näkemys - Robotics Engineering - # Kinematic modeling of tendon-actuated continuum robots

Modeling Hysteretic Kinematics in Tendon-Actuated Continuum Robots using Neural Networks

Q: How can the neural network models be extended to handle multi-tendon continuum robots, where the coupling between tendons introduces additional complexities

To extend neural network models to handle multi-tendon continuum robots with coupling between tendons, several approaches can be considered. One way is to incorporate the interactions between tendons by designing the neural network architecture to account for the coupled dynamics. This can involve creating separate branches within the network for each tendon and then integrating the outputs to capture the overall system behavior. Additionally, recurrent neural networks (RNNs) or Long Short-Term Memory (LSTM) networks can be utilized to model the temporal dependencies and interactions between tendons over time. By feeding historical tendon data into the network, it can learn the complex relationships and dependencies between multiple tendons in the continuum robot. Furthermore, attention mechanisms can be employed to focus on specific tendon interactions at different time steps, enhancing the network's ability to capture the coupling effects between tendons. Overall, by adapting the neural network architecture and training strategies to account for multi-tendon systems, the models can effectively handle the additional complexities introduced by tendon coupling in continuum robots.

Q: What other machine learning techniques, beyond neural networks, could be explored to model the hysteretic kinematics of continuum robots

Beyond neural networks, other machine learning techniques can be explored to model the hysteretic kinematics of continuum robots. One such approach is Gaussian Processes (GPs), which are probabilistic models capable of capturing complex nonlinear relationships in data. GPs can be used to model the hysteresis behavior by learning the underlying patterns and uncertainties in the kinematic data. Another technique is Support Vector Machines (SVMs), which can effectively model nonlinear relationships and have been applied in various regression tasks. SVMs can be trained to capture the hysteresis in continuum robot kinematics by optimizing the margin between data points. Additionally, evolutionary algorithms such as Genetic Algorithms (GAs) can be employed to optimize the parameters of physics-based hysteresis models, providing a data-driven approach to modeling the complex behavior of continuum robots. By exploring a combination of these techniques with neural networks, a comprehensive and robust modeling framework can be developed to accurately represent the hysteretic kinematics of continuum robots.

Q: How can the insights from this work on kinematic modeling be applied to improve the control and planning of tendon-actuated continuum robots in real-world applications

The insights gained from the work on kinematic modeling of tendon-actuated continuum robots can be applied to enhance the control and planning of these robots in real-world applications. By utilizing the developed neural network models, real-time control algorithms can be implemented to adapt to the hysteresis and rate-dependent behaviors exhibited by the robots. The models can be integrated into feedback control systems to improve the accuracy and efficiency of robot motion, especially in tasks requiring precise positioning and manipulation. Furthermore, the kinematic models can inform trajectory planning algorithms to optimize the robot's path based on historical dependencies and hysteresis characteristics. This can lead to more efficient and reliable robot movements, particularly in medical procedures where accuracy and safety are paramount. Overall, leveraging the insights from kinematic modeling can significantly enhance the performance and capabilities of tendon-actuated continuum robots in various practical applications.

Keskeiset käsitteet

Neural networks can effectively model the hysteretic kinematic behavior of tendon-actuated continuum robots, outperforming standard feedforward neural networks by capturing temporal dependencies.

Tiivistelmä

The paper investigates the use of neural networks to model the hysteretic kinematic behavior of two types of tendon-actuated continuum robots. Key highlights:

The hysteretic behavior and optimal modeling approach depend on the robot design. The robot with a superelastic backbone exhibits rate-independent hysteresis when modeled with tendon tension as input, but not when using tendon displacement. In contrast, the clinical cardiac catheter robot shows rate-dependent hysteresis.
Feedforward neural networks (FNNs) without a history buffer cannot capture the temporal dependencies and hysteretic effects. FNNs with a history input buffer and long short-term memory (LSTM) networks demonstrate comparable performance in modeling both rate-independent and rate-dependent hysteresis.
The paper presents a pre-tensioning calibration procedure to ensure repeatable kinematic behavior of the clinical catheter robot, which is crucial for accurate modeling.
Experimental results show the FNN-HIB and LSTM models outperform the standard FNN in capturing the hysteretic kinematics, with comparable performance between the two advanced models.

The findings highlight the importance of considering the robot design and the appropriate neural network architecture to effectively model the hysteretic kinematics of tendon-actuated continuum robots.

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Tilastot

The robot with superelastic backbone exhibits up to 7% variation in tip position depending on the approach path in joint space.
The clinical cardiac catheter robot shows rate-dependent hysteresis at frequencies above 0.3 Hz, with the actuator-induced lag causing the maximum bending angle to decrease at higher frequencies.

Lainaukset

"In contrast to FNNs, RNNs are designed to model systems that exhibit dependencies on historical inputs. Consequently, they can directly be used to model both nonlinear and hysteretic effects that are neglected in mechanics-based models."
"For example, LSTMs have been used to model nonlinear elasticity and friction in the tendons of robots with rigid links and in flexible endoscopic robots to estimate tendon force at the robot tip."

Tärkeimmät oivallukset

Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots

by Yuan Wang,Ma... klo arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07168.pdf

Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots

Syvällisempiä Kysymyksiä

How can the neural network models be extended to handle multi-tendon continuum robots, where the coupling between tendons introduces additional complexities

To extend neural network models to handle multi-tendon continuum robots with coupling between tendons, several approaches can be considered. One way is to incorporate the interactions between tendons by designing the neural network architecture to account for the coupled dynamics. This can involve creating separate branches within the network for each tendon and then integrating the outputs to capture the overall system behavior. Additionally, recurrent neural networks (RNNs) or Long Short-Term Memory (LSTM) networks can be utilized to model the temporal dependencies and interactions between tendons over time. By feeding historical tendon data into the network, it can learn the complex relationships and dependencies between multiple tendons in the continuum robot. Furthermore, attention mechanisms can be employed to focus on specific tendon interactions at different time steps, enhancing the network's ability to capture the coupling effects between tendons. Overall, by adapting the neural network architecture and training strategies to account for multi-tendon systems, the models can effectively handle the additional complexities introduced by tendon coupling in continuum robots.

What other machine learning techniques, beyond neural networks, could be explored to model the hysteretic kinematics of continuum robots

Beyond neural networks, other machine learning techniques can be explored to model the hysteretic kinematics of continuum robots. One such approach is Gaussian Processes (GPs), which are probabilistic models capable of capturing complex nonlinear relationships in data. GPs can be used to model the hysteresis behavior by learning the underlying patterns and uncertainties in the kinematic data. Another technique is Support Vector Machines (SVMs), which can effectively model nonlinear relationships and have been applied in various regression tasks. SVMs can be trained to capture the hysteresis in continuum robot kinematics by optimizing the margin between data points. Additionally, evolutionary algorithms such as Genetic Algorithms (GAs) can be employed to optimize the parameters of physics-based hysteresis models, providing a data-driven approach to modeling the complex behavior of continuum robots. By exploring a combination of these techniques with neural networks, a comprehensive and robust modeling framework can be developed to accurately represent the hysteretic kinematics of continuum robots.

How can the insights from this work on kinematic modeling be applied to improve the control and planning of tendon-actuated continuum robots in real-world applications

The insights gained from the work on kinematic modeling of tendon-actuated continuum robots can be applied to enhance the control and planning of these robots in real-world applications. By utilizing the developed neural network models, real-time control algorithms can be implemented to adapt to the hysteresis and rate-dependent behaviors exhibited by the robots. The models can be integrated into feedback control systems to improve the accuracy and efficiency of robot motion, especially in tasks requiring precise positioning and manipulation. Furthermore, the kinematic models can inform trajectory planning algorithms to optimize the robot's path based on historical dependencies and hysteresis characteristics. This can lead to more efficient and reliable robot movements, particularly in medical procedures where accuracy and safety are paramount. Overall, leveraging the insights from kinematic modeling can significantly enhance the performance and capabilities of tendon-actuated continuum robots in various practical applications.