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All Journal IAES International Journal of Robotics and Automation (IJRA)
La Hera, Pedro
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Automotive Engineering Electrical & Electronics Engineering

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Combining optimization and dynamic movement primitives for planning energy optimal forestry crane motions La Hera, Pedro; Morales, Daniel Ortíz; Mendoza-Trejo, Omar
IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v13i2.pp180-193

Abstract

Forestry cranes are an important tool for safe and efficient timber harvesting with forestry machines. However, their complex manual control often led to inefficiencies and excessive energy usage, due to the many joysticks and buttons that must be used in a precise sequence to perform efficient movements. To address this, the industry is increasingly turning to partial automation, making manual control more intuitive for the operator and, consequently, achieving improvements in energy efficiency. This article introduces a novel approach to energy-optimal motion planning that can be used along with a feedback control system to automate crane motions, taking over portions of the operator’s work. Our method combines dynamic movement primitives (DMPs) and an energy-optimization algorithm. DMPs is a machine learning technique for motion planning based on human demonstrations, while the optimization algorithm exploits the crane’s redundancy to find energy-optimal trajectories. Simulation results show that DMPs can replicate human-like controlled motions with a 25% reduction in energy consumption. However, our energy optimization algorithm shows improvements of over 40%, providing substantial energy savings and a promising pathway towards environmentally friendly partially automated machines.
Co-Authors Mendoza-Trejo, Omar Morales, Daniel Ortíz