Efficient, collision-free motion planning is essential for automating large-scale manipulators like timber cranes. They come with unique challenges such as hydraulic actuation constraints and passive joints-factors that are seldom addressed by current motion planning methods. This paper introduces a novel approach for time-optimal, collision-free hybrid motion planning for a hydraulically actuated timber crane with passive joints. We enhance the via-point-based stochastic trajectory optimization (VP-STO) algorithm to in-clude pump flow rate constraints and develop a novel collision cost formulation to improve robustness. The effectiveness of the enhanced VP-STO as an optimal single-query global planner is validated by comparison with an informed RRT* algorithm using a time-optimal path parameterization (TOPP). The over-all hybrid motion planning is formed by combination with a gradient-based local planner that is designed to follow the global planner's reference and to systematically consider the passive joint dynamics for both collision avoidance and sway damping.

Forestry machines operated in forest production environments face challenges when performing manipulation tasks, especially regarding the complicated dynamics of underactuated crane systems and the heavy weight of logs to be grasped. This study investigates the feasibility of using reinforcement learning for forestry crane manipulators in grasping and lifting heavy wood logs autonomously. We first build a simulator using Mujoco physics engine to create realistic scenarios, including modeling a forestry crane with 8 degrees of freedom from CAD data and wood logs of different sizes. We further implement a velocity controller for autonomous log grasping with deep reinforcement learning using a curriculum strategy. Utilizing our new simulator, the proposed control strategy exhibits a success rate of 96% when grasping logs of different diameters and under random initial configurations of the forestry crane. In addition, reward functions and reinforcement learning baselines are implemented to provide an open-source benchmark for the community in large-scale manipulation tasks. A video with several demonstrations can be seen at https://www.acin.tuwien.ac.at/en/d18a/.

Collision-free motion planning in complex outdoor environments relies heavily on perceiving the surroundings through exteroceptive sensors. A widely used approach represents the environment as a voxelized Euclidean distance field, where robots are typically approximated by spheres. However, for large-scale manipulators such as forestry cranes, which feature long and slender links, this conventional spherical approximation becomes inefficient and inaccurate.This work presents a novel collision detection algorithm specifically designed to exploit the elongated structure of such manipulators, significantly enhancing the computational efficiency of motion planning algorithms. Unlike traditional sphere decomposition methods, our approach not only improves computational efficiency but also naturally eliminates the need to fine-tune the approximation accuracy as an additional parameter. We validate the algorithm’s effectiveness using real-world LiDAR data from a forestry crane application, as well as simulated environment data.

Permanent magnet linear synchronous motors (PMLSM) are gaining attention in high-precision production systems. Smooth and accurate motion is vital, specifically for applications that involve on-the-move processing. This necessitates highly precise position measurement of the shuttles moving along the curvilinear motor track. In this work, we address an anisotropic magnetoresistive sensor (AMR) based position sensor system, where tolerances in the mounting, the magnetization of the permanent magnets (PM), and the sensor electronics can yield intolerably high position errors if no calibration is performed. This paper proposes a novel, user-friendly, cost-effective online calibration method utilizing iterative learning. The method leverages an acceleration sensor mounted directly on the shuttles, making it readily deployable in final PMLSM setups on production sites. Experimental results from a test stand demonstrate a significant reduction in measurement errors, paving the way for smooth shuttle motion along the entire curvilinear track within the PMLSM.

Multi-phase electric machines are frequently used in applications where a high system reliability is required. An accurate but computationally simple mathematical model of these electric machines is essential for the development of model-based control and fault detection strategies. Typically, fundamental wave models (dq0-models) are utilized for this task. They, however, exhibit a low accuracy for motor designs or in operating ranges where magnetic saturation or nonfundamental wave characteristics is relevant. In this article, magnetic equivalent circuit (MEC) modeling is utilized to derive a highly accurate model for a dual three-phase permanent magnet synchronous motor (PMSM). Thereby, graph theory is applied to both the electric and magnetic system to systematically derive a mathematical model of minimum order. The proposed approach allows for an accurate prediction of the system behavior in the entire operating range, including the case of a winding short circuit. The feasibility and high accuracy of the proposed model is proven by a comparison of the model with measurement results.