A Survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings

Mukherjee, Debasmita; Gupta, K. C.; Chang, Li Hsin; Najjaran, Homayoun

doi:10.1016/j.rcim.2021.102231

Cited by 156 publications

(70 citation statements)

References 101 publications

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“…To solve this problem, many researchers improve the motion planning algorithm by studying the psychological impact of humans on robot motion in the process of HRC and propose perceived safety and human-aware motion planning algorithms to ensure physical HRC (pHRC) [2,13,14]. For example, Kulic et al [15] and Zoghbi et al [16] used PP (pick and place movement) and RR (reach and retrieve motion) tasks to simulate typical robotic tasks during HRC and captured the physiological responses of 36 participants during this process.…”

Section: Iso/ts 15066 Type Of Cobot Operation Main Means Of Risk Redu...mentioning

confidence: 99%

“…Recently, humans-robot collaboration (HRC) gained increasing attention from researchers and companies due to its higher flexibility and automation level without barriers. It is adequate to combine the workers' flexibility with the power and accuracy of the robot [1][2][3]; it also can solve the problem of a companie's transformation to multi-variety and small-batch production. However, an essential prerequisite is workers' safety, for which researchers have carried out many research studies on [4], producing new safety standards relevant for HRC (ISO 10218 [5] and ISO/TS 15066 [6]).…”

Section: Introductionmentioning

confidence: 99%

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Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics

Liu

Wang

et al. 2022

Sensors

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Recently, the safety of workers has gained increasing attention due to the applications of collaborative robots (cobot). However, there is no quantitative research on the impact of cobot behavior on humans’ psychological reactions, and these results are not applied to the cobot motion planning algorithms. Based on the concept of the gravity field, this paper proposes a model of the psychological safety field (PSF), designs a comprehensive experiment on different speeds and minimum distances when approaching the head, chest, and abdomen, and obtains the ordinary surface equation of psychological stress about speed and minimum distance by using data fitting. By combining social rules and PSF models, we improve the robot motion planning algorithm based on behavioral dynamics. The validation experiment results show that our proposed improved robot motion planning algorithm can effectively reduce psychological stress. Eighty-seven point one percent (87.1%) of the experimental participants think that robot motion planned by improved robot motion planning algorithms is more “friendly”, can effectively reduce psychological stress, and is more suitable for human–robot interaction scenarios.

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Section: Iso/ts 15066 Type Of Cobot Operation Main Means Of Risk Redu...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics

Liu

Wang

et al. 2022

Sensors

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“…The design of the phalanges of the fingers is discussed in more detail below. A dynamic model of a similar design, but in the form of pedipulators of a mobile robot, is considered in [28], [29]. Here we will consider the construction of a flexible arm Figure 1…”

Section: Design Of Flexible Robot Handmentioning

confidence: 99%

Design and modeling industrial intelligent robot flexible hand

et al. 2022

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The article describes a description of a fundamentally new design, mathematical model and experimental research of a flexible arm with an anthropomorphic gripper for an industrial robot. The advantage of the proposed design of the robot arm in comparison with the known traditional technical solutions is achieved as close as possible to the functions of the human arm. This property significantly increases the versatility of the robot arm when performing various technological operations. Another difference from the known models of industrial robots is the presence of an anthropomorphic gripping device in the flexible arm, which allows you to service products with different shapes and arbitrary coordinates in space. In addition, the article for the first time proposes a method for calculating the parameters of a new hand and experimental studies of its functioning, which will allow engineers in the field of robotics to create similar designs. The economic effect of the proposed design is that the implementation of the movements of the proposed robot arm does not require separate electromechanical drives for each joint of the kinematic chain of the manipulator. This effect significantly reduces the cost of a robot arm while expanding its technological capabilities.

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“…In recent years, the mechanical adeptness of robots, compounded by human intelligence, has lead to the emergence of physical human-robot interaction (pHRI), the idea of robots and humans collaborating to perform many tasks more efficiently in such manufacturing environments [3]. This has been made easier by the emergence of machine/deep learning methods that can help robots learn to work with human operators [4]. Such collaboration can provide human operators with precise but also compliant tools to perform their tasks with reduced health risk and better ergonomics [5,6].…”

Section: Introductionmentioning

confidence: 99%

An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning

Guler¹,

Niaz²,

Madani³

et al. 2022

Preprint

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In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human-robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.

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A Survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings

Cited by 156 publications

References 101 publications

Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics

Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics

Design and modeling industrial intelligent robot flexible hand

An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning

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