外文翻译--轮式挖掘装载机自动控制(编辑修改稿)

外文翻译--轮式挖掘装载机自动控制(编辑修改稿)内容摘要：

alf of the paper details the control structure of the ADCS, while the remaining sections present data from field tests. These show that the performance of the automated system is parable to that of an expert human operator over a wide range of excavation sites. Overview of related automated digging control work The many potential applications for automated earth moving systems has attracted a significant amount of research in this area. Typically, research has fallen into two major areas: digging process modeling and planning, and automated digging. A prehensive summary of the current research in the field is given in Singh. This section concentrates on work related to the automated digging direction. In general, the simple trajectory planning and control approach is not effective, therefore several researchers measure forces during digging which are used to adjust the digging trajectory. Bullock and Huang use these forces to initiate digging trajectory actions when 4 fixed force thresholds are met. These techniques are not effective and often do not fill the bucket in a wide variety of excavation situations. Alternatively, other researchers have selected digging control actions using a set of control rules. The automated excavator (LUCIE) from the University of Lancaster is an example of this approach. This excavator plans and attempts to follow an initial digging trajectory, then uses the rule set to react to excavation conditions. A fuzzy logic controller has been developed by Sameshima et al . which controls the actuation of each degree of freedom relative to bucket motion during the digging process. Thus the fuzzy rules are evaluated at each control cycle and joint velocity mands are the weighted output of the rules. The Autodig approach used by Rocke uses the actual forces from hydraulic cylinder measurements. These forces are then related to forces inferred from bucket velocities. Commands for each degree of freedom for the bucket are generated from a lookup table based on how human operators control individual joints when digging in various soil conditions. Here, the soil condition must be provided to the system before digging and the material must remain relatively homogeneous for effective digging. Unexpected inclusions in the soil can be a problem for this system to deal with. Cannon implements an augmented Autodig algorithm for dig execution in their Autonomous Loading System (ALS) which pletely automates the task of loading trucks with a mass excavator. Another Autodig approach by Shull also uses actual forces measured from。