Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Liu, Quan; Liu, Zhihao; Xiong, Bo; Xu, Wenjun; Liu, Yang

doi:10.1016/j.aei.2021.101360

Cited by 73 publications

(19 citation statements)

References 32 publications

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“…Where, t   represents that the car continues to travel at speed along the current heading angle. This paper adds the prediction status of Unmanned Vehicle and resets reward function [24]. When the distance between the unmanned vehicle and the obstacle center is greater than the unmanned vehicle prediction distance, reward=-1 is set to give a positive return.…”

Section: B Q-learning Enhanced Learning Algorithm With Lstmmentioning

confidence: 99%

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

2022

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In order to solve the "minimum trap" of artificial potential field method and the limitation of traditional path planning algorithm in dynamic obstacle environment, a path planning control algorithm based on improved artificial potential field method is proposed. Firstly, a virtual potential field detection circle model (VPFDCM) with adjustable radius is proposed to detect the "minimum trap" formed by the repulsion field of obstacles in advance. And the motion model of unmanned vehicle is established. Combined with the improved reinforcement learning algorithm based on Long Short-Term Memory(LSTM), the radius of virtual potential field detection circle is adjusted to achieve effective avoidance of dynamic obstacles, The reliable online collision free path planning of unmanned vehicle in semi closed dynamic obstacle environment is realized. Finally, the reliability and robustness of the algorithm are verified by MATLAB simulation. The simulation results show that the improved artificial potential field method can effectively solve the problem of unmanned vehicle falling into the "minimum trap" and improve the reliability of unmanned vehicle movement. Compared with the traditional artificial potential field method, the improved artificial potential field method can achieve more than 90% success rate in obstacle avoidance.

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Section: B Q-learning Enhanced Learning Algorithm With Lstmmentioning

confidence: 99%

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

2022

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“…In order to obtain the signal estimation in the domain, the Bayesian maximum a posteriori estimation method is used to calculate the a posteriori probability. It is obtained by Equation (5).…”

Section: Design Of Wavelet Shrinkage Algorithmmentioning

confidence: 99%

“…In diagnosis, it is convenient for doctors to obtain the patient's condition information (3). With the development of medicine, medical images have been continuously optimized to gradually form three-dimensional (3D) multimodal medical images, which make medical images clearer and have higher resolution (4,5). In order to effectively distinguish the pathological region from the normal region in medical image and enable doctors to diagnose and treat more intuitively, the segmentation of 3D multimodal medical image has become the focus of current research.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics

Gao

Wang

et al. 2022

Front. Public Health

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To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using reinforcement learning and big data analytics is proposed. Bayesian maximum a posteriori estimation method and improved wavelet threshold function are used to design wavelet shrinkage algorithm to remove high-frequency signal component noise in wavelet domain. The low-frequency signal component is processed by bilateral filtering and the inverse wavelet transform is used to denoise the 3D multimodal medical image. An end-to-end DRD U-Net model based on deep reinforcement learning is constructed. The feature extraction capacity of denoised image segmentation is increased by changing the convolution layer in the traditional reinforcement learning model to the residual module and introducing the multiscale context feature extraction module. The 3D multimodal medical image segmentation is done using the reward and punishment mechanism in the deep learning reinforcement algorithm. In order to verify the effectiveness of 3D multimodal medical image segmentation algorithm, the LIDC-IDRI data set, the SCR data set, and the DeepLesion data set are selected as the experimental data set of this article. The results demonstrate that the algorithm's segmentation effect is effective. When the number of iterations is increased to 250, the structural similarity reaches 98%, the SNR is always maintained between 55 and 60 dB, the training loss is modest, relative overlap and accuracy all exceed 95%, and the overall segmentation performance is superior. Readers will understand how deep reinforcement learning and big data analytics test the effectiveness of 3D multimodal medical image segmentation algorithm.

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“…However, as a non-talent ability of the machine, applying this ability is a very challenging task. Further more, motion prediction has also been widely applied to autonomous driving [23,24,20], intelligent robot [39,38], human-robot collaboration [51,58,52,68,19,50,55,78], and multimedia applications [64,104], as shown in Fig 1.…”

Section: Introductionmentioning

confidence: 99%

3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.

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Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Cited by 73 publications

References 32 publications

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics

3D Human Motion Prediction: A Survey

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