Autonomous Robotic Agents
Agents have their origin in psychology, artificial intelligence,
and distributed artificial intelligence, integrating learning, planning,
reasoning, knowledge representation aspects, and have as goal to
execute complex tasks benefiting users, that otherwise would be
hard to accomplish. Users have the possibility of assigning goals
to be achieved by the agents, in contrast to conventional software
systems limiting the users to previously specified goals which cannot
An agent is anything which can be considered that perceives its
environment through sensors and responds or behaves in such environment
by means of effectors (Rusell & Norvig 1995).
An autonomous agents is one whose behavior is based mainly on its
own existence, although being able to use certain built-in knowledge.
Similar to the way evolution has given animals a number of built-in
reflexes so that they can survive until being capable of learning
by themselves, it is reasonable to give an intelligent agent with
certain initial knowledge and ability to learn.
As much as an agent acts based on integrated suppositions , its
behavior would be satisfactory only as much as those suppositions
are current, lacking any flexibility. A real autonomous agent will
be capable of successfully functioning under a broad environment
spectrum, given enough time to adapt. There is little or no dpendency
on abstract world representations, and behavior instead of plans
are the robot´s interaction with the world.
There exist different types pf autonomous agents:
- Human agents have organs, such as eyes and ears serving as sensors,
while body parts, such as hands, legs and mouth, serve as effectors.
- Robotic agents substitute sensors for cameras and readers, such
as infrared or ultrasound, and effectors are replaced by motors.
- Software agents receive perceptions and execute actions having
formats such as codified chains of bits.
Software agents vary and can be classified as:
- expert assistants, software agents assisting users in complex
decision making or knowledge processing, such as medical monitoring,
industrial control, business process administration, manufacturing,
and air traffic control.
- softbots, software agents interacting with real world software
environments, such as operating systems, the Internet, and the
- synthetic agents, software agents operating in simulated worlds,
such as virtual worlds, MUDS, or video games. Emphasis is given
on qualities such as credibility and personality, instead of intelligence
and specialization, and can play roles in interactive entertainment
systems, art and education.
Autonomous agents are studied as either single or multiple agents.
Autonomous Robotic Multi-Agents
A significant amount of research on multi-agent systems exists.
An important early work is. Fukuda's CEBOT system which demonstrates
the self-organizing behavior of a group of heterogeneous robotic
agents. Beni and Hackwood's research on swarm robotics demonstrates
large scale cooperation in simulation. Work at MIT, by Brooks and
Mataric, shows the development of subsumption-based multi-agent
These systems are characterized by their reactive control nature:
- A decomposition of robotic goals into a collection of primitive
Behaviors are either activated via arbitration, or permit concurrent
- Perceptual strategies are closely associated with each reactive
behavior, providing only the information that is necessary for
each activity; and global world models are generally avoided at
this level, yielding faster real-time robotic response.
Many systems have been developed which include multiple identical
units to carry out tasks of foraging, grazing, and consuming objects
in a cluttered world:
- Foraging consists of searching the environment for objects (referred
to as attractors) and carrying them back to a central location.
Robots performing this task would potentially be suitable for
garbage collection or specimen collection in a hazardous environment.
- Grazing is similar to lawn mowing; robot team must adequately
cover the environment. Grazing robots might be used to mow, plow
or seed fields, vacuum houses, for surveillance, or to remove
scrub in a lumber producing forest.
- Consuming requires the robot to perform work on the attractors
in place, rather than carrying them back. Applications might include
toxic waste cleanup, assembly, or cleaning tasks.
Communication mechanisms include:
- State communication enhances the performance of the social system
in quantifiable ways. When state communication is permitted, robots
are able to detect the internal state (wander, acquire, or deliver)
of other robots in a manner analogous to the display behavior
- Goal communication involves the transmission and reception of
specific goal-oriented information. Implementation on mobile robots
requires data to be encoded, transmitted, received, and decoded.
- Emergent behavior is evidenced as the phenomena of recruitment,
the shared effort of many robots to perform a task, which occurs
even in the absence of communication between the agents.
Little research has been conducted on multi-agent systems based
on biological studies and even less so on systems that have been
fielded on working robots. These aspects are an important concern
in the research project entitled Ecological
Robotics: A Schema-theoretic Approach.
Autonomous Robot Architecture (AuRA)
In the Autonomous Robot Architecture (AuRA) developed at College
of Computing's Mobile Robot Laboratory at Georgia Tech, the overarching
hybrid deliberative/reactive architecture employed, motor schemas
provide the reactive component of navigation. Instead of planning
by predetermining an exact route through the world and then trying
to coerce the robot to follow it, motor schemas (behaviors) are
selected and instantiated in a manner that enables the robot to
interact successfully with unexpected events while still striving
to satisfy its higher level goals. Motor schema outputs are, in
a sense, analogous to potential fields. Multiple active schemas
are usually present, each producing a velocity vector driving the
robot in response to its perceptual stimulus. The robot only needs
to compute the single vector at its current location. Each of the
individual schemas posts its contribution to the robot's motion
at a centralized location. The resultant vectors are summed and
normalized to fit within the limits of the robot vehicle, yielding
a single combined velocity for the robot. These vectors are continually
updated as new perceptual information arrives, with the result being
immediate response to any new sensory data.
The advantages of this form of navigation are many. They include
rapid computation and the ability to be mapped onto parallel architectures
making real-time response easily attainable. Modular construction
affords ease of integration of new motor behaviors simplifying both
system maintenance and the ease of transfer to new problem domains.
Motor schemas readily reflect uncertainty in perception, when such
a measure is available, and also react immediately to environmental
sensor data. These factors all contribute to the needs of a navigational
system that will successfully assist a robot's intentional goals.
MissionLab is designed to support such a system in a simulated environment.