Results 221 to 230 of about 191,827 (344)

Myoelectric Origami‐Based Soft Robotic Knee Exoskeleton to Enhance Sit‐to‐Stand Assistance in Elderly Populations

open access: yesAdvanced Intelligent Systems, EarlyView.
This work presents a lightweight Miura‐origami soft knee exoskeleton powered by vacuum actuation and integrated with a multimodal physiological intent‐recognition system, providing real‐time assistance during sit‐to‐stand movement to reduce muscle effort and improve user comfort.
Yuchuan Jia   +5 more
wiley   +1 more source

Nonlinear damping in energy harvesters

open access: yes, 2017
Energy harvesting from ambient vibration has become an attractive topic in the recent years. Initial studies aimed to maximise the performance of small linear device for different excitation scenarios. These devices were assumed to be located in hostile and inaccessible environments and be able to provide energy for low powered sensors.
openaire   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

open access: yesAdvanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi   +3 more
wiley   +1 more source

Experimental realization of logical elastic bits as qubit analogues in a nonlinear oscillator. [PDF]

open access: yesSci Rep
Mahmood KT   +5 more
europepmc   +1 more source

Collaborative Multiagent Closed‐Loop Motion Planning for Multimanipulator Systems

open access: yesAdvanced Intelligent Systems, EarlyView.
This work presents a hierarchical multi‐manipulator planner, emphasizing highly overlapping space. The proposed method leverages an enhanced Dynamic Movement Primitive based planner along with an improvised Multi‐Agent Reinforcement Learning approach to ensure regulatory and mediatory control while ensuring low‐level autonomy. Experiments across varied
Tian Xu, Siddharth Singh, Qing Chang
wiley   +1 more source

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