5 research outputs found
A Hybrid Adaptive Controller for Soft Robot Interchangeability
Soft robots have been leveraged in considerable areas like surgery,
rehabilitation, and bionics due to their softness, flexibility, and safety.
However, it is challenging to produce two same soft robots even with the same
mold and manufacturing process owing to the complexity of soft materials.
Meanwhile, widespread usage of a system requires the ability to fabricate
replaceable components, which is interchangeability. Due to the necessity of
this property, a hybrid adaptive controller is introduced to achieve
interchangeability from the perspective of control approaches. This method
utilizes an offline trained recurrent neural network controller to cope with
the nonlinear and delayed response from soft robots. Furthermore, an online
optimizing kinematics controller is applied to decrease the error caused by the
above neural network controller. Soft pneumatic robots with different
deformation properties but the same mold have been included for validation
experiments. In the experiments, the systems with different actuation
configurations and the different robots follow the desired trajectory with
errors of 0.040 and 0.030 compared with the working space length, respectively.
Such an adaptive controller also shows good performance on different control
frequencies and desired velocities. This controller endows soft robots with the
potential for wide application, and future work may include different offline
and online controllers. A weight parameter adjusting strategy may also be
proposed in the future.Comment: 8 pages, 9 figures, 4 table
A Novel and Accurate BiLSTM Configuration Controller for Modular Soft Robots with Module Number Adaptability
Modular soft robots have shown higher potential in sophisticated tasks than
single-module robots. However, the modular structure incurs the complexity of
accurate control and necessitates a control strategy specifically for modular
robots. In this paper, we introduce a data collection strategy and a novel and
accurate bidirectional LSTM configuration controller for modular soft robots
with module number adaptability. Such a controller can control module
configurations in robots with different module numbers. Simulation cable-driven
robots and real pneumatic robots have been included in experiments to validate
the proposed approaches, and we have proven that our controller can be
leveraged even with the increase or decrease of module number. This is the
first paper that gets inspiration from the physical structure of modular robots
and utilizes bidirectional LSTM for module number adaptability. Future work may
include a planning method that bridges the task and configuration spaces and
the integration of an online controller.Comment: 10 figures, 4 table
Dexterous Soft Hands Linearize Feedback-Control for In-Hand Manipulation
This paper presents a feedback-control framework for in-hand manipulation
(IHM) with dexterous soft hands that enables the acquisition of manipulation
skills in the real-world within minutes. We choose the deformation state of the
soft hand as the control variable. To control for a desired deformation state,
we use coarsely approximated Jacobians of the actuation-deformation dynamics.
These Jacobian are obtained via explorative actions. This is enabled by the
self-stabilizing properties of compliant hands, which allow us to use linear
feedback control in the presence of complex contact dynamics. To evaluate the
effectiveness of our approach, we show the generalization capabilities for a
learned manipulation skill to variations in object size by 100 %, 360 degree
changes in palm inclination and to disabling up to 50 % of the involved
actuators. In addition, complex manipulations can be obtained by sequencing
such feedback-skills.Comment: Accepted at 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
Data-Driven Methods Applied to Soft Robot Modeling and Control: A Review
Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently. Note to Practitioners āThis work is motivated by the need for a review introducing soft robot modeling and control methods in parallel. Modeling and control play significant roles in robot research, and they are challenging especially for soft robots. The nonlinear and complex deformation of such robots necessitates specific modeling and control approaches. We introduce the state-of-the-art data-driven methods and survey three approaches widely utilized. This review also compares the performance of these methods, considering some important features like data amount requirement, control frequency, and target task. The features of each approach are summarized, and we discuss the possible future of this area
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Soft pneumatic actuators: a review of design, fabrication, modeling, sensing, control and applications
Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable materials and usually follow a bioinspired design. Their high dexterity and safety make them ideal for applications such as gripping, locomotion, and biomedical devices, where the environment is highly dynamic and sensitive to physical interaction. Pneumatic actuation remains the dominant technology in soft robotics due to its low cost and mass, fast response time, and easy implementation. Given the significant number of publications in soft robotics over recent years, newcomers and even established researchers may have difficulty assessing the state of the art. To address this issue, this article summarizes the development of soft pneumatic actuators and robots up until the date of publication. The scope of this article includes the design, modeling, fabrication, actuation, characterization, sensing, control, and applications of soft robotic devices. In addition to a historical overview, there is a special emphasis on recent advances such as novel designs, differential simulators, analytical and numerical modeling methods, topology optimization, data-driven modeling and control methods, hardware control boards, and nonlinear estimation and control techniques. Finally, the capabilities and limitations of soft pneumatic actuators and robots are discussed and directions for future research are identified