Analysis of load displacement in grape harvesters and corresponding effect on dynamic weighing system under laboratory conditions

Harvester bin dynamic weighing systems are affected by a number of sources of variation such as field slopes and load displacement. In grape harvesters, the nature of the material (wine grapes and wine grape juice) and its relative composition can vary significantly. Laboratory tests were carried out using hydrogel spheres and water to simulate field dynamic conditions during harvest. This paper quantifies the sources of variation, submitting an instrumented grape harvester to graduated inclination under shaking conditions. Load displacement is characterized using image analysis from recorded movies on four different pitch axis motions of the machine: front to horizontal, horizontal to rear, rear to horizontal and horizontal to front. Differences in the displacement of the load in relation to the machine inclination and to the load composition have been addressed

Safety functional requirements for “Robot Fleets for Highly effective Agriculture and Forestry Management”

This paper summarizes the steps to be followed in order to achieve a safety verified design of RHEA robots units. It provides a detailed description of current international standards as well as scientific literature related to safety analysis and fault detection and isolation. A large committee of partners has been involved in this paper, which may be considered as a technical committee for the revision of the progress of safety development throughout the progress of RHEA project. Partners related to agricultural machinery, automation, and application development declare the interest of providing a stable framework for bringing the safety verification level required to be able to commercial unmanned vehicles such as those described in the RHEA flee

A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part II:Selection of relevant input variables for identification of material other than grain (MOG) content in the grain bin

The cleaning process on a combine harvester is a complex process that is influenced by a wide range of parameters such as machine settings, field and crop-related parameters, etc. Because of the high time pressures combine drivers have to deal with, optimal settings for the cleaning section are usually only estimated once for each crop. As a consequence, differences in temporal and site-specific conditions are neglected. No recent literature is available that considers the interaction between the settings of the cleaning section (like e.g. fan speed, lower sieve opening and upper sieve opening) and the material other than grain (MOG) content in the grain bin, which is, however, an important performance parameter of the cleaning shoe. In this study, a combine harvester was equipped with extra sensors that could contain valuable information necessary to predict the performance of the cleaning section. A nonlinear genetic polynomial regression technique was used to rank the pool of potential sensors as possible regression variables for a prediction model of the MOG content in the grain bin. This model is important for the automation of the cleaning shoe. Results showed that the MOG content in the grain bin is influenced non-linearly by differences in the amount of biomass on the sieve section and the fan speed, which are also correlated with each other.status: publishe

Aansturen van de luchtverdeling over de reiniging van een maaidorser in closed loop. /

Master of Science in de industriële wetenschappen: elektrotechniek - Campus Kortrij