EN:

This study proposes a method for combining regression equations using a relevance network model, a weight generating function, and a generalized mixed operator. The combination of these methods puts a relative weight on the predictions of each of the individual equations and then calculates a weighted average estimate. The method was validated using computer simulation structured within the Statistical Analysis System (SAS). The simulation tests demonstrated that the method is capable of making a prediction that is not significantly different from the true prediction provided the input values for the combined model fall within the valid range of at least one variable. The mean difference between the predictions using the proposed method and the prediction from the true models was less than 9.5 percent of the true model predictions for the complete set of randomized simulations. Prediction accuracy can be improved by increasing the number of variables in an equation and by broadening the width of the variable valid interval, but not necessarily by increasing the number of equations in an equation set. Individually, the number of variables is more influential than variable valid interval width on prediction accuracy.

EN:

Cable yarding has been used for many years in mountainous forests in central European countries. Tower yarders are common cable yarding systems in Austria. The goal of this study was to develop a general time prediction model for two kinds of tower yarders used in Austria. The multiple regression method was applied. The average production rate was 9.30 m³/PSH0 with a cost of US$25.48/m³. The results also showed that the production rate for downhill yarding was less than uphill yarding using the Syncrofalke tower yarder. The developed time production models can help forest engineers estimate production of tower yarders in similar logging operations.

EN:

The Swedish forestry industry competes on an international market; because raw material is more expensive than in other parts of the world, the chain from the stump to the industry needs to be very effective. One part in this chain is cutting and transporting trees from the forest to the landing area for further transportation with trucks to the paper or saw mill. When cutting and transporting trees, forestry machines equipped with booms are used to handle the trees. If boom handling time can be reduced thereby increasing productivity by 10 percent, the Swedish forestry industry can earn up to 250 million Swedish crowns (US$35 million) per year.

One way to decrease boom handling time is to introduce automatization. This paper describes how to solve the kinematic control of knuckle booms used on forestry machines when automatization is introduced. The objective was to develop a kinematic control strategy for maximum lifting capacity,which is suited for computer-controlled knuckle booms that are redundant. This strategy was analyzed with respect to time consumption when the manipulator tip moves along a predetermined path. The analysis was conducted on a knuckle boom used on a forwarder in a forestry application. The knuckle boom had one redundant degree of freedom. The analysis showed the necessary joint speed requirements and time consumption for certain motion cycles and also what happens when the joints reach their maximum velocity limits.

EN:

The feasibility of using autonomous forest vehicles (which can be regarded as logical developments in the ongoing automation of forest machines),the systems that could be applied in them, their potential advantages and limitations (in the foreseeable future) are considered in this paper. The goals were to analyze: 1) the factors influencing the degree of automation in logging; 2) the technical principles that can be applied to autonomous forest machines, and 3) the feasibility of developing an autonomous path-tracking forest vehicle. A type of vehicle that is believed to have considerable commercial potential is an autonomous forwarder. The degree of automation is influenced by increased productivity, the machine operator as a bottle-neck, cost reduction, and environmental aspects. Technical principles that can be applied to autonomous forest vehicles are satellite navigation,wheel odometry,laser scanner,and radar.A new path-tracking algorithm has been developed to reduce deviations from the desired path by utilizing the driver’s steering commands. The presented system has demonstrated both possibilities and difficulties associated with autonomous forest machines. A field study has shown that it is quite possible for them to learn and track a path previously demonstrated by an operator with an accuracy of 0.1 m on flat ground and also to detect and avoid unexpected obstacles. Although the forest machine safely avoids obstacles,the study shows that further research in the field of obstacle avoidance is needed to optimize performance and ensure safe operation in a real forest environment.

EN:

In this study, three different processing options for trees yarded whole at the roadside in a beech thinning operation, typical of the Italian Apennine mountain, were studied. Trees were delimbed, crosscut, and stacked, respectively, by a four-man crew equipped with chainsaws and a hydraulic loader (motor-manual control thesis),by a small stroke harvester head mounted on a light excavator, and by a dedicated 6-wheel harvester. Under the conditions of the study, mechanized processing was less expensive than the motor-manual control thesis, regardless of the specific option. Cost reductions amounted to 27 percent and 38 percent, respectively, for the light processor and the heavy harvester. Annual usage is a crucial factor for the introduction of industrial mechanization: the heavy harvester is preferable to motor-manual processing only when the annual output exceeds 5,000 metric tonnes (t) per year. When this figure grows above 13,000 tonnes per year, it will profitably replace the light processor, not just for monetary gain, but for the inability of the lighter unit to cope with such a heavy workload. On the other hand,the light processor was always less expensive than the motor-manual control, while requiring an additional investment of only (US)$47,000. Therefore, the acquisition of a light processor represents the most viable option, at least for immediate deployment. Its productivity closely matches that of the yarder, allowing for hot-deck (synchronic) operation. All of the options can efficiently process beech trees within the full range of diameters normally obtained from thinning operations, and up to a 30 cm diameter at breast height. As expected, productivity increases with tree size, and even more so for the mechanical units, which normally handle just one or a few trees at a time. Under the conditions of this study, both mechanized options have a potential for bringing processing cost near (US)$10 per tonne, which is half the cost of traditional motor-manual processing.

EN:

The GPSTRACK project has arisen as a result of a recommendation in the Forest Industry Transport Group (FITG) Code of Practice for Timber Haulage, which was to “Encourage closer co-operation between consignors and hauliers to plan routes in a manner which optimizes the economic returns within a legal framework.”The project involved the installation of Bluetree global positioning systems (GPS) asset tracking systems onto two timber haulage trucks: an articulated Iveco Stralis 530 6*2 tractor unit with tri-axle road friendly air suspension flatbed trailer with a design gross vehicle weight (dgvw) equal to 44 t and a Scania 124 (400) with a rigid (3 axle) + trailer (3 axle) + crane combination with an equivalent dgvw of 44 t.This paper discusses the background and use of real-time asset tracking devices in the context of timber haulage in Ireland. Real-time location information is a relatively new concept for Irish applications (less than 5 years), but there is an increasing deployment of the technology into the truck transport sector in Ireland. The goal of this study was to test the accuracy of the recorded GPS locations relative to the underlying travelled route network based on the criteria of: 1) a fixed GPS receiver location, 2) a truck travelling on public routes, and 3) comparing accuracy of public routes to the accuracy of the truck travelling in a more demanding environment such as the internal forest road network. The results analysis calculated the horizontal root mean square (HRMS) 63 percent GPS accuracy of both trucks tracklog on both the public road network and the internal forest road network over a period of 4 weeks which totalled approximately 15,000 GPS data points. The HRMS accuracy values ranged from 2.55 to 2.47 m for the public roads, while the forest road accuracy were approximately 27 m and 41 m for Iveco and Scania, respectively.