perceptual Subschema, MS Motor Schema, ES Environment Sensor)

35 Reactive paradigm Subsumption architecture Outline 1 Introduction 2 The Hierarchical (Deliberative) Paradigm General description Shakey robot and STRIPS planner 3 Reactive paradigm General description Motor Schema Subsumption architecture 4 The Hybrid Paradigm

36 Reactive paradigm Subsumption architecture Introduction Rodney Brooks' subsumption architecture the most inuential of the purely Reactive Paradigm systems. The philosophy behind Subsumption Architecture is that the world should be its own model. A behaviour is a network of sensing and acting modules which accomplish a task. Modules are grouped into layers of competence. Modules in a higher layer can override, or subsume, the output from behaviours in the next lower layer. The solution in consumption is a type of winner-takes all, where the winner is always the higher layer. Higher layers may subsume and inhibit behaviours in lower layers.

37 Reactive paradigm Subsumption architecture Layers The modules are augmented nite state machines AFSM. An AFSM is equivalent to the interface between the schemas and the coordinated control strategy in a behavioural schema. In terms of schema theory, a subsumption behaviour is actually a collection of one or more schemas into an abstract behaviour. The use of internal state is avoided. A task is accomplished by activating the appropriate layer, which then activates the lower layers below it, and so on.

38 Reactive paradigm Subsumption architecture AFSM Figure: Augmented Finite State Machine (AFSM) with connections

39 Reactive paradigm Subsumption architecture Example (1) Figure: Example: level 0 avoid

40 Reactive paradigm Subsumption architecture Example (2) Figure: Example: level 1 wander

41 Reactive paradigm Subsumption architecture Example (3) Figure: Example: level 2 folllow corridors

42 The Hybrid Paradigm Deliberative vs. Reactive Paradigm (1) Note that the hierarchical paradigm lacks low-level, immediate interaction with its environment; the types of behaviors that lead to concepts like "muscle memory" The reactive paradigm appears to lack the ability to explain and incorporate high-level human intelligence have capabilities of reacting only at the stimulus-response level reected in the work of B.F. Skinner be incapable of any form of planning (ecient path planning, map making, performance evaluation of the robot itself)

43 The Hybrid Paradigm Deliberative vs. Reactive Paradigm (2) Figure: Properties of deliberative and reactive approaches.

44 The Hybrid Paradigm Concept of hybrid approach Idea: combine properties of hierarchical methods with reactive methods in order to produce the desired arachitecture for the complex scenarios (synergy eect!). The planning aspect of hierarhical paradigm should be integrated with the rapid execution capabilities of the reactive paradigm. The key of hybrid paradigm: to allow the Plan stage to be executed separately from Sencer-Act sequence.

45 The Hybrid Paradigm Structure Figure: Hybrid structure SPA

46 The Hybrid Paradigm Integrating plans Hierarchical integration makes the planning layer and the reactive layer to be aligned vertically in which the planning layer provides the information on which the reactive layer acts. Second method involves planning layer working prior to or concurrently with the reactive behaviours, updating the robot behavioural parameters or changing the world state. Coupled planning-reacting method makes the planning and reactive activities to occur concurrently, allowing each one to interact with the other in real-time. The sensors data are shared with reactive and planning modules.

47 The Hybrid Paradigm Hybrid features Sequencer Resource Manager Cartographer Mission Planner Performance Monitor

48 The Hybrid Paradigm Example AuRA (Autonomous Robot Architecture) Figure: Structure of architecture proposed by Arkin in 1997