13 research outputs found
Hybrid Data-Driven and Analytical Model for Kinematic Control of a Surgical Robotic Tool
Accurate kinematic models are essential for effective control of surgical
robots. For tendon driven robots, which is common for minimally invasive
surgery, intrinsic nonlinearities are important to consider. Traditional
analytical methods allow to build the kinematic model of the system by making
certain assumptions and simplifications on the nonlinearities. Machine learning
techniques, instead, allow to recover a more complex model based on the
acquired data. However, analytical models are more generalisable, but can be
over-simplified; data-driven models, on the other hand, can cater for more
complex models, but are less generalisable and the result is highly affected by
the training dataset. In this paper, we present a novel approach to combining
analytical and data-driven approaches to model the kinematics of nonlinear
tendon-driven surgical robots. Gaussian Process Regression (GPR) is used for
learning the data-driven model and the proposed method is tested on both
simulated data and real experimental data
Dual-Rate Extended Kalman Filter Based Path-Following Motion Control for an Unmanned Ground Vehicle: Realistic Simulation
[EN] In this paper, a two-wheel drive unmanned ground vehicle (UGV) path-following motion control is proposed. The UGV is equipped with encoders to sense angular velocities and a beacon system which provides position and orientation data. Whereas velocities can be sampled at a fast rate, position and orientation can only be sensed at a slower rate. Designing a dynamic controller at this slower rate implies not reaching the desired control requirements, and hence, the UGV is not able to follow the predefined path. The use of dual-rate extended Kalman filtering techniques enables the estimation of the fast-rate non-available position and orientation measurements. As a result, a fast-rate dynamic controller can be designed, which is provided with the fast-rate estimates to generate the control signal. The fast-rate controller is able to achieve a satisfactory path following, outperforming the slow-rate counterpart. Additionally, the dual-rate extended Kalman filter (DREKF) is fit for dealing with non-linear dynamics of the vehicle and possible Gaussian-like modeling and measurement uncertainties. A Simscape Multibody (TM) (Matlab(R)/Simulink) model has been developed for a realistic simulation, considering the contact forces between the wheels and the ground, not included in the kinematic and dynamic UGV representation. Non-linear behavior of the motors and limited resolution of the encoders have also been included in the model for a more accurate simulation of the real vehicle. The simulation model has been experimentally validated from the real process. Simulation results reveal the benefits of the control solution.Grant RTI2018-096590-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF Away of making Europe" and Grant PRE2019-088467 funded by MCIN/AEI/10.13039/501100011033 and by "ESF Investing in your future".Carbonell-Lázaro, R.; Cuenca, Á.; Casanova Calvo, V.; Pizá, R.; Salt Llobregat, JJ. (2021). Dual-Rate Extended Kalman Filter Based Path-Following Motion Control for an Unmanned Ground Vehicle: Realistic Simulation. Sensors. 21(22):1-17. https://doi.org/10.3390/s21227557117212
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
Model-based and Model-Free Robot Control : A Review
Robot control is one of the key aspects of robotics research. Models are essential tools in robotics, such as the robot’s own body dynamics and kinematics models, actuator/motor models, and the models of external controllable objects. In this paper, we review the latest advances in model-based and model-free ap-proaches with a strong focus on robot control. Based on the designed search strategy, several prevailing control approaches are classified and discussed ac-cording to their control strategies. An insight into the gripper control is also explored. Then the research problems and applicability of the control methods are discussed by investigating their merits and demerits. Based on the discussion, we summarize the challenges and future research trends of robot control
Soft Robot-Assisted Minimally Invasive Surgery and Interventions: Advances and Outlook
Since the emergence of soft robotics around two decades ago, research interest in the field has escalated at a pace. It is fuelled by the industry's appreciation of the wide range of soft materials available that can be used to create highly dexterous robots with adaptability characteristics far beyond that which can be achieved with rigid component devices. The ability, inherent in soft robots, to compliantly adapt to the environment, has significantly sparked interest from the surgical robotics community. This article provides an in-depth overview of recent progress and outlines the remaining challenges in the development of soft robotics for minimally invasive surgery
Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms