Behavior Based Robotic

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Behavior Based Robotic
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• ???? Behavior Based Robotic
• Ronald C. Arkin

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• Behavior-based roboticists argue that there is
  much that can be gained for robotics through the
  study of neuroscience, psychology, and ethology.
• Neuroscience the study of the nervous system's
  anatomy, physiology, biochemistry,and molecular
  biology.
• Psychology the study of mind and behavior
• Ethology the study of animal behavior in
  natural conditions
• The behavior-based roboticist needs to decide how
  to use results from these
• other disciplines. Some scientists attempt to
  implement these results as closely
• as possibly, concerning themselves primarily with
  testing the underlying hypotheses of the
  biological models in question. Others choose to
  abstract the
• underlying details and use these models for
  inspiration to create more intelligent
• robots, unconcerned with any impact within the
  disciplines from which the original models arose.
  We will see examples of both approaches within
  this
• book.

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• Many different techniques and approaches for
  robotic control have been developed.
• Figme 1.10 depicts a spectrum of current robot
  control strategies. The
• left side represents methods that employ
  deliberative reasoning and the right
• represents reactive control. A robot employing
  deliberative reasoning requires
• relatively complete knowledge about the world and
  uses this knowledge to
• predict the outcome of its actions, an ability
  that enables it to optimize its performance
• relative to its model of the world. Deliberate
  reasoning often requires
• strong assumptions about this world model,
  primarily that the knowledge upon
• which reasoning is based is consistent, reliable,
  and certain. If the information
• the reasoner uses is inaccurate or has changed
  since obtained, the outcome of
• reasoning may err seriously. In a dynamic world,
  where objects may be moving
• arbitrarily (e.g., in a battlefield or a crowded
  corridor), it is potentially dangerous
• to rely on past information that may no longer be
  valid. Representational
• world models are therefore generally constructed
  from both prior knowledge
• about the environment and incoming sensor data in
  support of deliberation.

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Deliberative reasoning systems often have several
common characteristics

• They are hierarchical in structure with a clearly
  identifiable subdivision of
• functionality, similar to the organization of
  commercial businesses or military
• command.
• Communication and control occurs in a
  predictable and predetermined manner,
• flowing up and down the hierarchy, with little if
  any lateral movement.
• Higher levels in the hierarchy provide subgoals
  for lower subordinate levels.
• Planning scope, both spatial and temporal,
  changes during descent in the
• hierarchy. Time requirements are shorter and
  spatial considerations are more
• local at the lower levels.
• They rely heavily on symbolic representation
  world models.

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Reactive Systems

• The right side of the spectrum depicted in figure
  1.10 represents reactive systems.
• Simply put, reactive control is a technique for
  tightly coupling perception
• and action, typically in the context of motor
  behaviors, to produce timely
• robotic response in dynamic and unstructured
  worlds.
• We further define the following
• An individual behavior a stimulus/response
  pair for a given environmental
• setting that is modulated by attention and
  detennined by intentio,n.
• Attention prioritizes tasks and focuses
  sensory resources and is detennined
• by the current environmental context .
• Intention determines which set of behaviors
  should be active based on the
• robotic agent's internal goals and objectives .
• Overt or emergent behavior the global behavior
  of the robot or organism as
• a consequence of the interaction of the active
  individual behavior

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