Tactile data perception is of paramount importance in today’s robotics applications. This paper describes the latest design of the tactile sensor developed in our laboratory. Both the hardware and firmware concepts are reported in detail in order to allow the research community the sensor reproduction, also according to their needs. The sensor is based on optoelectronic technology and the pad shape can be adapted to various robotics applications. A flat surface, as the one proposed in this paper, can be well exploited if the object sizes are smaller than the pad and/or the shape recognition is needed, while a domed pad can be used to manipulate bigger objects. Compared to the previous version, the novel tactile sensor has a larger sensing area and a more robust electronic, mechanical and software design that yields less noise and higher flexibility. The proposed design exploits standard PCB manufacturing processes and advanced but now commercial 3D printing processes for the realization of all components. A GitHub repository has been prepared with all files needed to allow the reproduction of the sensor for the interested reader. The whole sensor has been tested with a maximum load equal to $15$N, by showing a sensitivity equal to $0.018$V/N. Moreover, a complete and detailed characterization for the single taxel and the whole pad is reported to show the potentialities of the sensor also in terms of response time, repeatability, hysteresis and signal to noise ratio.

Robust manipulation of mechanical parts in different grasping configurations is a challenging problem in autonomous robotic assembly that can be overcome by adopting suitable mechatronic solutions. This article proposes a tactile-sensor-based approach that exploits in-hand pose estimation and contact perception to compensate for unavoidable picking, placing, and insertion errors that may occur during task assembly execution under uncertain/perturbed conditions. The main objective of this work is to demonstrate how the use of tactile data, together with both machine learning and model-based methods, allows us to obtain an advanced system able to successfully complete a task that requires the manipulation of boltlike fasteners with different shapes and grasped in different poses. Experiments carried out using the proposed robotic system are reported for a specific assembly task in order to evaluate the effectiveness of the proposed solution. By means of suitable calibration procedures exploiting the same methods proposed here, the system can be easily adapted to different objects and shapes.

In robotic grasping and manipulation, the knowledge of a precise object pose represents a key issue. The point acquires even more importance when the objects and, then, the grasping areas become smaller. This is the case of Deformable Linear Object manipulation application where the robot shall autonomously work with thin wires which pose and shape estimation could become difficult given the limited object size and possible occlusion conditions. In such applications, a vision-based system could not be enough to obtain accurate pose and shape estimation. In this work the authors propose a Time-of-Flight pre-touch sensor, integrated with a previously designed tactile sensor, for an accurate estimation of thin wire pose and shape. The paper presents the design and the characterization of the proposed sensor. Moreover, a specific object scanning and shape detection algorithm is presented. Experimental results support the proposed methodology, showing good performance. Hardware design and software applications are freely accessible to the reader.

At present, the tactile perception is essential for robotic applications when performing complex manipulation tasks, e.g., grasping objects of different shapes and sizes, distinguishing between different textures, and avoiding slips by grasping an object with a minimal force. Considering Deformable Linear Object manipulation applications, this paper presents an efficient and straightforward method to allow robots to autonomously work with thin objects, e.g., wires, and to recognize their features, i.e., diameter, by relying on tactile sensors developed by the authors. The method, based on machine learning algorithms, is described in-depth in the paper to make it easily reproducible by the readers. Experimental tests show the effectiveness of the approach that is able to properly recognize the considered object’s features with a recognition rate up to 99.9%. Moreover, a pick and place task, which uses the method to classify and organize a set of wires by diameter, is presented.

Introduction: The rapid advancement of collaborative robotics has driven significant interest in Human-Robot Interaction (HRI), particularly in scenarios where robots work alongside humans. This paper considers tasks where a human operator teaches the robot an operation that is then performed autonomously.Methods: A multi-modal approach employing tactile fingers and proximity sensors is proposed, where tactile fingers serve as an interface, while proximity sensors enable end-effector movements through contactless interactions and collision avoidance algorithms. In addition, the system is modular to make it adaptable to different tasks.Results: Demonstrative tests show the effectiveness of the proposed system and algorithms. The results illustrate how the tactile and proximity sensors can be used separately or in a combined way to achieve human-robot collaboration.Discussion: The paper demonstrates the use of the proposed system for tasks involving the manipulation of electrical wires. Further studies will investigate how it behaves with object of different shapes and in more complex tasks.

The robotics research community is placing a major focus on the automation of difficult operations like the production of wiring harnesses, which require a high degree of dexterity and precision. In current industrial production lines, this operation is still manually done since it requires manipulating deformable linear objects like cables and wires. Thanks to the advent of multimodal sensory systems, it is possible to develop robotic systems capable of autonomously executing such complex tasks or assisting human operators. This article introduces a robotized system for autonomous wiring harness assembly by leveraging cutting-edge actuation and sensor technologies. The system here proposed is composed of a robotic arm equipped with tactile and proximity sensors for wire positioning and routing, and another one with a taping gun to secure wires. Furthermore, an external depth camera is utilized for real-time verification of the assembly process. To control the robotic arms' movements, a modular control architecture has been developed, providing adaptability of the system to different product designs or operational conditions. Finally, experimental evaluation for each component of the system is also provided, showing the effectiveness of the proposed solution in a real industrial scenario.