Page 313 - Introduction to AI Robotics
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                                                                                            8
                                                                                               Multi-agents
                                     or characteristics, in describing a team. For the purposes of this chapter,
                                     heterogeneity, control, cooperation, and goals will be used as the dimensions. 117
                               8.2   Heterogeneity


                      HETEROGENEITY  Heterogeneity refers to the degree of similarity between individual robots that
                                     are within a collection. Collections of robots are characterized as being ei-
                     HETEROGENEOUS   ther heterogeneous or homogeneous. Heterogeneous teams have at least two
                             TEAMS   members with different hardware or software capabilities, while in homoge-
                  HOMOGENEOUS TEAMS
                                     neous teams the members are all identical. To make matter more confusing,
                                     members can be homogeneous for one portion of a task by running identi-
                                     cal behaviors, then become heterogeneous if the team members change the
                                     behavioral mix or tasks.

                              8.2.1  Homogeneous teams and swarms

                                     Most multi-agent teams are homogeneous swarms. Each robot is identical,
                                     which simplifies both the manufacturing cost and the programming. The
                                     biological model for these teams are often ants or other insects which have
                                     large numbers of identical members. As such, swarms favor a purely reactive
                                     approach, where each robot operates under the Reactive Paradigm. Insect
                                     swarms have been modeled and mimicked since the 1980’s. The proceedings
                                     of the annual conference on the Simulation of Adaptive Behavior (also called
                                     “From Animals to Animats”) is an excellent starting point.
                                       An example of a successful team of homogeneous robots is Ganymede, Io,
                                     and Callisto fielded by Georgia Tech. These three robots won first place in the
                                     “Pick Up the Trash” event of the 1994 AAAI Mobile Robot Competition, 129
                                     also discussed in Ch. 5. Recall that the objective of that event was to pick up
                                     the most trash (coca-cola cans) and deposit it in a refuse area. The majority
                                     of the entries used a single agent, concentrating on model-based vision for
                                     recognizing trash, cans, and bins and on complex grippers.
                                       The three identical robots entered by Georgia Tech were simple, both phys-
                                     ically and computationally, and are described in detail in a 1995 AI Magazine
                                     article. 19  The robots are shown in Fig. 8.1, and were constructed from an In-
                                     tel 386 PC motherboard mounted on a radio-controlled toy tracked vehicle.
                                     The robots had a miniature wide-angle video camera and framegrabber. The
                                     flapper-style grippers had an IR to indicate when something was in the grip-
                                     per. The robots also had a bump sensor in front for collisions. The robots
                                     were painted fluorescent green.
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