外文翻譯--輪式挖掘裝載機自動控制-文庫吧
2025-04-17 05:49 本頁面
【正文】 neral, 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。bucket. The criteria listed above were used for the designing ADCS. 3 Fig. 1. The Caterpillar 980G Wheel Loader Test Platform In this paper, we show how the CARE approach has been used to develop the prototype Automated Digging Control System on the Caterpillar 980G. The ADCS utilizes only existing production sensors and actuators and has only modest putational needs. The first h
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