Graduation date: 2001Current robots are no match for biological organisms when adapting to real-world,\ud dynamic environments. Collective control strategies, such as those used by\ud synergistic biological systems composed of large numbers of identical parts like the\ud human nervous system, provide a novel and alternative approach for the design of fault-tolerant,\ud adaptable robotic systems that have traditionally relied on centralized control.\ud In this research, a robotic arm composed of multiple identical segments in a\ud collective computational architecture was tested for its ability to produce adaptive\ud pointing and reaching behavior. The movement rules for these robotic arm segments\ud were based on the concepts of the "reflex arc" and the "action system" in the human\ud nervous system.\ud Robotic arms of three to seven encapsulated segments were tested. These arms\ud received no central directions and used no direct informational exchange. The arms were\ud sensor-driven at their distal, or leading, outstretched ends to maximize pointing accuracy\ud on a two-dimensional target plane. The remaining non-distal segments in the arms were\ud moved in a sequential order using sensed locally-available movement information about\ud neighboring segments.\ud Successful pointing and reaching behavior was observed in situations with and\ud without movement obstacles. This led to the conclusion that because such behavior was\ud not specified within each segment, the overall arm behavior emerged due to the\ud interaction and coordination of all segments, rather than due to any single segment,\ud centrally-controlled influence, or explicit inter-segmental method of communication
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