Contact Localization of Continuum and Flexible Robot Using Data-Driven Approach

Ha, Xuan Thao; Wu, Di; Lai, Chun-Feng; Ourak, Mouloud; Borghesan, Gianni; Menciassi, Arianna; Poorten, Emmanuel Vander

doi:10.1109/lra.2022.3176723

Cited by 17 publications

(12 citation statements)

References 31 publications

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“…Each module is composed of multiple time-of-flight sensors to detect an object and map the environment as well as Hall effect sensors and magnets to detect contact and force [ 64 ]. A data-driven method for contact localization is presented in [ 62 ]. The approach presented in the paper uses a machine learning technique called AutoEncoders [ 74 ], which is a type of neural network.…”

Section: Classificationmentioning

confidence: 99%

Sensing of Continuum Robots: A Review

Sincak,

Prada,

Miková

et al. 2024

Sensors

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The field of continuum robotics is rapidly developing. The development of new kinematic structures, locomotion principles and control strategies is driving the development of new types of sensors and sensing methodologies. The sensing in continuum robots can be divided into shape perception and environment perception. The environment perception is focusing on sensing the interactions between the robot and environment. These sensors are often embedded on an outer layer of the robots, so the interactions can be detected. The shape perception is sensing the robot’s shape using various principles. There are three main groups of sensors that use the properties of electricity, magnetism and optics to measure the shape of the continuum robots. The sensors based on measuring the properties of electricity are often based on measuring the electrical resistance or capacitance of the flexible sensor. Sensors based on magnetism use properties of permanent magnets or coils that are attached to the robot. Their magnetic field, flux or other properties are then tracked, and shape reconstruction can be performed. The last group of sensors is mostly based on leveraging the properties of traveling light through optical fibers. There are multiple objectives of this work. Objective number one is to clearly categorize the sensors and make a clear distinction between them. Objective number two is to determine the trend and progress of the sensors used in continuum robotics. And finally, the third objective is to define the challenges that the researchers are currently facing. The challenges of sensing the shape or the interaction with the environment of continuum robots are currently in the miniaturization of existing sensors and the development of novel sensing methods.

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Section: Classificationmentioning

confidence: 99%

Sensing of Continuum Robots: A Review

Sincak,

Prada,

Miková

et al. 2024

Sensors

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“…Note that the proposed methods can be generalized to other sensors that are able to provide real-time estimates of the catheter's tip pose such as fluoroscopy [22], Electrical impedance tomography [23], bending resistance [24] or Fiber Bragg Grating-based shape sensing method [25], [26]. However, in this work, the EM tracker serves as a candidate that can track tip position in order to enable compliant motion control.…”

Section: B Compliant Motion Control Algorithmmentioning

confidence: 99%

Deep-Learning-Based Compliant Motion Control of a Pneumatically-Driven Robotic Catheter

Zhang

et al. 2022

IEEE Robot. Autom. Lett.

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In cardiovascular interventions, when steering catheters and especially robotic catheters, great care should be paid to prevent applying too large forces on the vessel walls as this could dislodge calcifications, induce scars or even cause perforation. To address this challenge, this paper presents a novel compliant motion control algorithm that relies solely on position sensing of the catheter tip and knowledge of the catheter's behavior. The proposed algorithm features a data-driven tip position controller. The controller is trained based on a so-called control Long Short-Term Memory Network (control-LSTM). Trajectory following experiments are conducted to validate the quality of the proposed control-LSTM. Results demonstrated superior positioning capability with sub-degree precision of the new approach in the presence of severe rate-dependent hysteresis. Experiments both in a simplified setup as well as in an aortic phantom further show that the proposed approach allows reducing the interaction forces with the environment by around 70%. This work shows how deep learning can be exploited advantageously to avoid tedious modeling that would be needed to precisely steer continuum robots in constrained environments such as the patient's vasculature.

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“…Soft tactile sensors are mainly designed using biological inspiration from human skin, which has distributed mechanical receptors that can sense force, stiffness, temperature, texture, and vibration [8], [9], [10]. These sensors are based on optical [11], [12], [13], [14], resistive [15], [16], [17], capacitive [18], [19], [20], [21], magnetic [22], [23], [24], [25], inductive [26], [27], [28] and piezoelectric [29], [30], [31], [32], [33] principles. Besides these soft tactile sensors, vision-based sensors are also used for tactile perception in robotics [34], [35], [36], [37], [38], [39].…”

Section: A Related Workmentioning

confidence: 99%

“…They applied machine learning algorithms for contact localization estimation, and the best result was achieved with a maximum error of 7.5 mm with an accuracy of 91.1% using SVM. Another optical-based sensor was studied by Thao Ha et al [14]. They placed fibers into a flexible continuum robot and estimated contact localization using data-driven machine learning and a KNN algorithm.…”

Section: A Related Workmentioning

confidence: 99%

Development of a Soft Tactile Sensor Array for Contact Localization Estimations

Kalafat

Yıldız

2022

IEEE Access

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Contact localization is an essential feature of tactile sensors for biomedical, rehabilitation, surgical, and service robot applications where interaction with the environment is needed. In this work, we have developed a piezoelectric (PZT) based soft tactile sensor using the layer-by-layer fabrication technique. We placed 4 × 4 mm 2 size PZT elements in 3 × 3 matrix form with 3 mm spaces and embedded them in silicone layers. Although the sensor has discrete brittle sensing elements, the silicone layer distributes the contact force on the surface area, which enables continuous contact localization on the sensor. Here, we studied the estimation using the collected and conditioned output voltage signals of PZT elements. First, we used a logic-based algorithm that gives results qualitatively using a developed user interface. Then we used machine learning algorithms to estimate the contact position coordinates numerically. Finally, we applied Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), and k-Nearest Neighbor (KNN) algorithms and compared the results. We got our best results using a two-layer ANN algorithm which provides a mean deviation of 0.08 mm for the X-axis and 0.07 mm for the Y-axis. Besides, we had 97.20% of testing estimations with an error lower than 0.35 mm. Therefore, we believe our tactile sensor gives promising results and can be used in small-scale applications where contact localization estimation is desired.

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Contact Localization of Continuum and Flexible Robot Using Data-Driven Approach

Cited by 17 publications

References 31 publications

Sensing of Continuum Robots: A Review

Sensing of Continuum Robots: A Review

Deep-Learning-Based Compliant Motion Control of a Pneumatically-Driven Robotic Catheter

Development of a Soft Tactile Sensor Array for Contact Localization Estimations

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