Research highlights: An automatic localization system for ground obstacles on harvested forest land based on existing mature hardware and software architecture has been successfully implemented. In the tested area, 98% of objects were successfully detected and could on average be positioned within 0.33 m from their true position in the full range 1–10 m from the camera sensor. Background and objectives: Forestry operations in forest environments are full of challenges; detection and localization of objects in complex forest terrains often require a lot of patience and energy from operators. Successful automatic real-time detection and localization of terrain objects not only can reduce the difficulty for operators but are essential for the automation of harvesting and logging tasks. We intend to implement a system prototype that can automatically locate ground obstacles on harvested forest land based on accessible hardware and common software infrastructure. Materials and Methods: An automatic object detection and localization system based on stereo camera sensing is described and evaluated in this paper. This demonstrated system detects and locates objects of interest automatically utilizing the YOLO (You Only Look Once) object detection algorithm and derivation of object positions in 3D space. System performance is evaluated by comparing the automatic detection results of the tests to manual labeling and positioning results. Results: Results show high reliability of the system for automatic detection and location of stumps and large stones and shows good potential for practical application. Overall, object detection on test tracks was 98% successful, and positional location errors were on average 0.33 m in the full range from 1–10 m from the camera sensor. Conclusions: The results indicate that object detection and localization can be used for better operator assessment of surroundings, as well as input to control machines and equipment for object avoidance or targeting.

Sustainable forestry requires efficient regeneration methods to ensure that new forests are established quickly. In Sweden, 99% of the planting is manual, but finding labor for this arduous work is difficult. An autonomous scarifying and planting machine with high precision, low environmental impact, and a good work environment would meet the needs of the forest industry. For two years, a collaborative group of researchers, manufacturers, and users (forest companies) has worked together on developing and testing a new concept for autonomous forest regeneration (Autoplant). The concept comprises several subsystems, i.e., regeneration and route planning, autonomous driving (path planning), new technology for forest regeneration with minimal environmental impact, automatic plant management, crane motion planning, detection of planting spots, and follow-up. The subsystems were tested separately and integrated together during a field test at a clearcut. The concept shows great potential, especially from an environmental perspective, with significantly reduced soil disturbances, from approximately 50% (the area proportion of the area disturbed by disc trenching) to less than 3%. The Autoplant project highlights the challenges and opportunities related to future development, e.g., the relation between machine cost and operating speed, sensor robustness in response to vibrations and weather, and precision in detecting the size and type of obstacles during autonomous driving and planting.

To further develop forest production, higher automation of forest operations is required. Such endeavour promotes research on unmanned forest machines. Designing unmanned forest machines that exercise forwarding requires an understanding of positioning and angle estimations of logs after cutting and delimbing have been conducted, as support for subsequent crane loading work. This study aims to improve the automation of the forwarding operation and presents a system to realize real-time automatic detection, positioning, and angle estimation of harvested logs implemented on an existing unmanned forest machine experimental platform from the AORO (Arctic Off-Road Robotics) Lab. This system uses ROS as the underlying software architecture and a Zed2 camera and NVIDIA JETSON AGX XAVIER as the imaging sensor and computing platform, respectively, utilizing the YOLOv3 algorithm for real-time object detection. Moreover, the study combines the processing of depth data and depth to spatial transform to realize the calculation of the relative location of the target log related to the camera. On this basis, the angle estimation of the target log is further realized by image processing and color analysis. Finally, the absolute position and log angles are determined by the spatial coordinate transformation of the relative position data. This system was tested and validated using a pre-trained log detector for birch with a mean average precision (mAP) of 80.51%. Log positioning mean error did not exceed 0.27 m and the angle estimation mean error was less than 3 degrees during the tests. This log pose estimation method could encompass one important part of automated forwarding operations.

Reforestation is performed after the final felling as an important and often law-mandated step to ensure that wood production is sustainable. In Sweden alone, over 400 millions seedlings are planted annually. This work is physically demanding and the quality is uneven. Therefore, automatic production systems are under research and development. A necessary effort in this endeavor is presented in this paper: the development and evaluation of a mission supervisor utilized to control the mission and behavior of a full-scale autonomous forest regeneration machine tested in realistic environments. The mission supervisor is implemented in the Robot Operating System framework using a finite state machine package called SMACH. A terrain machine built as a research platform with an added full-scale forwarder crane is used as a base machine. First, we describe the scenario in which planting is conducted, whereupon we develop the composite tasks required as states. A simplified simulator then enables an intermediate step before field experiments. The system is implemented and operated in real time on a full-scale machine. Results show that the developed SMACH mission supervisor can be used as a sound basis for an autonomous forest regeneration machine and the chosen communication solution between different systems works well. The simulations show good agreement with the experiments. The results also show that crane movements take 70% of the machine time, emphasizing the importance of limiting crane movement, improving the actuator movement speed and integrating the composite solutions. Further development with a holistic approach is required before the concept can reach the prototype level.

This work presents outdoor test results of autonomous navigation system based on two different reference points of PurePursuit algorithm for a 10-ton articulated research platform. PurePursit is commonly used simplified tracking algorithm based on geometric calculation of desired steering angle by pursuing certain number of points/distance ahead on given path from a fixed reference point of vehicle. Choice of fixed reference point effects the tracking accuracy particularly for articulated/center-steered vehicles. In this experimental work, PurePursuit algorithm with a virtual reference point (PPV) and commonly used front-axle reference point (PPF) is evaluated for heavy duty articulated vehicles in outdoor experiments. Experimental data shows that choice of reference point in PurePursuit algorithm for articulated vehicle has impact on tracking accuracy in terms of crosstrack errors and heading errors. Navigation tests were performed on a flat asphalt surface for paths of sharp complexities i-e a path with continuous curvature (circular path) and a path with sharp turns (zigzag path) with different initial conditions i-e initial position of vehicle. In general, it can be concluded that PurePursuit algorithm with front reference point (PPF) produced fewer crosstrack errors while PurePursuit algorithm with virtual midpoint reference (PPV) produced fewer heading errors.