901 research outputs found
Pneumatic robotic systems for upper limb rehabilitation
The aim of rehabilitation robotic area is to research on the application of robotic devices to therapeutic procedures. The goal is to achieve the best possible motor, cognitive and functional recovery for people with impairments following various diseases. Pneumatic actuators are attractive for robotic rehabilitation applications because they are lightweight, powerful, and compliant, but their control has historically been difficult, limiting their use. This article first reviews the current state-of-art in rehabilitation
robotic devices with pneumatic actuation systems reporting main features and control issues of each therapeutic device. Then, a new pneumatic rehabilitation robot for proprioceptive neuromuscular facilitation therapies and for relearning daily living skills: like taking a glass, drinking, and placing object on shelves is described as a case study and compared with the current pneumatic rehabilitation devices
Intelligent upper-limb exoskeleton using deep learning to predict human intention for sensory-feedback augmentation
The age and stroke-associated decline in musculoskeletal strength degrades
the ability to perform daily human tasks using the upper extremities. Although
there are a few examples of exoskeletons, they need manual operations due to
the absence of sensor feedback and no intention prediction of movements. Here,
we introduce an intelligent upper-limb exoskeleton system that uses cloud-based
deep learning to predict human intention for strength augmentation. The
embedded soft wearable sensors provide sensory feedback by collecting real-time
muscle signals, which are simultaneously computed to determine the user's
intended movement. The cloud-based deep-learning predicts four upper-limb joint
motions with an average accuracy of 96.2% at a 200-250 millisecond response
rate, suggesting that the exoskeleton operates just by human intention. In
addition, an array of soft pneumatics assists the intended movements by
providing 897 newton of force and 78.7 millimeter of displacement at maximum.
Collectively, the intent-driven exoskeleton can augment human strength by 5.15
times on average compared to the unassisted exoskeleton. This report
demonstrates an exoskeleton robot that augments the upper-limb joint movements
by human intention based on a machine-learning cloud computing and sensory
feedback.Comment: 15 pages, 6 figures, 1 table, Submitted for possible publicatio
Design, fabrication and control of soft robots
Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.National Science Foundation (U.S.) (Grant IIS-1226883
A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions
A long-lasting goal of robotics research is to operate robots safely, while
achieving high performance which often involves fast motions. Traditional
motor-driven systems frequently struggle to balance these competing demands.
Addressing this trade-off is crucial for advancing fields such as manufacturing
and healthcare, where seamless collaboration between robots and humans is
essential. We introduce a four degree-of-freedom (DoF) tendon-driven robot arm,
powered by pneumatic artificial muscles (PAMs), to tackle this challenge. Our
new design features low friction, passive compliance, and inherent impact
resilience, enabling rapid, precise, high-force, and safe interactions during
dynamic tasks. In addition to fostering safer human-robot collaboration, the
inherent safety properties are particularly beneficial for reinforcement
learning, where the robot's ability to explore dynamic motions without causing
self-damage is crucial. We validate our robotic arm through various
experiments, including long-term dynamic motions, impact resilience tests, and
assessments of its ease of control. On a challenging dynamic table tennis task,
we further demonstrate our robot's capabilities in rapid and precise movements.
By showcasing our new design's potential, we aim to inspire further research on
robotic systems that balance high performance and safety in diverse tasks. Our
open-source hardware design, software, and a large dataset of diverse robot
motions can be found at https://webdav.tuebingen.mpg.de/pamy2/
Modeling, Reduction, and Control of a Helically Actuated Inertial Soft Robotic Arm via the Koopman Operator
Soft robots promise improved safety and capability over rigid robots when
deployed in complex, delicate, and dynamic environments. However, the infinite
degrees of freedom and highly nonlinear dynamics of these systems severely
complicate their modeling and control. As a step toward addressing this open
challenge, we apply the data-driven, Hankel Dynamic Mode Decomposition (HDMD)
with time delay observables to the model identification of a highly inertial,
helical soft robotic arm with a high number of underactuated degrees of
freedom. The resulting model is linear and hence amenable to control via a
Linear Quadratic Regulator (LQR). Using our test bed device, a dynamic,
lightweight pneumatic fabric arm with an inertial mass at the tip, we show that
the combination of HDMD and LQR allows us to command our robot to achieve
arbitrary poses using only open loop control. We further show that Koopman
spectral analysis gives us a dimensionally reduced basis of modes which
decreases computational complexity without sacrificing predictive power.Comment: Submitted to IEEE International Conference on Robotics and
Automation, 202
- …