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Porous feet from the 3D printer reduce the energy consumption of robot dogs
Germany💻 Technology23 hr. ago

Porous feet from the 3D printer reduce the energy consumption of robot dogs

A research team from the School of Mechanical System Engineering at Seoul National University of Science and Technology has developed porous foot pads for quadruped robots using Triply Periodic Minimal Surfaces (TPMS). These foot pads store impact energy during movement and release it during propulsion, significantly reducing the energy consumption of legged robots. Legged robots typically require more energy for locomotion compared to wheeled counterparts, limiting their operational duration. The researchers used 3D printing to create three different TPMS-based foot pad designs, 'Primitive,' 'Gyroid,' and 'Diamond', each with varying structures and densities. They tested these designs during walking, which is the least efficient gait for energy recovery in legged robots. To optimize energy usage, the team employed deep reinforcement learning to adjust motor control, minimizing energy expenditure during force application. Tests showed up to a 6.2% reduction in power consumption during slow walking speeds between 0.4 and 1.0 meters per second, with minimal impact on the robot's stability and gait characteristics.

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heise online logoheise onlineIndependentCenter23 hr. ago
Porous feet from the 3D printer reduce the energy consumption of robot dogs

A research team from the School of Mechanical System Engineering at Seoul National University of Science and Technology has developed porous foot pads for quadruped robots using Triply Periodic Minimal Surfaces (TPMS). These foot pads store impact energy during movement and release it during propulsion, significantly reducing the energy consumption of legged robots. Legged robots typically require more energy for locomotion compared to wheeled counterparts, limiting their operational duration. The researchers used 3D printing to create three different TPMS-based foot pad designs, 'Primitive,' 'Gyroid,' and 'Diamond', each with varying structures and densities. They tested these designs during walking, which is the least efficient gait for energy recovery in legged robots. To optimize energy usage, the team employed deep reinforcement learning to adjust motor control, minimizing energy expenditure during force application. Tests showed up to a 6.2% reduction in power consumption during slow walking speeds between 0.4 and 1.0 meters per second, with minimal impact on the robot's stability and gait characteristics.

Bias read (Center): The article presents a scientific study on technological innovation without any political implications. It focuses on engineering advancements and energy efficiency in robotics, which is a technical rather than political issue. There is no indication of ideological leaning or partisan framing in the

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