【正文】 equire operators to make a judgement concerning digging difficulty. In general, the subsurface characteristics of the material to be loaded and its potential interactions with the bucket during digging have the greatest effect on digging difficulty. Human operators cannot see below the surface. Thus, with no operator input the automated system must be able to adjust its digging trajectory by reacting to perceived changes in digging conditions. Automatic digging control of loading machines is particularly difficult because they operate in dynamic and unstructured environments where conditions are unknown, extremely variable and difficult to detect. On the other hand, expert human operators can achieve sophisticated control of loading machines in these difficult environments. Repeated excavation experiences help the operator to learn machine operational skills and how to adapt their operating modes to the dynamic conditions. The plexities of the interactions between the excavation machine and its environment make it impractical or infeasible to develop mathematical models typically used in traditional control paradigms. Therefore, researchers at the University of Arizona have been developing an excavation control system that utilizes excavation knowledge gathered from skilled human operators. The Control Architecture for Robotic Excavation (CARE) is a hybrid architecture that employs a behaviorbased control structure. It has reactive control at the lowest level to generate primitive bucket actions, and task planning using finite state machines (FSM) that capture excavation knowledge required for behavior arbitration. Fuzzy logic bined with behaviorbased control provide the excavation controller with the realtime reactive response necessary for digging task execution in an uncertain and dynamic environment Several years ago, the University of Arizona researchers started a project funded by Caterpillar Inc. to use CARE as the basis to develop, implement and test an Automated Digging Control System (ADCS) on a wheel loader. The implementation platform for the prototype ADCS was a Caterpillar 980G wheel loader (see Figure 1). This wheel loader weighs 29,497 kg, is m long, m high and has a m179。試驗結果表明，自動化系統的性能 在 廣泛 的 開挖情況 下與專業(yè)操作員 相媲美。 這些困難的 挖掘 情況下， 專業(yè)操作人員需要的是高 效 的裝載 。第二，控制器能 在設計范圍內的挖掘過程 中 操作這臺機器 來 提高機器的可用性。輪式裝載機鏟斗運動包括電液驅動，斗式位置傳感器和傳動有限數目的參數測量。在一般情況下，材料的表面特性被加載和其 滿斗 挖 掘過程中潛在的相互作用 是 開挖難度最大的影響。 另一方面，人類 操作者 可以在這些艱難的環(huán)境中 對 裝載機實現復雜的控制。機械 挖掘 控制結構 (CARE)是一種混合的體系結構，采用了基于行為 的控制結構。作 為原型的 ADC 的實現平臺是 980G 卡特彼勒輪式裝載機（見圖 1）。