Biologically-Inspired Collective Control for an Autonomous Robotic Arm

Abstract

Biological collective control architectures and simple control principles used in nervous systems provide novel alternative approaches for the design of fault-tolerant, adaptable real-world robotic systems that have traditionally relied on centralized control. In this research, a robotic arm composed of multiple identical segments in a collective computational architecture was tested for its ability to produce adaptive pointing and reaching behavior. The movement rules for these robotic arm segments were derived from “reflex arc” principles in the human nervous system. These arm segments received no central directions and used no direct informational exchange, but rather the arm was sensor-driven at its leading segment in a way that maximized pointing accuracy of the arm. The remaining non-leading segments in the arm were moved in a sequential order using only sensed locally-available movement information about neighboring segments.

Pointing and reaching behavior was observed in experiments with and without obstacles to movement. Because such behavior was not specified within each segment, the overall limb behavior emerged due to the interaction and coordination of all segments, rather than due to any single segment, centrally controlled influence, or explicit inter-segmental method of communication.