Experimental robotic excavation with fuzzy logic and neural networks
Experimental robotic excavation with fuzzy logic and neural networks
复制标题
模糊逻辑和神经网络的实验机器人挖掘
DOI：
10.1109/robot.1996.503896
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发表时间：
1996
期刊：
影响因子：
--
通讯作者：
Fei
中科院分区：
文献类型：
--
作者：
Xiaobo Shi;P. Lever;Fei
本文介绍了基于模糊逻辑和神经网络的自主岩石挖掘机器人的实验结果。挖掘目标被分解为几个任务，而获取是通过执行适当的挖掘行为来完成的。最后，行为由一系列原始的机器执行挖掘动作来执行。挖掘目标、任务和行为是使用有限状态机（FSM）来指定的，该机器基于挖掘启发式和熟练的人类操作员的专业知识。FSMs中的决策是使用神经网络来实现的，该神经网络能够从先前的任务执行中改进其性能。挖掘动作是根据人类经验和启发式获得的模糊逻辑规则给出的。实验结果表明，该系统能够有效地完成所需的挖掘任务。
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.