Exploration and Reconstruction of Unknown Objects using a Novel Normal and Contact Sensor

Ottenhaus, Simon; Weiner, Pascal; Kaul, Lukas; Tulbure, Andreea; Asfour, Tamim

doi:10.1109/iros.2018.8594272

Cited by 4 publications

(8 citation statements)

References 34 publications

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“…(a) Model reconstruction by tactile perception. 66 (b) Object stabilization by BioTac and independent tactile grip stabilization controller. 67 (c) A classification model combines tactile and visual.…”

Section: Perception For Dexterous Manipulationmentioning

confidence: 99%

“…There have been some ways to combine touch with other sensors to explore objects. Ottenhaus et al 66 introduced a combined sensor concept consisting of an inertial measurement unit and a pressure to detect tactile sensing of the surface normal. With the proposed combined sensor, they reconstruct the surface of an unknown object with a low error.…”

Section: Planning-level Perceptionmentioning

confidence: 99%

“…Example for planning-level perception. (a) Model reconstruction by tactile perception 66. (b) Object stabilization by BioTac and independent tactile grip stabilization controller 67.…”

mentioning

confidence: 99%

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A review on sensory perception for dexterous robotic manipulation

Xia¹,

Deng

Fang

et al. 2022

International Journal of Advanced Robotic Systems

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Sensory perception for dexterous robotic hands is an active research area and recent progress in robotics. Effective dexterous manipulation requires robotic hands to accurately feedback their state or perceive the surrounding environment. This article reviews the state-of-the-art of sensory perception for dexterous robotic manipulation. Two types of sensors, such as intrinsic and extrinsic sensors, are introduced according to their function and layout in robotic hands. These sensors provide rich information to a robotic hand, which contains the posture, the contact information of objects, and the physical information of the environment. Then, a comprehensive analysis of perception methods including planning-level, control-level, and learning-level perceptions is presented. The information obtained from sensory perception can help robotic hands to make decisions effectively. Previously issued reviews mainly focus on the design of tactile senor, while we analyze and discuss the relationship among sensing, perception, and dexterous manipulation. Some potential research topics on sensory perception are also summarized and discussed.

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Section: Perception For Dexterous Manipulationmentioning

confidence: 99%

Section: Planning-level Perceptionmentioning

confidence: 99%

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A review on sensory perception for dexterous robotic manipulation

Xia¹,

Deng

Fang

et al. 2022

International Journal of Advanced Robotic Systems

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“…The algorithm classifies the force gradient to decide when to release the object in a handover context. Ottenhaus et al (Ottenhaus et al (2016), Ottenhaus et al (2018)) proposed a method for haptic exploration through implicit surface modelling. Weiss-Robotics (2020.…”

Section: Low-frequency Responsementioning

confidence: 99%

“…Weiss-Robotics (2020. [Online]) composed of 32 capacitive cells uniformly distributed across the contact surface were used for collecting the tactile data (Ottenhaus et al (2016), Ottenhaus et al (2018)). These authors expanded the work initially presented in Ottenhaus et al (2016) to propose a contact and surface orientation sensor concept, again for surface reconstruction, with a claimed mean reconstruction accuracy of 3.8mm (Ottenhaus et al (2018)).…”

Section: Low-frequency Responsementioning

confidence: 99%

Exploiting High Quality Tactile Sensors for Simplified Grasping

Machado¹,

McGinnity²

2022

Preprint

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Robots are expected to grasp a wide range of objects varying in shape, weight or material type. Providing robots with tactile capabilities similar to humans is thus essential for applications involving human-to-robot or robot-to-robot interactions, particularly in those situations where a robot is expected to grasp and manipulate complex objects not previously encountered. A critical aspect for successful object grasp and manipulation is the use of high-quality fingertips equipped with multiple high-performance sensors, distributed appropriately across a specific contact surface.In this paper we present a detailed analysis of the use of two different types of commercially available robotic fingertips (BioTac and WTS-FT), each of which is equipped with multiple sensors distributed across the fingertips' contact surface. We further demonstrate that, due to the high performance of the fingertips, a complex adaptive grasping algorithm is not required for grasping of everyday objects. We conclude that a simple algorithm based on a proportional controller will suffice for many grasping applications, provided the relevant fingertips exhibit high sensitivity. In a quantified assessment, we also demonstrate that, due in part to the sensor distribution, the BioTac-based fingertip performs better than the WTS-FT device, in enabling lifting of loads up to 850g, and that the simple proportional controller can adapt the grasp even when the object is exposed to significant external vibrational challenges.

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Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

Martinez-Hernandez

2023

IEEE Sensors J.

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In this paper, we present a low-cost multimodal tactile sensor capable of providing accelerometer, gyroscope and pressure data using a 7-axis chip as sensing element. This approach reduces the complexity of tactile sensor design and collection of multimodal data. The tactile device is composed of a top layer (a printed circuit board and sensing element), a middle layer (soft rubber material), and bottom layer (plastic base) forming a sandwich structure. This approach allows the measurement of multimodal data when force is applied on different parts of the top layer of the sensor. The multimodal tactile sensor is validated with analyses and experiments in both offline and real-time. First, the spatial impulse response and sensitivity of the sensor are analysed with accelerometer, gyroscope and pressure data systematically collected from the sensor. Second, estimation of contact location from a range of sensor positions and force values is evaluated using accelerometer and gyroscope data together with a Convolutional Neural Network (CNN) method. Third, the estimation of contact location is used to control the position of a robot arm. The results show that the proposed multimodal tactile sensor has the potential for robotic applications such as tactile perception for robot control, human-robot interaction and object exploration.

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Exploration and Reconstruction of Unknown Objects using a Novel Normal and Contact Sensor

Cited by 4 publications

References 34 publications

A review on sensory perception for dexterous robotic manipulation

A review on sensory perception for dexterous robotic manipulation

Exploiting High Quality Tactile Sensors for Simplified Grasping

Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

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