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Potential Fields & Hybrid Architectures

COMP329 Lectures

Potential Fields

The robot is treated as a point under the influence of an artificial potential field.

  • The filed depends on the targets and goal as well as desired travel directions.
    • The goal attracts it, whilst obstacles repel it.
  • The strength of the field may change with the distance to the obstacle or target.

The robot travels along the derivative of the potential.

This is similar to a ball rolling down a smooth surface.

Types of Fields

  • Uniform - guides the robot in a straight line

    uniform field

  • Perpendicular - pushes the robot away from linear obstacles

    perpendicular field

  • Tangential - guides the robot around an obsticle

    tangential field

  • Attractive - draws the robot to a point

    attractive field

    This can be useful for defining waypoints in a path.

  • Repulsive - pushes the robot away from a point

    repulsive field

Local Minima

One issue with potential fields is local minima. This is a well in the field.

There are various escape options:

  • Backtracking
  • Random Motioon
  • Planner to search for a sub-optimal plan to escape.
  • Increase potential of visited regions.

Characteristics of Potential Fields

Advantages:

  • Easy to visualise.
  • Easy to combine different fields.

Disadvantages:

  • High update rates necessary.
  • Parameter tuning is important.

Hybrid Architectures

Hybrid architectures combine both deliberative and reactive systems in order to control a robot.

In such an architectures, an agents control subsystems are arranged into a hierachy with higher layers dealing with information at increasing levels of abstraction.

Horizontal Layers

  • Layers are each directly connected to the sensory input and action output.
  • Each layer acts like an agent, producing suggestions as to what action to perform.
graph LR

pi[Perceptual Input] --> l1[Layer 1]
pi[Perceptual Input] --> l2[Layer 2]
pi[Perceptual Input] --> l3[Layer 3]
pi[Perceptual Input] --> ln[Layer n]
l1 --> ao[Action Output]
l2 --> ao[Action Output]
l3 --> ao[Action Output]
ln --> ao[Action Output]

Vertical Layering

Sensory input and action output are each dealt with by at most one layer each.

  • Vertical Layering (one pass control):

      graph LR
  pi[Perceptual Input] --> l1[Layer 1]
  l1 --> l2[Layer 2]
  l2 --> l3[Layer 3]
  l3 --> ln[Layer n]
  ln --> ao[Action Output]

  • Vertical Layering (two pass control):

      graph LR
  pi[Perceptual Input] -->|1| l1[Layer 1]
  l1 -->|2| l2[Layer 2]
  l2 -->|3| l3[Layer 3]
  l3 -->|4| ln[Layer n]
  ln -->|5| l3
  l3 -->|6| l2
  l2 -->|7| l1
  l1 -->|8| ao[Action Output]

Ferguson - TouringMachines

The TouringMachine architecture consists of perception and action subsystems:

  • These interface directly with the agent’s environment, and three control layers, embedded in a control framework, which mediates between the layers.
graph LR
si[Sensor Input] --> pss[Perceptual Sub-System]
pss --> ml[Modelling Layer]
pss --> pl[Planning Layer]
pss --> rl[Reactive Layer]
subgraph Control Sub-System
ml
pl
rl
end
ml --> as[Action Subsystem]
pl --> as
rl --> as
as --> Actions

The control sub-system mediates between the layers.

  • The reactive layer is implemented as a set of situation-action rules (like a subsumption architecture).
  • The planning layer constructs plans and selects actions to execute in order to achieve the agents goals
  • The modelling layer contains symbolic representations of the cognitive state of other entities in the agent’s environment.