Joint MECHATRONICS 2025, ROBOTICS 2025 Paper Abstract

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Vu, Minh Nhat (Automation & Control Institute (ACIN), TU Wien, Austria), Ebmer, Gerald (TU Wien), Wachter, Alexander (TU Vienna), Ecker, Marc-Philip (TU Wien, Austrian Institute of Technology), Nguyen, Giang Hoang (University of Bremen), Glück, Tobias (Austrian Institute of Technology)

GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments

Scheduled for presentation during the Regular Session "Robot Task Planning" (FrAT2), Friday, July 18, 2025, 11:20−11:40, Room 106

Joint 10th IFAC Symposium on Mechatronic Systems and 14th Symposium on Robotics, July 15-18, 2025, Paris, France

This information is tentative and subject to change. Compiled on August 2, 2025

Keywords domestic robots, Transport and delivery robots, Service robots

Abstract

Autonomous large-scale machine operations require fast, efficient, and collision-free motion planning while addressing unique challenges such as hydraulic actuation limits and underactuated joint dynamics. This paper presents a novel two-step motion planning framework designed for an underactuated forestry crane. The first step employs GPU-accelerated stochastic optimization to rapidly compute a globally shortest collision-free path. The second step refines this path into a dynamically feasible trajectory using a trajectory optimizer that ensures compliance with system dynamics and actuation constraints. The proposed approach is benchmarked against conventional techniques, including RRT-based methods and purely optimization-based approaches. Simulation results demonstrate substantial improvements in computation speed and motion feasibility, making this method highly suitable for complex crane systems.

 

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