Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration

Abstract

Human–robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implement method. In this paper, we proposed to add human–machine differentiation to the speed and separation monitoring in human–robot collaborative applications. The formula for the protective separation distance was extended with a variable for the kind of object that approaches the robot. Different sensors for differentiation of human and non-human objects are presented. Thermal cameras are used to take measurements in a proof of concept. Through differentiation of human and non-human objects, it is possible to decrease the protective separation distance between the robot and the object and therefore increase the overall efficiency of the collaborative application.

Collaborative Behavior for Non-Conventional Custom-Made Robotics: A Cable-Driven Parallel Robot Application

“…These methods allowed for the continuous dynamic adjustment of the robot's speed. Novel research demonstrated a method for dynamically adjusting the protective separation distance, enabling the robot to operate at higher speeds by distinguishing between human and non-human entities using thermal imaging technologies [61]. Finally, another distinct research effort employed fuzzy logic to control a robot's speed by detecting the proximity and interaction velocity between robots and humans [62].…”

“…The maximum safe speed threshold requested from the servo drives and the minimum protective separation distance that establishes their area of application must be pre-calculated according to ISO/TS 15066, and the risk assessment for the specific application [51,61]. Finally, considering the maximum velocity threshold value, the toolpath velocity override was selected so that none of the axes exceeded its individual velocity limit.…”

LiDAR-Based Maintenance of a Safe Distance between a Human and a Robot Arm

“…A wide variety of sensors can be encountered in the HRC-related literature, which can be positioned on a robot, on locations with a good view near the HRC workspace, sometimes even on a human. Commonly used sensors include the following: a single camera [7] or several cameras [8], stereo cameras [9], RGB-D visual sensors [10], ultrasonic sensors [11], infrared thermal sensors [12], laser-based technologies, including time-of-flight (TOF) sensor arrays, 3D TOF cameras and 2D/3D light detection and ranging (LiDAR) scanners [13][14][15][16], or different combinations, such as 2D laser scanners and a Kinect RGB-D visual sensor [17], or RGB cameras, a depth camera and a thermal imager [18].…”

Human–Robot Collaboration in Industrial Automation: Sensors and Algorithms

“…These engineers will also need to design connected systems that can efficiently and safely interact with humans during the manufacturing process, e.g., a car assembly line [ 3 ]. The focal points of this Special Issue are the smart sensors that enable robots and humans to “see” each other [ 4 , 5 , 6 , 7 , 8 , 9 ] and the machine learning algorithms that process these complex data so the robot can make decisions [ 10 , 11 , 12 , 13 ].…”

“…One limitation of using red–green–blue (RGB) imaging is the difficulty in distinguishing between humans in the foreground and moving objects in the background. Himmelsbach addressed this limitation in the field using thermal imaging [ 6 ]. This is especially advantageous for situations where robots can “see” both the operator’s workspace and walkways with roaming autonomous vehicles.…”