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Description
Forestry cranes play a critical role in mechanized logging operations, yet their core mechanical architecture has changed little over the past decades. As a result, they consume substantial energy, much of which is continuously required to support their own structural mass and payload against gravity, even when not performing productive work. While prior research has focused extensively on automation, trajectory planning, advanced control strategies, and improvements in hydraulic and actuation systems, these approaches primarily optimize motion execution rather than addressing the underlying mechanical sources of energy demand.
This work proposes a model-based optimization framework to minimize overall operational energy consumption. Passive mechanical elements are incorporated as design variables within the mechanical architecture, enabling systematic exploration of energy-efficient configurations. Although such elements are widely used in robotics to improve energy efficiency through gravity compensation, their integration into forestry crane design remains largely unexplored.
The optimized configurations are experimentally validated using reduced-scale crane prototypes, enabling direct comparison with the original design. Results demonstrate energy reductions exceeding 45% during dynamic tasks while maintaining functional performance and operational capability.
These findings show that substantial gains in energy efficiency can be achieved by rethinking the mechanical architecture of forestry cranes, rather than relying solely on improved control strategies or actuation technologies. Integrating passive elements into the structural design significantly reduces inherent energy demand, offering a practical pathway toward more sustainable and energy-efficient forestry operations.
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