Abstract
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.
Highlights
While robotics scientists are moving toward the creation of humanoid robots, thought to be able to work alongside humans in human-centric and unstructured environments, industries are faced with even more complex tasks, which require important technological advances and, in particular, human-like features, in the new generation of robotic systems
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
Summary
While robotics scientists are moving toward the creation of humanoid robots, thought to be able to work alongside humans in human-centric and unstructured environments, industries are faced with even more complex tasks, which require important technological advances and, in particular, human-like features, in the new generation of robotic systems. The authors overcomes the limitations of the use of vision-based systems, e.g., poor light or limited visibility during grasping, and obtains a good recognition performance (93.1%) by combining tactile sensor and neural networkbased learning algorithms to estimate local object curvature during grasping maneuvers. The proposed system uses machine learning methods to estimate the orientation of in-hand objects from the data gathered by tactile sensors mounted on the phalanges of under-actuated fingers. An appropriate video has been prepared and attached to this paper (Please see the Supplementary Materials) to show the use of the wire diameter estimation during a sorting task
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
