Vision-Based Robotic Solution for Wire Insertion with an Assigned Label Orientation

This paper tackles the problem of wire insertion in switchgear assembly according to the current regulations. In particular, the wire connections require that the wire label has to be oriented facing up in order to simplify and speed up testing and maintenance of the switchgear. The proposed approach exploits the a priori knowledge of the scenario with a calibrated RGB camera and a robotic arm to estimate both wire end pose and label position. The procedure combines several techniques (gradient base, trained classifier and stereo vision) to elaborate standard images in order to extract some wire features related to its shape and label. Specific frames are fixed according to estimated features and then used to correctly complete the task by using a robotic system. Experiments are reported to verify the effectiveness of the proposed approach

Wire Grasping by Using Proximity and Tactile Sensors

Nowadays robots have to be able to perform increasingly complex tasks. In grasping and manipulation, the knowledge of the environment and the pose of the target object are crucial for the correct execution of the task. Vision systems are widely used for environment and object perception, but they need to be finely calibrated to obtain high accuracy and they are not able to sense small objects like thin wires. Tactile sensors could be used to explore areas close to the target object, but this 'blind' physical interaction is not always feasible. This paper proposes a strategy to use a proximity sensor mounted on the robot's end effector to obtain a pose estimation of the target object that, in this study, is represented by a thin electrical wire

Proximity sensor for thin wire recognition and manipulation

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

Tactile sensor data interpretation for estimation of wire features

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

Tactile sensors for parallel grippers: Design and characterization

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

Deformable objects grasping and shape detection with tactile fingers and industrial grippers

In this paper, a cyber-physical system composed by a tactile sensor, a robotic gripper and suitable ROS software nodes is proposed. The tactile sensors are shown to be compatible with three different commercial grippers, and the developed ROS nodes for the data acquisition and elaboration enable the implementation of complex tasks such as the grasping and the shape reconstruction of deformable linear objects like cables. The effectiveness of the systems is tested with cable of different diameters and with wiring harnesses composed by several cables grouped together, focusing on the reconstruction of linear and quadratic curves representing the cable shape. Experimental trials are also executed to show the possibility of exploiting the shape reconstruction provided by the proposed system to correct the gripper grasping pose

An Intelligent System for Human Intent and Environment Detection Through Tactile Data

The tackled application aims at demonstrating the effectiveness of using tactile sensors for multiple purposes at the same time. In particular, tactile data are exploited for: the estimation of a wire diameter by using a previously trained classifier; teaching the robot new wire routing trajectories in case of unknown grasped wires; stopping the autonomous execution of previously learned trajectories to avoid damages due to possible wire entanglements

A cloud-edge smart infrastructures for road safety

This paper presents a cloud-edge solution for smart road safety monitoring and control that has been released as commercial product and service. We introduce the hardware and software architecture and its operation in real environments. Finally we discuss the application of an original methodology for security assessment

Vision-based grasp learning of an anthropomorphic hand-arm system in a synergy-based control framework

In this work, the problem of grasping novel objects with an anthropomorphic hand-arm robotic system is considered. In particular, an algorithm for learning stable grasps of unknown objects has been developed based on an object shape classification and on the extraction of some associated geometric features. Different concepts, coming from fields such as machine learning, computer vision, and robot control, have been integrated together in a modular framework to achieve a flexible solution suitable for different applications. The results presented in this work confirm that the combination of learning from demonstration and reinforcement learning can be an interesting solution for complex tasks, such as grasping with anthropomorphic hands. The imitation learning provides the robot with a good base to start the learning process that improves its abilities through trial and error. The learning process occurs in a reduced dimension subspace learned upstream from human observation during typical grasping tasks. Furthermore, the integration of a synergy-based control module allows reducing the number of trials owing to the synergistic approach

Weight-in-Motion System for Traffic Overload Detection: Development and Experimental Testing

A new load cell-based Weight-in-Motion (WIM) equipment for detecting traffic overload conditions on the roadway structure is proposed. The system consists of two load detection sensors, a supporting and stiffening steel structure, a reinforced concrete (RC) basement, and a steel covering plate. Embedded within the road pavement at grade with the top asphalt surface, the sensors are offset to avoid missing values while gathering the data from the passing vehicle. This strategy ensures the passage of both the axle’s wheels through the measuring area, which is the top surface of a steel beam supported by four load cells. Being the beam width smaller than the wheel’s contact surface, a tailored algorithm has been implemented to process the gathered data and return the Gross Vehicle Weight (GVW). More than 500 experimental tests have been performed to assess the system’s performance. A relatively rigid basement reduces the noise related to the vibrations generated by the system itself, which might affect the data analysis. Nonlinear dynamical FE analyses have been performed to support the structural design. A reduction in operating costs is allowed by the streamlined low-maintenance configuration of the system, along with its robustness