Forestry Crane Research Articles

Combining optimization and dynamic movement primitives for planning energy optimal forestry crane motions

Forestry cranes are an important tool for safe and efficient timber harvesting with forestry machines. However, their complex manual control often led to inefficiencies and excessive energy usage, due to the many joysticks and buttons that must be used in a precise sequence to perform efficient movements. To address this, the industry is increasingly turning to partial automation, making manual control more intuitive for the operator and, consequently, achieving improvements in energy efficiency. This article introduces a novel approach to energy-optimal motion planning that can be used along with a feedback control system to automate crane motions, taking over portions of the operator’s work. Our method combines dynamic movement primitives (DMPs) and an energy-optimization algorithm. DMPs is a machine learning technique for motion planning based on human demonstrations, while the optimization algorithm exploits the crane’s redundancy to find energy-optimal trajectories. Simulation results show that DMPs can replicate human-like controlled motions with a 25% reduction in energy consumption. However, our energy optimization algorithm shows improvements of over 40%, providing substantial energy savings and a promising pathway towards environmentally friendly partially automated machines.

Simulation-Driven Universal Surrogates of Coupled Mechanical Systems: Real-Time Simulation of a Forestry Crane

Abstract Preparing simulation-driven surrogates for a coupled mechanical system can be challenging because the associated mechanical and actuator dynamics demand high-fidelity numerical solutions. Proposed here is a universal hydraulic surrogate (UHS), which can provide solutions to high-fidelity mechanical systems with a universal actuator in a surrogate-assisted monolithic approach. The UHS acts as an alternative to the standard lumped fluid theory by eliminating the hydraulic pressures differential equations. A surrogate-assisted universal actuator uses an approximated model to define hydraulic force in high-fidelity mechanical systems. The approximated force model was developed through training against the dynamics of a one-dimensional (1D) hydraulic cylinder and spring-damper. A covariance matrix adaption evolutionary strategy (CMA-ES) was used as an optimization algorithm to minimize differences between the standard dynamics and UHS approaches at the position and velocity levels. The robustness of resulting UHS was validated to predict the behaviors of the simple four-bar mechanism and the forestry crane. The focus was on numerical accuracy and computational efficiency. The maximum percent normalized root mean square error (PN-RMSE) between the states of the approximated force model and lumped fluid theory were approximately 2.04% and 6.95%, respectively. The proposed method was approximately 52 times faster than the standard lumped fluid theory method. By providing accurate predictions outside the training data, the simulation-driven UHS promises better computational performance leading to real-time simulation solutions for the coupled mechanical systems. The UHS can be applied in simulation, optimization, control, state and parameter estimation, and Artificial Intelligence (AI) implementations for coupled mechanical systems.

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.

Experimental investigation into the state estimation of a forestry crane using the unscented Kalman filter and a multiphysics model

To increase productivity, reduce energy use, and minimize unplanned maintenance, manufacturers of heavy machinery must instrument their products. As explained in the literature, state and parameter estimators can successfully integrate machine sensor signals with simulation results from computational models. This leads to comparable or improved observations even when fewer sensors are being used. This study introduces a state observer based on the unscented Kalman filter for the coupled mechanical and hydraulic systems. The resulting reality-driven simulation procedure is applied to a hydraulically actuated forestry crane that has been instrumented to provide the necessary sensor information. This study analyzes the performance of state observer in four different scenarios and recommends an optimal sensor configuration for the application. Estimation accuracy of observer in the simulation of the mechanics and hydraulics components is evaluated using the percent normalized root mean square error (PN-RMSE) and 95% confidence interval.

Design and Evaluation of Capacitive Smart Transducer for a Forestry Crane Gripper

Stable grasps are essential for robots handling objects. This is especially true for "robotized" large industrial machines as heavy and bulky objects that are unintentionally dropped by the machine can lead to substantial damages and pose a significant safety risk. Consequently, adding a proximity and tactile sensing to such large industrial machinery can help to mitigate this problem. In this paper, we present a sensing system for proximity/tactile sensing in gripper claws of a forestry crane. In order to avoid difficulties with respect to the installation of cables (in particular in retrofitting of existing machinery), the sensors are truly wireless and can be powered using energy harvesting, leading to autarkic, i.e., self-contained, sensors. The sensing elements are connected to a measurement system which transmits the measurement data to the crane automation computer via Bluetooth low energy (BLE) compliant to IEEE 1451.0 (TEDs) specification for eased logical system integration. We demonstrate that the sensor system can be fully integrated in the grasper and that it can withstand the challenging environmental conditions. We present experimental evaluation of detection in various grasping scenarios such as grasping at an angle, corner grasping, improper closure of the gripper and proper grasp for logs of three different sizes. Results indicate the ability to detect and differentiate between good and poor grasping configurations.

Experimental Investigation into the State Estimation of a Forestry Crane Using the Unscented Kalman Filter and a Multiphysics Model

Experimental Investigation into the State Estimation of a Forestry Crane Using the Unscented Kalman Filter and a Multiphysics Model

Latency impact on Quality of Experience in a virtual reality simulator for remote control of machines

In this article, we have investigated a VR simulator of a forestry crane used for loading logs onto a truck. We have mainly studied the Quality of Experience (QoE) aspects that may be relevant for task completion, and whether there are any discomfort related symptoms experienced during the task execution. QoE experiments were designed to capture the general subjective experience of using the simulator, and to study task performance. The focus was to study the effects of latency on the subjective experience, with regards to delays in the crane control interface. Subjective studies were performed with controlled delays added to the display update and hand controller (joystick) signals. The added delays ranged from 0 to 30 ms for the display update, and from 0 to 800 ms for the hand controller. We found a strong effect on latency in the display update and a significant negative effect for 800 ms added delay on latency in the hand controller (in total approx. 880 ms latency including the system delay). The Simulator Sickness Questionnaire (SSQ) gave significantly higher scores after the experiment compared to before the experiment, but a majority of the participants reported experiencing only minor symptoms. Some test subjects ceased the test before finishing due to their symptoms, particularly due to the added latency in the display update.

Imitate and optimize modern control algorithms for forestry cranes by means of artificial neural networks

Modern hydrostatic function drives for agricultural and forestry machines require complex control algorithms. Electric controls offer significant energy and control advantages over the state of the art, such as reduced tendency to oscillate or implementation of a variable power limitation. Therefore, new algorithms are essential for sustainable optimization of future machines. The paper investigates a method to automatically transfer an existing control algorithm to an artificial neural network (ANN), which will be optimized by the Pattern Search algorithm afterwards. The method was applied to a forestry crane with an electro-hydraulic flow-on-demand control. After 41 generations of optimized parameter sets, the ANN control already shows a behavior comparable to the reference control. With this approach it is possible to transfer deterministic algorithms into stochastic algorithms with comparable transfer functions, which can then be optimized using machine learning methods.

Simulation-based comparison between two crane-bunk systems for loading work when considering energy-optimal motion planning

ABSTRACTPerforming work for extended periods of time while using the lowest amount of resources is an important aspect for productivity in many industries. In forestry, the productivity of a forwarder is seen as the volume of material it can extract to a roadside landing in a certain amount of time, where the process of loading and unloading logs represents a large part of the work. During this process, the esnergy consumed by the machine is directly related to the speed of the crane. Thus, increasing productivity implies increasing the operating velocity of cranes. But according to current design of forestry cranes, this conversely leads to an undesired increase in consumption of resources (e.g. fuel). A second method is to alter the machine’s design, such as rotating the log bunk. This article considers both methods through a simulation-based comparison aiming to evaluate the energy consumption of two crane-bunk systems when loading. The first simulation system considers a forestry crane with a fixed log bunk (forwarder-like crane). The second simulation system takes into account a forestry crane and a rotating log bunk (harwarder-like crane). The analysis presented considers the fundamental mathematics required to analyze the dynamics of forestry cranes and the principles required to plan energy-optimal motions. The simulation results show that energy savings of 43% to 61% can be obtained by determining energy-optimal motions and using a harwarder-like crane architecture.

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.

Metamodelling for Design of Mechatronic and Cyber-Physical Systems

The paper presents the issue of metamodeling of Domain-Specific Languages (DSL) for the purpose of designing complex mechatronics systems. Usually, one of the problems during the development of such projects is an interdisciplinary character of the team that is involved in this endeavour. The success of a complex machine project (e.g., Computer Numerically Controlled machine (CNC), loading crane, forestry crane) often depends on a proper communication between team members. The domain-specific modelling languages developed using one of the two approaches discussed in the work, lead to a machine design that can be carried out much more efficiently than with conventional approaches. Within the paper, the Meta-Object Facility (MOF) approach to metamodeling is presented; it is much more prevalent in modern modelling software tools than Graph-Object-Property-Relationship-Role (GOPRR). The main outcome of this work is the first presentation of researchML modelling language that is the result of more than twenty ambitious research and development projects. It is effectively used within new enterprises and leads to improved traceability of the project goals. It enables for fully-featured automatic code generation which is one of the main pillars of the agile management within mechatronic system design projects.

Quality of Experience of hand controller latency in a virtual reality simulator

In this study, we investigate a VR simulator of a forestry crane used for loading logs onto a truck, mainly looking at Quality of Experience (QoE) aspects that may be relevant for task completion, ...

Forestry crane posture estimation with a two‐dimensional laser scanner

AbstractCrane posture estimation is the stepping stone to forest machine automation. Here, we introduce a robust minimal perception solution, that is, one that uses minimal constraints for maximal benefits. Specifically, we introduce a robust particle‐filter‐based method to estimate and track the posture of a flexible hydraulic crane by using only low‐cost equipment, namely, a two‐dimensional (2D) laser scanner, two short magnetically attached metal tubes as targets, and an angle sensor. An important feature of our method is that it incorporates control signals for hydraulic actuators. In contrast to the previous works employing laser scanners, we do not use the full shape of the crane to estimate the crane posture, but, instead, we use only two small targets in the field of view of the laser scanner. Thus, a large share of the range data is useful for other purposes, for example, to map the surrounding environment. We test the proposed method in a challenging forest environment and show that the particle filter is able to estimate the posture of the hydraulic crane efficiently and reliably in the presence of occlusions and obstructions. During our comprehensive testing, the tip position was measured with average errors smaller than 4.3 cm whereas the absolute maximum error was 15 cm.

Quality of Experience for a Virtual Reality simulator

In this study, we investigate a VR simulator of a forestry crane used for loading logs onto a truck, mainly looking at Quality of Experience (QoE) aspects that may be relevant for task completion, but also whether there are any discomfort related symptoms experienced during task execution. The QoE test has been designed to capture both the general subjective experience of using the simulator and to study task completion rate. Moreover, a specific focus has been to study the effects of latency on the subjective experience, with regards both to delays in the crane control interface as well as lag in the visual scene rendering in the head mounted display (HMD). Two larger formal subjective studies have been performed: one with the VR-system as it is and one where we have added controlled delay to the display update and to the joystick signals. The baseline study shows that most people are more or less happy with the VR-system and that it does not have strong effects on any symptoms as listed in the Simulator Sickness Questionnaire (SSQ). In the delay study we found significant effects on Comfort Quality and Immersion Quality for higher Display delay (30 ms), but very small impact of joystick delay. Furthermore, the Display delay had strong influence on the symptoms in the SSQ, and causing test subjects to decide not to continue with the complete experiments. We found that this was especially connected to the longer added Display delays (≥ 20 ms).

Augmented Reality in Forest Machine Cabin

Augmented reality human machine interface is demonstrated in the cabin of a forest machine outdoors for the first time in real time. In this work, we propose a system setup and a real-time capable algorithm to augment the operator’s visual field with measurements from the forest machine and its environment. In the demonstration, an instrumented forestry crane and a lidar are used to model the pose of the crane and its surroundings. In our approach, a camera and an inertial measurement unit are used to estimate the pose of the operator’s head in difficult lighting conditions with the help of planar markers placed on the cabin structures. Using the estimate, a point cloud and a crane model are superimposed on the video feed to form an augmented reality view. Our system is tested to work outdoors using a forest machine research platform in real time with encouraging initial results.

Coordinated motion of a hydraulic forestry crane and a vehicle using nonlinear model predictive control

Forests are a challenging environment for autonomous operations. The automatic driving and control of hydraulic forestry cranes have been studied earlier as separate problems. However, this study focuses on control where the boom and the forest machine are employed in a coordinated manner. Such control could be beneficial in certain tasks, such as forest cleaning operations where small trees are removed. To accomplish the coordinated actions, nonlinear model predictive control (NMPC) is utilized. NMPC is a control strategy based on numerical optimization which minimizes a given objective function. The coordinated control was tested with real hardware consisting of a tractor and a forestry crane. In the tests, the tractor has a target path and the tip of the boom has a target trajectory. These tests correspond to a real world situation where the forest machine has its own driving lines and the tool is used to accomplish a given task. Two different trajectories for the boom tip were tested with the target velocity of the boom tip being 0.5m/s or 1.0m/s. At these velocities, the average tracking error of the tractor ranged from 6.7cm to 36.2cm while the average error of the boom tip varied between 3.7cm and 13.6cm. In a separate test where only the tractor was controlled, its tracking error was 15.4cm.

Input nonlinearity compensation and chattering reduction in a mobile hydraulic forestry crane

We present a sliding-mode-based control design for a telescopic link of a mobile-hydraulic forestry crane under bounded modeling uncertainties and external disturbances. Mobile hydraulic systems are typically subject to strong perturbation conditions and the design of resilient control solutions is an important challenge. Furthermore, nonlinear phenomena primarily, characterized by easily excited oscillations, an input nonlinearity, and friction, are dominating the dynamics. The proposed control scheme takes advantage of an input-nonlinearity compensation in order to overcome these problems and includes the formulation of a sliding-mode-control-based design. Two strategies for chattering attenuation are examined aimed at improving the controller performance. Experimental results performed over an industrial setup, including a comparison with a PID controller, confirm the efficacy of the proposed methodology.

Productivity and woodchip quality of different chippers during poplar plantation harvesting

In this work, the productivity and work quality of different types of chipping machines used for biomass comminution produced by dedicated plantations were evaluated. Drum and disc chippers with different powers were compared with feller-chippers and grinders. Machines were tested using only one tree species (poplar) and two different feedstocks: branchwood (seven-year-old treetops and biomass produced by a vSRC) and whole-trees (materials produced by an SRC). This study showed a similar performance for all types of machines tested in terms of working rate using different feeding systems, i.e., automatic and forestry crane. However, different results were obtained for woodchip quality. The whole tree comminution was able to guarantee the best woodchips, and chippers produced better wood chips in comparison to grinders. The results obtained indicate that productivity is linked to engine power and that feedstock size can influence wood chip quality. Furthermore, feller-chippers are able to guarantee the same productivity and wood chip quality as “conventional” chippers.

Nonlinear model predictive control of hydraulic forestry crane with automatic sway damping

Forest machines are used in many tasks and come in various designs. They can be used for cutting down trees, collecting logs and in different forest cleaning operations. Currently, in the commercial machines the level of automation is still relatively low, and they require a professional operator for good work efficiency. Nonlinear model predictive control (NMPC) is an optimal control strategy based on a dynamic model of the system. NMPC algorithms require quite a lot of computational power, but are becoming a more viable option as the performance of computers has increased. We demonstrate how an NMPC can be used for controlling a hydraulic forestry crane that has a freely hanging tool or processing head attached. The goal of the control is to follow a predefined path while simultaneously damping the undesired oscillations of the tool. Three different reference paths with velocities of 0.5m/s to 1.0m/s are tested. The average tracking error in these tests is between 0.02m and 0.11m. Anti-sway control can reduce the amplitude of sideways oscillations between 2% and 64% and longitudinal oscillations between 59% and 76%. The impact of anti-sway control on the tracking accuracy or the velocity is negligible.

Path-Constrained Motion Analysis: An Algorithm to Understand Human Performance on Hydraulic Manipulators

We propose a novel method to analyze how human operators use hydraulic manipulators of heavy-duty equipment. The approach is novel in the sense that it applies knowledge of motion planning and optimization techniques used in robotics. As an example, we consider the case of operating a forestry crane. To that end, we use motion data that has been recorded during standard operation with the help of sensors and a data acquisition unit. The data backs up the notion that operators work by performing repeatable patterns observed in the trajectories of the manipulator's joints. We show how this nominal behavior is computed, and consequently, this allows us to present the following: 1) an analytical procedure to analyze motions, 2) how to represent the “performance” of the operator in a 2-D plot, 3) an example of how to use this information to suggest customized control settings, and 4) some complementary ideas needed for improving efficiency through automation.