32 research outputs found
Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning
This paper presents an efficient learning-based method to solve the inverse
kinematic (IK) problem on soft robots with highly non-linear deformation. The
major challenge of efficiently computing IK for such robots is due to the lack
of analytical formulation for either forward or inverse kinematics. To address
this challenge, we employ neural networks to learn both the mapping function of
forward kinematics and also the Jacobian of this function. As a result,
Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real
training transfer strategy is conducted to make this approach more practical.
We first generate a large number of samples in a simulation environment for
learning both the kinematic and the Jacobian networks of a soft robot design.
Thereafter, a sim-to-real layer of differentiable neurons is employed to map
the results of simulation to the physical hardware, where this sim-to-real
layer can be learned from a very limited number of training samples generated
on the hardware. The effectiveness of our approach has been verified on
pneumatic-driven soft robots for path following and interactive positioning
An Online Model-Free Adaptive Tracking Controller for Cable-Driven Medical Continuum Manipulators
Continuum manipulators have demonstrated promising potential for flexible access and complicated operation and thus have been emerging and introduced in robot-assisted flexible endoscopy. However, due to their inherent structural compliance and strong nonlinearities, developing an accurate and robust control framework remains challenging. This paper proposes a model-free control method based on the Model-Free Adaptive Control (MFAC) algorithm to accomplish the trajectory tracking for two kinds of continuum manipulators by solely utilizing the robotic system’s real-time input/output data. The presented controller discretizes and dynamically linearizes the motion process of the continuum actuator to obtain a dynamic linearization data (DLD) model. This DLD model can be derived from a pseudo-partial derivative (PPD) matrix updated based on the I/O measurement data for the iterative operation. The stability of the presented MFAC controller can be mathematically guaranteed in theory to provide generality, and the control framework demonstrates a low computational cost and real-time control capability. The superior performance of the presented controller is firstly validated in MATLAB simulations and then compared with the other two controllers. Through experimental validation on two kinds of continuum manipulators, the model-free control framework shows high tracking accuracy and good robustness against the system uncertainty and external disturbances, as well as high transferability
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
Design and validation of a novel fuzzy-logic-based static feedback controller for tendon-driven continuum robots
10.13039/100013406-Aerospace Technology Institute; 10.13039/501100000266-Engineering and Physical Sciences Research Council
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 —This 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
Robust control of a silicone soft robot using neural networks
International audienceThis paper deals with the robust controller design problem to regulate the position of a soft robot with elastic behavior, driven by 4 cable actuators. In this work, we first used an artificial neural network to approximate the relation between these actuators and the controlled position of the soft robot, based on which two types of robust controllers (type of integral and sliding mode) are proposed. The effectiveness and the robustness of the proposed controllers have been analyzed both for the constant and the time-varying disturbances. The performances (precision, convergence speed and robustness) of the proposed method have been validated via different experimental tests