Guided genetic evolution: A framework for the evolution of autonomous robotic controllers.

摘要

The development of autonomous robotic agents capable of complex navigation, control and planning has always been an intriguing area of research. The benefits associated with the successful implementation of such systems are enormous. However, the creation of robotic controllers for the efficient manipulation of autonomous agents in real-time is a very computationally complex task. Such complexity increases exponentially as the structure of the robot or its surrounding environment increase in sophistication. We propose a new genetic framework labeled Guided Genetic Evolution, or GGE. The guided genetic evolution platform encapsulates a connectionist model, labeled Trigger Networks, for the representation of articulated robotic structures as well as the behavioral capabilities of robotic agents. The evolution of trigger networks is based upon genetic programming methodologies with the inclusion of specialized algorithms for the evolution of articulated robotic controllers. Evolutionary guidance constructs are also introduced as means for minimizing the search space associated with the control problem and achieving successful evolution of agents in a shorter time duration. A simulation environment based on rigid body dynamics is utilized for the functional modeling of system interactions. The simulation environment allows for the utilization of minimal agent representation in order to achieve reliable fitness allowing for the further expansion of the research into the real domain.