Practical application of a safe human-robot interaction software

Abstract

Purpose Because of the increased use of robots in the industry, it has become inevitable for humans and robots to be able to work together. Therefore, human security has become the primary noncompromising factor of joint human and robot operations. For this reason, the purpose of this study was to develop a safe human-robot interaction software based on vision and touch. Design/methodology/approach The software consists of three modules. Firstly, the vision module has two tasks: to determine whether there is a human presence and to measure the distance between the robot and the human within the robot’s working space using convolutional neural networks (CNNs) and depth sensors. Secondly, the touch detection module perceives whether or not a human physically touches the robot within the same work environment using robot axis torques, wavelet packet decomposition algorithm and CNN. Lastly, the robot’s operating speed is adjusted according to hazard levels came from vision and touch module using the robot’s control module. Findings The developed software was tested with an industrial robot manipulator and successful results were obtained with minimal error. Practical implications The success of the developed algorithm was demonstrated in the current study and the algorithm can be used in other industrial robots for safety. Originality/value In this study, a new and practical safety algorithm is proposed and the health of people working with industrial robots is guaranteed.

Dynamical system based variable admittance control for physical human-robot interaction

“…Many approaches exist for exploring the sharing of task execution and control between humans and robots, for instance, impedance control and admittance control (Hogan, 1985), adjustable compliance actuators or variable stiffness mechanism (Jujjavarapu et al, 2019), teleoperation systems with haptic device (Soyguder and Abut, 2016;Abut and Soyguder, 2017) and robot contact operation control in uncertain environments (Cao et al, 2019). The recent work by Bingol and Aydogmus (2020) develops a safe HRI software based on both vision and touch to guarantee the health of people working with industrial robots. Zhu et al (2020) propose an intuitive shared control strategy to control a humanoid manipulator.…”

Layer Selection for Subtraction and Concatenation: A Method for Visual Velocity Estimation of a Mobile Robot

“…To illustrate, a liver tumor diagnosis was realized using deep learning techniques and CT -MR imaging [6]. In another study, an industrial robot was converted into a collaborative robot that did not harm people by utilizing deep learning techniques [7]. Artificial intelligence can be subdivided into two categories: deep learning and machine learning.…”

Adaptive enhanced admittance force-tracking controller design for highly dynamic interactive tasks

“…With the development of robotics, the contact process control between robot end-effector and the environment is becoming an important part of robotic applications involving, surface handling (such as polishing (Cao et al , 2020; Kana et al , 2021; Lakshminarayanan et al , 2021; Ochoa and Cortesao, 2021), medical rehabilitation physiotherapy (Ajani and Assal, 2020; Hasanzadeh Fereydooni et al , 2020; Zhang et al , 2020; Ajani and Assal, 2021) and physical human–robot interaction (Keemink et al , 2018; Ferraguti et al , 2019; Mustafa Can Bingol and Aydogmus, 2020b; Mustafa Can Bingol and Aydogmus, 2020a; Sharifi et al , 2021). The main problem of contact establishment involves the scale of contact force, which may reach prohibitive value, which may directly lead to mission failure or even more serious consequences.…”