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.

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.

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.