Improved operator comfort and off-road capability through pendulum arm technology

Gelin, Olle; Henriksen, Fredrik; Volungholen, Rolf; Björheden, Rolf

doi:10.1016/j.jterra.2020.01.003

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…An interesting improvement on forwarders to reduce rutting has been tested in Sweden by applying a pendulum arm technology to a 6-wheel forwarder. It has been compared with a traditional 8-wheel forwarder; results of rutting measurements were not better than traditional technology, mainly due to the lower number of wheels, but the good potential of this technology was reported [87]. Furthermore, this potential was partially confirmed in another study comparing different forwarders equipped with standard wheels, SFT, rubber tracks, and pendulum arms.…”

Section: Technical Featuresmentioning

confidence: 98%

“…In this sense, several authors have reported a series of recommendations to mitigate impacts on soil physical properties. Many authors agree on the fundamental need to have a correct and rational planning of forest roads and machine operating trails in particular [38,87,96,100]. On the other hand, some authors recommend to reduce the number of trails in order to concentrate impacts on limited surfaces [38], while others suggest to limit the number of passes in a single trail recommending the creation of more trails to distribute soil compaction in the forest [100].…”

Section: Machines Usementioning

confidence: 99%

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Strategies to Mitigate the Effects of Soil Physical Disturbances Caused by Forest Machinery: a Comprehensive Review

et al. 2022

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Purpose of Review Ground-based mechanized forest operations can cause severe soil disturbances that are often long lasting and detrimental to the health of forested ecosystems. To reduce these soil disturbances, focus is being increasingly directed at identifying and using appropriate mitigation techniques. This systematic review considered 104 scientific articles and reported the main findings according to four core themes: terrain-related factors, operational planning, machine modifications, and types of amendments used to mitigate machine-induced soil impacts. Recent Findings For terrain-related factors, most severe disturbances occur on machine operating trails exceeding 20% slope and that soil bulk density and rut depth show greater increases in fine-textured soils. When considering operational planning, trafficability maps proved to be helpful in reducing the frequency and magnitude of soil damages as well as the length of trails needed within harvest sites, especially if they are regularly updated with weather information. Machine modifications, through high flotation tires, use of extra bogie axle, lower inflation pressure, and use of steel flexibles tracks, are highly researched topics because of the considerable upside in terms of machine ground pressure distribution and increased traction. Two main types of amendments emerged to mitigate soil disturbances: brush mats and mulch cover. Brush mats created from harvesting debris can spread the load of a machine to a greater area thereby lowering peak loads transferred to the soil. Brush mats of 15–20 kg m−2 are being recommended for adequate soil protection from harvesting operations. Summary To conclude, we outline recommendations and strategies on the use of soil mitigation techniques within cut-to-length forest operations. New research opportunities are also identified and discussed. Considering single factors causing machine-induced soil disturbances remains important but there is a pressing need for having a multi-disciplinary approach to tackle the complex problems associated with machine/soil/plant interactions.

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Section: Technical Featuresmentioning

confidence: 98%

Section: Machines Usementioning

confidence: 99%

Strategies to Mitigate the Effects of Soil Physical Disturbances Caused by Forest Machinery: a Comprehensive Review

et al. 2022

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“…The model, depicted in Fig. 2, is a slightly modified version of the Xt28 concept forwarder [18], consisting of 52 rigid bodies and 60 constraints. It has a total mass of 16.800 kg, whereof the crane stands for 16 bodies, 20 constraints, and 2.000 kg.…”

Section: Virtual Forwardermentioning

confidence: 99%

Reinforcement Learning Control of a Forestry Crane Manipulator

Andersson¹,

Bodin²,

Lindmark³

et al. 2021

Preprint

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Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation.

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“…These have the potential to reduce the energy consumption and ground damage, yet increase traversability and tip over stability [1]- [5]. The concepts have been a reappearing topic in planetary exploration, military, construction, agriculture, and forestry applications, but not yet reached the maturity of practical use [6]. However, there is reason to believe that the full potential of the vehicles is not being utilized.…”

Section: Introductionmentioning

confidence: 99%

Control of Rough Terrain Vehicles Using Deep Reinforcement Learning

Wiberg

Wallin

Nordfjell

et al. 2022

IEEE Robot. Autom. Lett.

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We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27 • , and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.

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Improved operator comfort and off-road capability through pendulum arm technology

Cited by 6 publications

References 6 publications

Strategies to Mitigate the Effects of Soil Physical Disturbances Caused by Forest Machinery: a Comprehensive Review

Strategies to Mitigate the Effects of Soil Physical Disturbances Caused by Forest Machinery: a Comprehensive Review

Reinforcement Learning Control of a Forestry Crane Manipulator

Control of Rough Terrain Vehicles Using Deep Reinforcement Learning

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