外文翻譯--輪式挖掘裝載機(jī)自動(dòng)控制-文庫(kù)吧資料
2025-05-22 05:49本頁(yè)面
【正文】 oes not get tired or lose concentration. Second, improve machine availability because the controller will always operate the machine within design limits during digging. Third, reduced wheel slippage during digging. However, to achieve these benefits and also operate effectively in the harsh excavation environment, it is important that the design of an automated system meets the following criteria. The sensors and actuators used should be limited to those currently available on a modern loading machine. For a wheel loader this includes electrohydraulic actuation of 2 bucket motions, bucket position sensors and measurement of a limited number of drive train parameters. Complex sensing and actuation systems may be prone to failure in the harsh environment. Next, the system should require no input from the operator related to characterizing digging difficulty. This would require 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
畢業(yè)設(shè)計(jì)相關(guān)推薦
鄂ICP備17016276號(hào)-1