A framework to develop and test a model-free motion control system for a forestry crane

This article has the objective of presenting our method to develop and test a motion control system for a heavy-duty hydraulically actuated manipulator, which is part of a newly developed prototype featuring a fully-autonomous unmanned forestry machine. This control algorithm is based on functional analysis and differential algebra, under the concepts of a new type of approach known as model-free intelligent PID control (iPID). As it can be unsafe to test this form of control directly on real hardware, our main contribution is to introduce a framework for developing and testing control software. This framework incorporates a desktop-size mockup crane equipped with comparable hardware as the real one, which we design and manufactured using 3D-printing. This downscaled mechatronic system allows to safely test the implementation of control software in real-time hardware directly on our desks, prior to the actual testing on the real machine. The results demonstrate that this development framework is useful to safely test control software for heavy-duty systems, and it helped us present the first experiments with the world's first unmanned forestry machine capable of performing fully autonomous forestry tasks

Comparison of Alternative Pulpwood Inventory Strategies and Machine Systems at a Log-Yard Using Simulations

The rising throughput of log-yards imposes new constraints on existing equipment and increases the complexity of delivering an optimal and uninterrupted supply of pulpwood to pulp mills. To find ways of addressing these problems by reducing log cycle times, this work uses a discrete-event mathematics model to simulate operations at a log-yard and study the impact of three different log-yard inventory strategies and two alternative machine systems for log transportation between main log-yard and buffer storage. The yard's existing inventory strategy of last load in and first out limits access to older logs at the main storage site. By allocating space for 89,000 m(3) and 99,000 m(3) of pulpwood at the buffer storage it is possible to keep the log cycle time at the main storage to a maximum of 12 and 6 months. Additionally, the use of an alternative log transportation machine system comprising a material handler with a trailer increased the work time capacity utilization relative to the yard's current machine system of two shuttle trucks and a material handler for transporting logs between the main and buffer storage areas. Compared to the currently-used last in first out inventory strategy and purposely emptying the main storage area once or twice per year did reduce the total work time of both machine systems by 14% and 30%. Consequently, the volume delivered from the buffer to the log-yard decreased on average by 17% and 37% when emptying the main storage area once and twice per year. Even with reduced work time when emptying the main storage area, both machine systems could fulfil given work load for transporting logs from the buffer storage to the main log-yard without interrupting operations of the log-yard

Drivers of Advances in Mechanized Timber Harvesting – a Selective Review of Technological Innovation

Timber harvesting operations vary greatly around the world, as do the adaptations of technology to the complex, locally variable conditions. Similarly, technological innovations occur as a response to a large number of different situations. This review examines the three main drivers considered to generate substantial technological change in mechanized timber harvesting: 1) availability of new technology, 2) demand for new products and 3) introduction of new regulations. The main focus is on Nordic cut-to-length harvesting using a harvester and forwarder, partly due to its advanced level of technology and partly due to the authors’ backgrounds. Examining new technology, progress towards increased automation is highlighted with examples of entry-level products that provide computer-assisted motion control and semiautomation. Examples of unmanned machines and other high-level automation are also presented. Innovations in the field of bioenergy harvesting are presented as examples of advances addressing the demand for new products. Thus, illustrations span from harvesting of tree parts other than stemwood, to how such harvesting and transportation can be integrated into the traditional stemwood harvest. The impact of new regulations on technological innovation is demonstrated with advances aimed at reducing soil damage. Examples range from technical solutions for reducing soil pressure, to walking, flying and even climbing machines. Some predictions are given as to when certain advances can be expected to become reality. However, even though the main drivers are likely to change timber harvesting with new products and new rules, they will probably do so through a continued adaptation of technology to local needs

A study case of Dynamic Motion Primitives as a motion planning method to automate the work of forestry cranes

Dynamic motion primitives (DMPs) is a motion planning method based on the concept of teaching a robot how to move based on human demonstration. To this end, DMPs use a machine learning framework that tunes stable non-linear differential equations according to data sets from demonstrated motions. Consequently, the numerical solution of these differential equations represent the desired motions. The purpose of this article is to present the steps to apply the DMPs framework and analyse its application for automating motions of forestry cranes. Our study considers an example of a forwarder crane that has been equipped with sensors to record motion data while performing standard work in the forest with expert operators. The objective of our motion planner is to automatically retract the logs back into the machine once the operator has grabbed them manually using joysticks. The results show that the final motion planner has the ability of reproducing the demonstrated action with above 95% accuracy. In addition, it has also the versatility to plan motions and perform similar action from other positions around the workspace, different than the ones used during the training stage. Thus, this initial study concludes that DMPs gives the means to develop a new generation of dynamic motion planners for forestry cranes that readily allow merging the operator?s experience in the development process

What Do We Observe When We Equip a Forestry Crane with Motion Sensors?

Forestry machines have the power to efficiently move very heavy loads, but they are not very smart at communicating information, especially information regarding motion. Understanding how a system produces motion is one of the main stepping stones towards the world of automation. However, to acquire motion data requires sensor hardware that is not largely available in forestry machines today. As a result, at the moment there is no motion data analysis for forestry machines. Therefore, the objective of this article is to present this data, and discuss how we can use such data in regards to technology development. To this end, we have equipped a commercial forestry machine with state-of-the-art sensors and a data acquisition unit. Our aim is to understand what possibilities exist for automation, when we analyze how machine operators control forestry cranes. Among our objectives is to show how motion data can: a) give a better comprehension of the way forestry operators control cranes, b) be useful to analyze crane motion patterns, and c) show additional information that can be estimated via mathematical algorithms. The topics we cover only touch the surface of future applications, where sensor data analysis will be able to team up with other technologies to improve operator’s work, including automation, decision making, motion optimization, and operators’ training, just to mention some

Evaluation of Bayesian Networks in Participatory Water Resources Management, Upper Guadiana Basin, Spain

Stakeholder participation is becoming increasingly important in water resources management. In participatory processes, stakeholders contribute by putting forward their own perspective, and they benefit by enhancing their understanding of the factors involved in decision making. A diversity of modeling tools can be used to facilitate participatory processes. Bayesian networks are well suited to this task for a variety of reasons, including their ability to structure discussions and visual appeal. This research focuses on developing and testing a set of evaluation criteria for public participation. The advantages and limitations of these criteria are discussed in the light of a specific participatory modeling initiative. Modeling work was conducted in the Upper Guadiana Basin in central Spain, where uncontrolled groundwater extraction is responsible for wetland degradation and conflicts between farmers, water authorities, and environmentalists. Finding adequate solutions to the problem is urgent because the implementation of the EU Water Framework Directive requires all aquatic ecosystems to be in a “good ecological state” within a relatively short time frame. Stakeholder evaluation highlights the potential of Bayesian networks to support public participation processes

Solid – Liquid separation of dairy manure: distribution of components and methane production

Chemical treatment and screening can be an effective technique for separation of dairy cattle manure into a liquid fraction (LF) and a nutrient-rich solid fraction (SF). The optimum loading of a strong cationic polyacrylamide was found to be 43.9 g kg−1 of dry excreta. The separated SF contained 29.1% of the initial mass present in the manure and the chemicals added. The Volatile Solids (VS)/Total Solids (TS) ratio, which was 0.78 for the manure, rose to 0.82 for the SF and decreased to 0.63 in the LF. Furthermore, the SF retained 76.1, 79.9, 59.4 and 87.4% of TS, VS, Total Kjeldahl Nitrogen and Total Phosphorus, respectively. In the LF, the ratio of filtrate chemical oxygen demand (CODfiltrate) and COD due to volatile fatty acids (CODVFA) in relation to total COD (CODT) were 0.86 and 0.76, respectively. The percentage of anaerobically biodegradable chemical oxygen demand (CODBD) for the LF was 83.0%. Treatment of the LF in high loading anaerobic reactors would be possible due to these COD characteristics. Specific methane production in terms of VS for the separated LF was 0.580 m3 kg−1. For dairy manure and SF, it was 0.320 and 0.258 m3 kg−1, respectivel