Overview

WP 1: Describing advantageous and disadvantageous work patterns and development of algorithms to detect them
Short name of participant: Skogforsk, Skogkurs, NIBIO, WH-NRW, UGoe
This work package aims to develop algorithms to automatically detect phases in the highly cyclical work of both harvester and forwarder work. Identifying work phases is the basis for further analysis of operator performance and suggestion for improvement. How to efficiently use the machines as well as common mistakes, bad practices and fixes are extracted from machine instructors and literature review. Good and bad practices in different phases are quantified in terms of information that can be available in the machines. Algorithms to detect these practices are developed.

WP 2: Quantifying the operational environment, machine positioning and virtualization
Short name of participant: NIBIO, UGoe, Skogforsk, Optea
Task 2.1 will deal with the development of methods for capturing and describing environmental data including the stand, ground vegetation and terrain, and the visualization of this data. Methods and technologies could include using 2D laser scanning to assess standing tree diameters and position, acoustic sensors and stereo photogrammetry for depth and obstacle detection. Accurate merging of data with LiDAR based forest and terrain models (<1m). Methods for structuring the data for visualisation (WP 5) to be developed.
Task 2.2. will work on methods for analysing how the operator balances machine movement with crane work in support of WP 1. These include improving estimates of machine position, machine heading, crane angle and crane extension. Methods for doing that include enhanced GPS, radar, cameras, and proximity sensors. Tree proximity and pile location is calculated as a combination of crane geometry and machine position. Inputs and outcomes here are developed interactively with Tasks 1.1 and 1.2. Engine use profiles (including fuel consumption) to be analysed directly from CAN bus and modelled against the given conditions.
In Task 2.3 a virtual model of harvester forwarder operations will be created on the basis of data and relationship functions generated from WP 1 and Tasks 2.1 and 2.2. Once the modules have achieved satisfactory representation levels they will be used in providing input functions to WP 4 and WP 5.
Objectives
O2.1 To implement and test sensors and other technologies in supplementing data captue in other WPs
O2.2 To demonstrate a model depicting machine component movement and task duration
O2.3 To automate log control measurements in ensuring a higher data quality in the supply chain

WP 3: Value recovery coach - feedback on wood utilization
Short name of participant: Skogforsk, Skogkurs, NIBIO, UGoe
Efficient use of the forest resource includes assuring a high value recovery of each stem. Operators rely on regular and tangible feedback on their operations to achieve continuous improvements in their work. Today, this is often a shortcoming, leaving the operators unaware of the final results of the harvest. A tool for continuous feedback on value recovery thus has a potential to increase resource efficiency and sustainability of forest operations.
Objectives
O3.1 To improve stem value recovery through feedback systems on bucking quality and stem measurement.

WP 4: Systems perspective - linking harvester and forwarder dynamics
Short name of participant: UGoe, Skogforsk, Skogkurs, NIBIO, WH-NRW
While operation productivity of the harvester is depending on stand and terrain conditions (Mederski et al. 2016), harvesting pattern, including log positioning, determines by far forwarding efficiency. Despite the significant effect of overall travel distance, the conditions for log loading in the stand, such as pre-bunching of assortments, directional position of logs and timber concentration along the skid trails, are the decisive determinants for loading drive time and overall cycle time demand (Manner et al. 2013; Nurminen et al. 2006). Ghaffarian et al. (2007) showed that under Central European conditions, the loading element alone can contribute to more than 50% of the overall extraction time within CTL operations. It is therefore essential to address the whole range of identified factors for operator-machine interaction efficiency (WP 1, WP 2 & WP 3) between the individual actors of the supply chain, namely the harvester and consecutive forwarder, too. Generated and accessible stand information to the harvester operator, communicated work output information to the forwarder operator, and both directional exchange of technical machine specifications to ensure optimized grapple utilization and payload capacities, as well as accounting for local infrastructure conditions is the way forward to generate efficient harvester-forwarder system dynamics.
Objectives
O4.1 To improve work flow interaction among harvester and forwarder operators
O4.2 To increase efficiency of forwarding activities by optimized harvester pre-bunching and operator assistance

WP 5: Designing the framework for a digital coach
Short name of participant: IfADo, WH-NRW, Skogforsk, Skogkurs, Optea, UGoe
Even experienced operators exhibit a high variability in productivity under similar working conditions. AVATAR therefore will focus on the individual improvement potential on operator level, based on personal work patterns. The aim of WP 5 is to design digital coach which provides individual feedback and support for the optimal handling of the machine and the efficiency of the harvest. The feedback is based on the algorithms designed in WP 1 and WP 3. Two levels of feedback will be provided. At the first level, augmented feedback is provided on the basis of detrimental working patterns identified by the algorithms of WP 1. Suggestions for correcting the deviations and the rationale behind them will be part of the feedback. In addition, the deviation pattern is used to identify the potential for improvement, which can then be used to further tailor an individual training focus for the operator. At the second level of feedback the operator continuously will be informed about the final results of the harvest. This feedback is based on the outcome and algorithms of WP 3 and provides different aspects of value recovery from forest operations for example by key performance indicators (KPIs) to train and assist the operator directly on the spot.
Objectives
O5.1 Development of skill development monitoring and feedback solutions.
O5.2 Development of a prototypical operator interface with optional presentation of feedback presentation.

WP 6: Implementing a digital coach prototype and assessing its potential
Short name of participant: WH-NRW, IfADo, Skogkurs, Skogforsk, NIBIO, UGoe
The aim of WP 6 is to verify the function and actual improvement potential of the digital coach through a practical in-field and/or simulator based case study with selected operators. A machine equipped with the required hard and software for the digital coach as developed in WP 2 and WP 3, will be run by operators of different professional level within standard cut-to-length operations under representative conditions for Scandinavian and Central European forests. Through operational monitoring and work study approaches, such as time and motion studies and stress level strain observations (e.g. heart rate frequency measurements), efficiency increase potential through the digital coach, but also the mental effect on the operator is quantified. Objectives
The overall aim of WP 6 is to test and to evaluate the systems developed in work packages 2 and 3 and to make recommendations to future development and implementation of an operator interface with optimal timing of feed back interpretation for decision support.
O6.1 The prototyp of digital coach will be integrated in forest machines in Germany and Scandinavia and also as a testbed in the simulators of FBZ. The assessment of the pilot use cases shall critically evaluate the enhancement in the forest supply chain by the pilot cases. Additionally, the assessment by stakeholders and users of the pilots shows how well the project met the needs of the market which is crucial interest for all partners involved in the project
O6.2 The approach adopted by the project consortium with the regard of training is to ensure that the acquired knowledge can be optimally applied by users. The project can take full advantage of the role of the training center FBZ in NRW, as they do run training courses for machine operators on a permanent basis. They are offering these courses not only for the federal state but even on an international level. (e.g. cooperation contracts exists between Switzerland and Arnsberg)

WP 7: Dissemination, Exploitatio & Communication
Short name of participant: UGoe, IfDo, Skogforsk, NIBIO, WH-NRW, Skogkurs
Objectives
O7.1 To ensure appropriate dissemination and exploitation of project findings
O7.2 To facilitate an adaptation of practitioners and machine manufacturers of project results

WP 8: Management
Short name participant: UGoe, NIBIO, Skogforsk, IfaDo, Optea, WH-NRW, Skogkurs
The project governance structure (i.e. management) is adapted from the DESCA H2020 Version 1.0 Governance structure for small-medium sized projects.
Despite the relatively small size of the consortium, the governance structure of the AVATAR project will comprise both a General Assembly (GA), and the Executive Board (EB), with clearly defined roles and responsibilities. The GA consists of one representative of each participating organisation, will convene every 6 months (accommodating online participation), and is chaired by the coordinator. The General Assembly (GA) is the ultimate decision-making body of the project, dealing with issues related to the content, finances, intellectual property rights, the publication of project results and evolution of the consortium.
The Executive Board (EB) is (with the Management Support Team) the operational management body of the project responsible for efficient implementation of the project. EB will work under GA and assist, make proposals and report to GA. The EB will decide on possible changes to the Consortium plan and the publication of project information materials. Being a small project, the EB will also take responsibility for discussing important DEC issues, although final decisions again lie with the GA. The EB consists of the Coordinator (chairman) & WP leaders, and convenes monthly via online meetings.
The operational procedures of EB as well as GA shall be laid down in the Consortium Agreement. The operative instruments of the project will be the Coordinator, WP leaders and local project manager in each participating organisation. The roles and responsibilities of the Coordinator and each contractor will be defined in the Grant Agreement and its general conditions and the Consortium Agreement.
Objectives
O8.1 To ensure a sound project implementation according to the grant agreement with respect to all technical, administrative, financial and legal aspects.
O8.2 To monitor project progress and adapt work plans if risks are identified, jeopardizing
O8.3 To oversee the successful implementation of the DEC plan