Características principais
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Ultra-Lightweight Bionic Design: Pesando apenas 383g (aprox. the weight of six eggs), the hand is 20% lighter than many industry competitors, reducing the inertial load on the robot’s arm joints.
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High Payload & Grip Force: Despite its compact size, it delivers a powerful 50N grip force and can support a static load of up to 20 kg, allowing it to handle heavy everyday objects like a full water jug.
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Atuação de precisão: Recursos 11 degrees of freedom (DoF) com 6 active joints, enabling complex gestures and adaptive grasping. It achieves a repeat accuracy of 0.1° for fine-motor tasks.
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Human-Scale Ergonomics: At 16 cm in length, it matches the size of an average adult hand, ensuring it can interact with tools and environments designed for humans.
Especificações técnicas
| Parâmetro | Especificações |
| Modelo | BrainCo Revo 2 Basic |
| Peso | ~383 g |
| Dimensões | 160 mm x 76 mm |
| Graus de Liberdade (DoF) | 11 (6 Active Joints) |
| Power Grasp Force | >= 50 N |
| Finger Pinch Force | >= 15 N |
| Max Static Load | 20 kg |
| Repeat Accuracy | 0.1° |
| Operating Noise | <= 50 dB |
| Comunicação | RS485, CAN-FD |
| Alcance de voltagem | 9V – 28V |
Comparison: Basic vs. Pro/Touch Versions
O Basic Version is optimized for movement and strength. While it supports position, speed, and current control, it lacks certain advanced features found in the higher versions:
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Comunicação: Basic uses RS485/CAN-FD; the Pro/Touch versions add EtherCAT support.
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Tactile Feedback: The Basic version does não include the 3D tactile perception system (pressure, friction, and force direction sensing) found in the Revo 2 Touch.
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Voltagem: The Basic version operates up to 28V, whereas the Pro/Touch versions support a wider range up to 64V.
Aplicações ideais
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Pesquisa sobre humanoides: Developing locomotion and balance while carrying objects.
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Robótica de Serviço: Simple pick-and-place tasks in domestic or laboratory settings.
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Educação: Teaching kinematics and bionic control at the university level.
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AI Training: Using reinforcement learning to master human-like grasping gestures.












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