Articles tagged with Robot Components
Researchers from Singapore University of Technology and Design developed a new reconfigurable workspace soft robotic gripper that can pick and place a wide range of consumer items. The RWS gripper's adaptive capabilities make it particularly useful in logistics and food industries where robotic automation is crucial.
A team of researchers developed a new method for 3D-printing microrobots with multiple component modules inside the same microfluidic chip. The 'assembly line' approach allowed for the combination of various modules, such as joints and grippers, into a single device. This innovation may help realize the vision of microsurgery performed...
Researchers develop mechanical neural networks (MNNs) with tunable beams that can learn behaviors and adapt to external forces. The MNNs, composed of a triangular lattice pattern, exhibit smart properties through machine learning algorithms. Early prototypes overcame lag issues and achieved accurate performance in various applications.
Researchers found that an elephant's folded skin plays a crucial role in its flexible and strong trunk, enabling it to grasp fragile vegetation and rip apart tree trunks. The study suggests that wrapping soft robotics with a skin-like structure could give machines protection and strength while maintaining flexibility.
Researchers at Harvard University have developed inflatable actuators that can bend, twist, and move in complex ways using origami-inspired designs. The actuator's bistable origami blocks allow it to perform up to eight different motions with a single pressure source.
Researchers at Singapore University of Technology and Design developed a new machine learning approach to model underwater robot dynamics, allowing for efficient swimming in complex environments. The approach, published in IEEE-RAL, uses deep neural networks to predict required flapping motions for a set of given propulsive force targets.
Researchers from Harvard John A. Paulson School of Engineering and Applied Sciences have developed a single-material, single-stimuli microstructure that can outmaneuver even living cilia. These programmable structures could be used for soft robotics, biocompatible medical devices, and dynamic information encryption.
Developed by Prof. Qing Shi's team, SQuRo can mimic the motion of actual rats and perform various motions like crouching-to-standing, walking, crawling, and turning. It successfully passed through an irregular narrow passage and demonstrated its potential application to inspection tasks inside narrow spaces.
Researchers developed a hybrid machine-learning approach combining CNN and LSTM to recognize complex hand gestures in prosthetic hands. The technique achieved far superior performance than traditional machine learning efforts, with an accuracy of over 80%, but struggled with certain pinching gestures.
A new design for thermal actuators accelerates soft robotic movement by exploiting temperature-dependent bi-stability. The structure changes shape in response to heat, allowing for rapid snapping actions. Prototypes demonstrate rapid movement capabilities, paving the way for biomedical, prosthetic, and manufacturing applications.
Researchers created a paper-like material that folds itself into new shapes in response to environmental humidity, with potential applications in self-folding envelopes and boxes. The material's ability to morph on demand could lead to the development of autonomous origami robots and other complex shapes.
Researchers developed electrically-driven soft valves to control hydraulic soft actuators, enabling faster and more powerful control of macro- and small-scale hydraulic actuators. The breakthrough allows for unprecedented motion control of soft robots with internal volume ranging from hundreds of microliters to tens of milliliters.