A Pneumatic Tactile Sensor for Co-Operative Robots

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.

Human Body Parts Proximity Measurement Using Distributed Tactile Robotic Skin

Safety in human–machine cooperation is the current challenge in robotics. Safe human–robot interaction requires the development of sensors that detect human presence in the robot’s workspace. Detection of this presence should occur before the physical collision of the robot with the human. Human to robot proximity detection should be very fast, allowing machine elements deceleration to velocities safe for human–machine collision. The paper presents a new, low-cost design of distributed robotic skin, which allows real-time measurements of the human body parts proximity. The main advantages of the proposed solution are low cost of its implementation based on comb electrodes matrix and real-time operation due to fast and simple electronic design. The main contribution is the new idea of measuring the distance to human body parts by measuring the operating frequency of a rectangular signal generator, which depends on the capacity of the open capacitor. This capacitor is formed between the comb electrodes matrix and a reference plate located next to the matrix. The capacitance of the open capacitor changes if a human body part is in vicinity. The application of the developed device can be very wide. For example, in the field of cooperative robots, it can lead to the improvement of human–machine interfaces and increased safety of human–machine cooperation. The proposed construction can help to meet the increasing requirements for cooperative robots.

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object’s location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

A versatile 4D capacitive imaging array: a touchless skin and an obstacle-avoidance sensor for robotic applications

There are growing interests in the use of robots in collaborative environments with humans or other intelligent machines. Sensing the environment for which the robot is operating can be done in many ways, generally guided by skin-like sensors. Some of the skins are inspired by natural sensing in humans or other species. As humans, we use many of our senses, such as version, hearing, smell, and touch, to move around by avoiding colliding with other humans or objects. Different from humans, many other mammals also use whiskers as an additional sensor to help navigate around. In this paper, we demonstrate a touchless capacitive imaging-based sensor in the situation where the obstacles are in close vicinity to the robot. The proposed imaging system can sense the changes in areas near to the skin-like sensors by measuring the capacitances between the array of electrodes. A 4D sensing approach has been developed with the spatiotemporal Total Variation algorithm. The 4D operational mode gives sensors the time awareness that allows for dynamical responses and hence the better control of the robots. Several experiments are conducted to show the skin-like behaviour of this sensor by simulating various scenarios. The sensor shows the excellent ability to detect an object in its vicinity, where the depth is close to half of the planar sensor array size.

Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation

In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripper is presented. A haptic exploratory procedure based on robotic palpation is performed to get pressure images at different grasping forces that provide information not only about the external shape of the object, but also about its internal features. The gripper consists of two underactuated fingers with a tactile sensor array in the thumb. A new representation of tactile information as 3D tactile tensors is described. During a squeeze-and-release process, the pressure images read from the tactile sensor are concatenated forming a tensor that contains information about the variation of pressure matrices along with the grasping forces. These tensors are used to feed a 3D Convolutional Neural Network (3D CNN) called 3D TactNet, which is able to classify the grasped object through active interaction. Results show that 3D CNN performs better, and provide better recognition rates with a lower number of training data.

Tactile Robotic Skin with Pressure Direction Detection

Tactile sensing is the current challenge in robotics and object manipulation by machines. The robot's agile interaction with the environment requires pressure sensors to detect not only location and value, but also touch direction. The paper presents a new, two-layer construction of artificial robotic skin, which allows measuring the location, value, and direction of pressure from external force. The main advantages of the proposed solution are its low cost of implementation based on two FSR (Force Sensitive Resistor) matrices and real-time operation thanks to direction detection using fast matching algorithms. The main contribution is the idea of detecting the pressure direction by determining the shift between the pressure maps of the skin's upper and lower layers. The pressure map of each layer is treated as an image and registered using a phase correlation (POC-Phase Only Correlation) method. The use of the developed device can be very wide. For example, in the field of cooperative robots, it can lead to the improvement of human machine interfaces and increased security of human-machine cooperation. The proposed construction can help meet the increasing requirements for robots in cooperation with humans, but also enable agile manipulation of objects from their surroundings.