Experimental robotic excavation with fuzzy logic and neural networks

Abstract

This paper describes experimental results for autonomous robotic rock excavation with fuzzy logic and neural networks. An excavation goal is decomposed into several tasks, whereas a take is accomplished by executing appropriate excavation behaviors. Finally, a behavior is carried out by a sequence of primitive, machine executing excavation actions. Excavation goals, tasks, and behaviors are specified using finite state machines (FSM) based on excavation heuristics and expertise from skilled human operators. The decision making in the FSMs are implemented using neural networks which are capable of improving their performance from previous task executions. Excavation actions are given using fuzzy logic rules acquired from human experience and heuristics. Several experiments are presented that demonstrate the system's ability to complete required excavation tasks effectively.