Collaborative Robot Safety for Human-Robot Interaction in Domestic Simulated Environments

Kaonain, T E; Rahman, Mohd Azizi Abdul; Ariff, Mohd Hatta Mohammed; Yahya, Wira Jazair; Mondal, Kuheli

doi:10.1088/1757-899x/1096/1/012029

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Kaonain et al [6] studied the safety of human when interacting with robot works in home. They designed a model with Gazebo based on ROS to simulate the interaction between human and robot.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of safety method for a 3-DOF industrial robot based on recurrent neural network

Mahmoud¹,

Sharkawy

Abdel-Jaber

2023

J. Eng. Appl. Sci.

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In this paper, a safety method for a 3-DOF industrial robot is developed based on recurrent neural network (RNN). Safety standards for human robot interaction (HRI) are taken into accounts. The main objective is to detect the undesired collisions on any of robot links. Since most of industrial robots are not collaborative, the dependence of the method on torque sensors to detect collisions makes its ability to use very restricted. Therefore, only the position data of joints are collected to be the data inputs of the proposed method in order to detect the undesired collisions. These data are aggregated from KUKA LWR IV robot while no collisions and in another time when applying collisions. These data are used to train the proposed RNN using Levenberg-Marquardt LM algorithm. KUKA robot is configured to act as a 3-DOF manipulator that moves in space and under the effect of gravity.The results show that the modelled and trained RNN is sensitive and efficient in detecting collisions on each link of robot separately. Studying the resulted error from the developed model reveals clearly that the method is reliable.

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Section: Literature Reviewmentioning

confidence: 99%

“…When analysing the feasibility of applying of the intelligent control in robotics, it can be concluded that intelligent control enhances quality control and safety in workspace. Moreover, it helps to reduce energy consumed during operation [6].…”

Section: Introductionmentioning

confidence: 99%

Development of safety method for a 3-DOF industrial robot based on recurrent neural network

Mahmoud¹,

Sharkawy

Abdel-Jaber

2023

J. Eng. Appl. Sci.

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“…Human-robot collaborative tasks have been widely extended from their day-to-day domestic and industrial applications to various other defense, military, and geospatial exploration operations [2]. The robots can assist humans to improve ergonomic conditions, maximize human abilities in industrial spaces while ensuring safety measures, automatize surgeries in medical applications, and overall, improve the quality of daily life [3].…”

Section: Introductionmentioning

confidence: 99%

Visual cue-aided human supervised robot navigation guidance in photometrically challenging environments using adaptive spatial-feature kernel-guided bilateral LPP

Ghosh

Chatterjee

Munshi

2023

Meas. Sci. Technol.

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The objective of an effective human-robot collaborative (HRC) task is the maximization of human-robot competencies while ensuring the user’s convenience. In photometrically challenging and unstructured HRC environments, data obtained from vision sensors often tend to get degraded due to illumination irregularities and spatio-temporal complexities. To extract useful and discriminative features from the data under such situations, locality-sensitive methods like locality preserving projections (LPP) become quite useful as it captures the local geometric structure of the high-dimensional data. In LPP, the local structural information is encoded in the form of weight values between two samples in the higher-dimensional Euclidean space. The weight values are learned in a regular and continuous manner which only depends on the spatial distribution of the data. Moreover, because of its weight dependency solely on the Euclidean distance, improper weight values can occur frequently, as the Euclidean distance is susceptible to noise, outliers, and different types of geometrical transformations. This paper proposes an adaptive weight learning method to be utilized in the weight computation of LPP, which allows it to adaptively select and extract more discriminative features from the higher-dimensional input data while preserving the intrinsic structural information of the data. Additionally, to alleviate the issues with spatial dependency, the concept of bilateral filtering that incorporates the range weights from the feature space along with the similarity weight in the Euclidean space has been utilized here. This paper proposes an augmented version of adaptive spatial-feature kernel-guided bilateral filtering inspired LPP which addresses two of these basic and fundamental issues of the conventional LPP.

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“…Considering work cells using robot arms, the most critical factor is ensuring work and worker safety [10]. Some studies combine various design approaches for the hardware and software of safetyrelated systems [11,12].…”

Section: Introductionmentioning

confidence: 99%

Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells

2021

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A two-dimensional (2-D) camera system with a real-time image processing-based safety technology is a cost-effective alternative that needs optimization of the cell layout, the number of cameras, and the camera’s locations and orientations. A design optimization study was performed using the multi-criteria linear fractional programming method and considering the number of cameras, the resolution, as well as camera positions and orientations. A table-top experimental setup was designed and built to test the effectiveness of the optimized design using two cameras. The designs at optimal and nonoptimal parameters were compared using a deep learning algorithm, ResNet-152. To eliminate blind spots, a simple but novel 2-D image merging technique was proposed as an alternative to commonly employed stereo imaging methods. Verification experiments were conducted by using two camera resolutions with two graphic processors under varying illuminance. It was validated that high-speed entrances to the safety system were detected reliably and with a 0.1 s response time. Moreover, the system was proven to work effectively at a minimum illuminance of 120 lux, while commercial systems cannot be operated under 400 lux. After determining the most appropriate 2-D camera type, positions, and angles within the international standards, the most cost-effective solution set with a performance-to-price ratio up to 15 times higher than high-cost 3-D camera systems was proposed and validated.

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Collaborative Robot Safety for Human-Robot Interaction in Domestic Simulated Environments

Cited by 6 publications

References 15 publications

Development of safety method for a 3-DOF industrial robot based on recurrent neural network

Development of safety method for a 3-DOF industrial robot based on recurrent neural network

Visual cue-aided human supervised robot navigation guidance in photometrically challenging environments using adaptive spatial-feature kernel-guided bilateral LPP

Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells

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