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Technical Paper

Computer Modeling and Simulation of a Tracked Log Skidder with Different Grapple Configurations

A track-type grapple log skidder was dynamically modeled to allow machine modification by computer to determine the effects of these modifications on the operation of the machine in the forest. The model consisted of an undercarriage, power train, log/drag force, and logging equipment (arch and grapple). This skidder had three types of logging attachments: winch, swinging boom (grapple), and single-function arch (grapple). Each was modeled and simulated under various conditions. The dynamic model of the skidder can be used to analyze its drawbar pull capability and lateral stability with various log weights and soil types on steep slopes. Validation of this model is needed later.
Technical Paper

Dynamic Simulation Techniques for Steering of Tracked Agricultural and Forestry Vehicles

A procedure for simulating the dynamics of agricultural and forestry machines using mechanical system simulation software is presented. A soil/track interface model including rubber-track and steel-track was introduced as well as equations that can be used to model mechanical and hydraulic power trains commonly found in tracked vehicles. Two rubber-tracked vehicles (agricultural tractors) and two steel-tracked machines (forestry vehicles) were simulated to illustrate the technique, and some analysis results are presented. The examples given in this paper are based on the author’s research over the past several years.
Technical Paper

Evaluation of Machine Vision Algorithms for Locating Corn Plants

The feasibility study of using machine vision technology to locate corn plants was conducted to determine its potential in the development of an intelligent detasseling machine. A corn plant feature, the main vein of leaf, was used and the method of feature detection was developed for corn plant identification. Experimental results showed that the leaf feature and the center of the plant can be detected and located using image processing techniques when an image is taken from above of a plant. This research showed that it is possible to identify and locate the corn plant using machine vision technology.