The timber-harvesting industry is lagging its peer industries, such as mining and agriculture, with respect to deployment of robotic, AI and autonomous technologies. In this paper, we tackle automation of a critical task that arises in transporting logs from the forest to the sawmill: the log loading operation. This work is motivated by the acute shortages of human operators and the need to improve the efficiencies of timber-harvesting processes. To this end, we demonstrate the full autonomy pipeline for the log loading operation with a fixed-base manipulator (a.k.a., the crane), starting with perception of logs around the machine, then grasp planning for where to grasp logs, through motion planning and control of the log loading maneuver. Our main contribution is in the full integration of the necessary elements to achieve a completely autonomous loading cycle, where the crane picks up and loads all logs within its reach on a trailer. Notable features of our implementation are a generalizable perception stack, a grasp planner to pick up multiple logs at a time and an extensive experimental campaign conducted outdoors, on a commercial log loader retrofitted for autonomy. Our results demonstrate an overall 87% success rate of the log loading operation, with primary failure cases due to log segmentation errors and deficiencies in the final height adjustment algorithm for grasping logs. We also present detailed timing results of the main parts of the autonomy pipeline, which support the feasibility of deployment in operational environment.

Log loading constitutes a key operation in timber harvesting, and despite the recent spike of interest in introducing automation to the forestry sector, efficient and intelligent grasping of logs remains unresolved. This paper presents a grasp planning pipeline that relies on the identification of logs' characteristics and pose in the environment of a log-loading machine, to generate high quality grasps. The proposed pipeline involves replicating identified logs in a virtual environment where grasp planning is carried out by using a convolutional neural network and a virtual depth camera. The network relies solely on depth information and the virtual camera can be positioned at a strategically selected location or to follow a certain trajectory to enhance exposure of the logs, all this without having to move the log-loader's crane. The grasp planning pipeline is evaluated through simulated grasping trials and experiments on a large-scale log-loading test-bed with several configurations of wood logs ranging from a single to multiple logs. The grasp planning pipeline proved to be successful with a grasping rate of 98.33 % in the simulated trials and 96.67 % in the experimental trials. The grasp planner was able to overcome log characterization and localization uncertainties, thus allowing the log-loader to pick individual logs, and multiple logs at once when possible.