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Robot Control Architectures

COMP329 Lectures

Agents With State

graph TB
Environment --> see
subgraph Agent
see --> next
next --> state
state --> next
state --> action
end
action --> Environment

Tasks for Agents

We want to tell agents to complete a particular task without telling them how to do it. We can achieve this with the following tools.

Utility Functions

  • We associate rewards with states that we want agents to bring about.
  • We associate utilities with individual states:
    • The task of the agent is to bring about states that maximise utility.

A task specification is a function which associates a real number with every environment sate:

\[u:E\rightarrow\Bbb R\]

Local Utility Functions

How to we apply a utility score to a state in a run?

It is difficult to specify a long term view when assigning utilities to individual states.

One option would be to discount the cost for later states. This is what happens in reinforcement learning.

Local Utility Function Example

Consider we have a robot that wants to reach a reward:

  • Each movement has a negative reward ($r=-0.04$)
    • This ensures that the agent will learn to get the reward in the shortest path.
  • The target has a positive reward ($r=1$)

The utility gained is the sum of the rewards received.

Mobile Robotics

To enable the robot to reach it’s goal then we need to consider the following:

  • Locomotion & Kinematics
  • Perception
  • Localisation & Mapping
  • Planning & Navigation

General Control Architecture

graph TB
l[Localisation/Map Building] -->|Position, Global Map| c[Cognition, Path Planning]
c -->|Path| pe[Path Execution]
subgraph Motion Control
pe -->|Actuator Commands| Action
end
Action -->|Real World, Environment| Sensing
subgraph Perception
Sensing -->|Raw Data| ie[Information Extraction]
end
ie -->|Environment Model, Local Map| l

Perception & Localisation

This is how the robot senses the world:

  • A map then says how these features sit relative to one another.
  • A robot localises by identifying features and the position in the map from which it could see them.

This is the process for finding a path through the map:

  • It is important that we avoid obstacles.

Architecture Approaches

Classical/Deliberative

Complete modelling:

  • Function Based
  • Horizontal Decomposition
graph LR
Sensors --> Perception
Perception --> lm[Localisation/Map Building]
lm --> cp[Cognition/Planning]
cp --> mc[Motion Control]
mc --> Actuators

Behaviour Based

  • Sparse or no modelling.
  • Behaviour Based
  • Vertical Decomposition
  • Bottom Up
graph LR
Sensors --> cd[Communicate Data]
Sensors --> da[Discover New Area]
Sensors --> dg[Detect Goal Position]
Sensors --> ao[Avoid Obsticals]
Sensors --> fw[Follow Right/Left Wall]
cd --> cf[Coordination Fusion]
da --> cf[Coordination Fusion]
dg --> cf[Coordination Fusion]
ao --> cf[Coordination Fusion]
fw --> cf[Coordination Fusion]
cf --> Actuators

Hybrid

This is a combination of the previous two approaches:

  • Let lower level pieces be behaviour based:
    • Localisation
    • Obstacle avoidance
    • Data collection
  • Let more cognitive pieces be deliberative:
    • Planning
    • Map building

Reactive Architectures

Deliberative architectures aren’t always ideal as planning may take too long and the environment may have changed.

Brooks Behavioural Languages

  1. Intelligent behaviour can be generated without explicit representations of the kind that symbolic AI proposes.
  2. Intelligent behaviour can be generated without explicit abstract reasoning of the kind that symbolic AI proposes.
  3. Intelligence is an emergent property of certain complex systems.

He identified two key ideas that have informed his research:

  1. Situatedness and embodiment: ‘Real’ intelligence is situated in the world, not in disembodied systems such as theorem provers or expert systems.
  2. Intelligence and emergence: ‘Intelligent’ behaviour arises as a result of an agent’s interaction with its environment. Also, intelligence is ‘in the eye of the beholder’; it is not an innate, isolated property.

Emergent Behaviour

When observed behaviour exceeds the programmed behaviour, then we have emergence.

Flocking is an emergent behaviour. Each agent uses the following rules:

  1. Don’t run into any other agent.
  2. Don’t get too far from other agents.
  3. Keep moving if you can.

Synergies

We can implement the following behaviours to follow a wall:

  1. Move forwards with a slight bias towards the wall.
  2. Avoid obsticles by turning in the opposite direction.