Single Pixel Time-of-Flight Sensors for Object Detection and Self-Detection in Three-Sectional Single-Arm Robot Manipulators

Himmelsbach, Urban B.; Wendt, Thomas M.; Hangst, Nikolai; Gawron, Philipp

doi:10.1109/irc.2019.00046

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The first approaches used a camera mounted on the flange of the robot [17]. Further research investigated the use of single-pixel ToF sensors distributed over the links of the manipulator [18]. In [19], we presented a sensor solution in form of an adapter-plate that is mounted between the flange of the robot and the gripper.…”

Section: Introductionmentioning

confidence: 99%

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

Himmelsbach

Wendt

Hangst

et al. 2021

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Himmelsbach

Wendt

Hangst

et al. 2021

Sensors

Self Cite

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“…Another thing to consider is self-detection—a situation where the ToF sensor detects a segment of the robot on which it is mounted. This problem can be solved by either simulating the expected measurements in an empty room or by calculating the expected values [ 15 ]. In either case, the exact mounting positions of the sensors has to be known.…”

Section: Introductionmentioning

confidence: 99%

Modular Lidar System for Multiple Field-of-View Ranging

Pogačnik,

Munih

2023

Sensors

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This paper explores the possibility of distributing the fields of view (FOVs) of a centralized lidar cluster using fixed mirrors for future use in safety applications in robotics and elsewhere. A custom modular lidar system with time-over-threshold (TOT) walk error compensation was developed for the experiments. It comprises a control board that provides the processing power and adjustable voltage regulation, and multiple individually addressable analogue front end (AFE) boards that each contain a transmitter, a receiver, time-to-digital (TDC) converters for pulse width measurements on the bot Tx and Rx side, and adjustable reference voltage generators for both the Tx and Rx pulse detection threshold. The lidar system’s performance with a target in the direct line of sight is compared to the configurations where the FOV is redirected with up to three mirrors in different configurations. The results show that the light path through the neighboring mirrors introduces a minor but noticeable measurement error on a portion of the measurement range.

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“…After two calibrations by Kinect, the manipulator was deleted from the scene through the data intersection between the manipulator model and the point cloud data. Himmelsbach et al [8] calculated the expected distance according to the robot joint angle and time-of-flight sensor position. It simplified obstacle detection to a two-dimensional detection problem.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate obstacle detection of manipulator in complex human–machine interaction workspace

Cui¹,

Jiang²,

Liu³

et al. 2022

Meas. Sci. Technol.

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In order to solve the problems of a large amount of calculation, poor real-time performance, and insufficient detection accuracy in an existing robot obstacle detection system, this paper proposes an improved lightweight You Only Look Once Version 3 algorithm for obstacle detection and tracking, by combining the DeepSort algorithm with a three-dimensional model velocity estimation method. First, the depthwise separable convolution and convolution kernel pruning methods are used to lighten the network. Second, to meet the requirement of dynamic obstacle detection, the DeepSort method is used to track dynamic obstacles and estimate their moving speed. Third, a method of bounding-sphere modeling and discrete marking is proposed to simplify the obstacle model. The experimental results show that: the model size is reduced by 92.11%, which suggests that the proposed lightweight detection system is more suitable for industrial applications; the detection speed in the actual scene reaches 118 frames per second and the average detection accuracy is 92.6%, while the multiple objects tracking accuracy and multiple objects tracking precision are all above 0.8 in the dynamic obstacle tracking process. Static and dynamic obstacles in the workspace can be detected simultaneously with an average positioning accuracy error of 1.5 cm. The proposed model can not only effectively improve the detection efficiency of obstacles in the manipulator workspace but also enhance the safety of industrial robot systems.

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Single Pixel Time-of-Flight Sensors for Object Detection and Self-Detection in Three-Sectional Single-Arm Robot Manipulators

Cited by 8 publications

References 8 publications

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

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

Modular Lidar System for Multiple Field-of-View Ranging

Fast and accurate obstacle detection of manipulator in complex human–machine interaction workspace

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