Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment

Kamtikar, Shivani Kiran; Marri, Samhita; Walt, Benjamin Thomas; Uppalapati, Naveen Kumar; Krishnan, Girish; Chowdhary, Girish

doi:10.1109/lra.2022.3155821

Cited by 12 publications

(2 citation statements)

References 22 publications

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“…CNNs are also employed for estimating the pose difference between current and desired images, specifically in the category of Pose-based Visual Servoing (PBVS). PBVS relies on image information to estimate the camera pose, and CNNs play a crucial role in this process [11], [12].…”

Section: Introduction a Motivationmentioning

confidence: 99%

A Study on Learned Feature Maps Toward Direct Visual Servoing

Quaccia,

André,

Yoshiyasu

et al. 2024

2024 IEEE/SICE International Symposium on System Integration (SII)

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Direct Visual Servoing (DVS) is a technique used in robotics and computer vision where visual information, typically obtained from camera pixels brightness, is directly used for controlling the motion of a robot. DVS is known for its ability to achieve accurate positioning, thanks to the redundancy of information all without the necessity to rely on geometric features.In this paper, we introduce a novel approach where pixel brightness is replaced with learned feature maps as the visual information for the servoing loop. The aim of this paper is to present a procedure to extract, transform and integrate deep neural networks feature maps toward replacing the brightness in a DVS control loop.

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Section: Introduction a Motivationmentioning

confidence: 99%

A Study on Learned Feature Maps Toward Direct Visual Servoing

Quaccia,

André,

Yoshiyasu

et al. 2024

2024 IEEE/SICE International Symposium on System Integration (SII)

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“…Equipping soft robots with artificial sensing is nowadays a thriving field of research [4]. Vision has been implemented both with cameras at the tip in an eye in hand fashion [5], [6], and with external cameras, whose frames are usually used as ground truth data for machine learning schemes. Self sensing, or proprioception, has been implemented with a variety of sensors either embedded [7], [8], [9], [10], placed at the robot's base [11], or on the robot's body as a skin [12].…”

Section: Introductionmentioning

confidence: 99%

Smell Driven Navigation for Soft Robotic Arms: Artificial Nose and Control

Stella

Hughes

Falotico

et al. 2023

2023 IEEE International Conference on Soft Robotics (RoboSoft)

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Elephants and other animals heavily rely on the sense of smell to operate. Soft robots would also benefit from an artificial sense of smell, which could be helpful in typical soft robotic tasks such as search and rescue, pipe inspection, and all the tasks involving unstructured environments. This work proposes an artificial nose on a soft robotic arm that ensures separate smell concentration readings. We propose designing the nose to generate a one-to-one matching between the sensors' inputs and the actuators. This design choice allows us to implement a simple control strategy tailored to reach a dynamically varying smell in the environment, which we validate on a two-segment tendon-driven soft robotic arm equipped with the proposed artificial nose. We also propose and validate in simulation a control strategy for reaching tasks in the case of a stationary smell.

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Deep Learning Methods in Soft Robotics: Architectures and Applications

Čakurda,

Trojanová,

Pomin

et al. 2024

Advanced Intelligent Systems

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The area of soft robotics has been subject to intense research efforts in the past two decades and constitutes a paradigm for advanced machine design in future robotic applications. However, standard methods for industrial robotics may be difficult to apply when analyzing soft robots. Deep learning, which has undergone rapid and transformative advancements in recent years, offers a set of powerful tools for analyzing and designing complex soft machines capable of operating in unstructured environments and interacting with humans and objects in a delicate manner. This review summarizes the most important state‐of‐the‐art deep learning architectures classified under supervised, unsupervised, semisupervised, and reinforcement learning scenarios and discusses their main features and benefits for different soft robotic applications, including soft robot manipulators, soft grippers, soft sensors, and e‐skins, as well as bioinspired soft robots. Specific properties of recent deep learning architectures and the usefulness of their features in addressing various types of issues found in soft robotics are analyzed. The existing challenges and future prospects are identified and discussed in view of the enhanced integration of both areas, which improves the performance of next‐generation soft machines operating in real‐world conditions.

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Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment

Cited by 12 publications

References 22 publications

A Study on Learned Feature Maps Toward Direct Visual Servoing

A Study on Learned Feature Maps Toward Direct Visual Servoing

Smell Driven Navigation for Soft Robotic Arms: Artificial Nose and Control

Deep Learning Methods in Soft Robotics: Architectures and Applications

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