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.

Automated visual quality inspection is a core topic of robotics and computer vision. In industrial applications, the CAD model of the object to be inspected is often known and can be used to generate appropriate sensor poses (viewpoints) from which to inspect the object’s surface and assure the quality of its geometry. Current approaches in this field generate optimal viewpoints by evaluating the geometric coverage but the photometric appearance of the object is usually not considered. This lack of photometric information results in a loss of crucial cues to establish actual visibility, especially when the object to inspect presents specular highlights (e.g., polished metal parts) and a complex geometry. In this paper, we propose integrating photometric information into the viewpoint evaluation to consider the object’s appearance. To achieve this, we embed a bidirectional reflectance distribution function (BRDF) within the evaluation of the viewpoint candidates. We benchmark different BRDFs with increasingly realistic rendering to prove the concept of our approach. Specifically, we consider the Blinn-Phong and Cook-Torrance reflectance models. Our simulation results demonstrate the suitability and importance of using a photometric approach that considers material properties and selects optimal viewpoints for specific materials.

Automatic visual quality inspection is pivotal in both computer vision and robotics. It plays a crucial role in manufacturing, where robotic systems are increasingly employed to enhance the speed and efficiency of visual quality assessments. Several inspection planning methodologies have been developed; however, they often address the inspection challenge from a singular perspective of robotics or computer vision. This work introduces a comprehensive approach that synergistically integrates principles from both domains. We present an innovative algorithm designed to generate optimal inspection poses by considering the interplay between the inspected object’s geometry and the kinematics of the robotic setup used for inspection. This is accomplished by taking advantage of the concept of visibility. The effectiveness of our algorithm is demonstrated through simulations and experiments, revealing complete coverage for diverse geometries and materials with a small number of inspection poses. Moreover, we benchmark our framework against box constraints and workspace sampling techniques to generate feasible inspection poses. The results indicate superior performance in achieving extensive coverage and reducing the number of required optimal inspection poses, enhancing the overall inspection process.

In this article, a novel approach for the versatile wrinkle-free application of (curved) precut adhesive tapes on freeform 3-D surfaces is presented. Straight and curved tape application paths are mapped onto the 3-D object as geodesics and as lines with imposed geodesic curvature, respectively. The proposed surface-based path following control concept extends the classical path following control by a novel parallel contact frame and a parallel projection operator. Using a static state feedback, the robotic system is transformed into a system with linear input-output behavior in the path coordinates. This allows to traverse a path on a 3-D object with a draping roll without turning around the surface normal vector. The latter prevents distortions and wrinkles of the applied tape. Experimental results with a Kuka LBR iiwa 14 R820 demonstrate the feasibility of the proposed approach.

The workspace of a robot can be restricted by virtual fixtures to assist an operator in physical human-robot interaction tasks. This paper introduces a combination of surface following control (SFC) with compliance control and presents a path/SFC approach to systematically generate virtual fixtures. This approach allows implementation of numerous types of constraints like guidance and forbidden region virtual fixtures, hard and soft constraints, as well as static and dynamic virtual fixtures, and their combinations. Additionally, closed-loop stability proofs of the proposed control concepts are given. The flexibility of the presented approach is demonstrated by a series of measurement results from an industrial robot.