ISO 11783 Compliant Forest Crane as a Platform for Automatic Control

Journal of Field Robotics

Lehtola

Visala

2018

Self Cite

Crane 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.

x, y, z

vs. three angles of rotation and one linear displacement position) (Kalmari et al., ; Westerberg & Shiriaev, ).…”

Section: Introductionmentioning

confidence: 99%

Forestry crane posture estimation with a two‐dimensional laser scanner

Journal of Field Robotics

Lehtola

Visala

2018

Self Cite

“…On the other hand, the forest machine research platform consists of a Valtra T132 agricultural tractor and a Kesla 305T forestry crane (Kalmari et al, 2013b;Kalmari et al, 2014). It is ISO 11783 compliant platform and it enables joysticks to control the crane.…”

Section: System Setupmentioning

confidence: 99%

“…The model of the forestry crane is obtained from the instrumentation of the forest machine research platform (Kalmari et al, 2013b). Additionally, the method by Kalmari et al (2013a) is used to localize the freely hanging tool.…”

Section: Augmenting Virtual Datamentioning

confidence: 99%

See 1 more Smart Citation

Augmented Reality in Forest Machine Cabin

Palonen¹,

Visala³

2017

IFAC-PapersOnLine

Self Cite

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.

“…The agricultural tractor and the forest crane are designed to work as a research platform for an autonomous forest machine. The forest crane is instrumented for autonomous operation [9]. The prototype point cleaning tool is depicted in Fig.…”

Section: Hardware and Environmentmentioning

confidence: 99%

Real-time detection of young spruce using color and texture features on an autonomous forest machine

The 2013 International Joint Conference on Neural Networks (IJCNN)

Kalmari

Visala

2013

Self Cite

Abstract-Forest machines are manually operated machines that are efficient when operated by a professional. Point cleaning is a silvicultural task in which weeds are removed around a young spruce tree. To automate point cleaning, machine vision methods are used for identifying spruce trees. A texture analysis method based on the Radon and wavelet transforms is implemented for the task. Real-time GPU implementation of algorithms is programmed using CUDA framework. Compared to a single thread CPU implementation, our GPU implementation is between 18 to 80 times faster depending on the size of image blocks used. Color information is used in addition of texture and a location estimate of the tree is extracted from the detection result. The developed spruce detection system is used as a part of an autonomous point cleaning machine. To control the system, an integrated user interface is presented. It allows the operator to control, monitor and train the system online.