Increasing the Level of Automation in the Forestry Logging Process with Crane Trajectory Planning and Control

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Abstract

Working with forestry machines requires a great deal of training to be sufficiently skilled to operate forestry cranes. In view of this, it would be desirable within the forestry industry to introduce automated motions, such as those seen in robotic arms, to shorten the training time and make the work of the operator easier. Motivated by this fact, we have developed two experimental platforms for testing control systems and motion-planning algorithms in real time. They correspond to a laboratory setup and a commercial version of a hydraulic manipulator used in forwarder machines. The aim of this article is to present the results of this development by providing an overview of our trajectory-planning algorithm and motion-control method, with a subsequent view of the experimental results. For motion control, we design feedback controllers that are able to track reference trajectories based on sensor measurements. Likewise, we provide arguments to design controllers in an open-loop for machines that lack sensing devices. Relying on the tracking efficiency of these controllers, we design time-efficient reference trajectories of motions that correspond to logging tasks. To demonstrate performance, we provide an overview of extensive testing done on these machines.

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