Incremental Learning for Autonomous Navigation of Mobile Robots based on Deep Reinforcement Learning

Luong, Manh; Pham, Cuong

doi:10.1007/s10846-020-01262-5

Cited by 48 publications

(17 citation statements)

References 31 publications

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“…Many systems have been developed to help visually impaired people perceive information of images on a screen. (6,(9)(10)(11)(12)(13) Some previous systems also implemented a deep learning methodology; (4,13,14,19) however, none of them used deep learning to detect objects in images. Therefore, the novelty of our system is that a deep learning method is implemented for object detection, then the sound or word of the object is presented to the user.…”

Section: Discussionmentioning

confidence: 99%

“…Object detection has been a popular area of research over the past few decades, as indicated by the large number of new applications related to identifying objects based on visual detection, such as facial expression recognition, (2) navigation assistants, (3) autonomous robot navigation, (4) self-driving systems, (5) image recognition, (6) and pedestrian detection. (7) Many approaches have been used to assist visually impaired people in obtaining information on a digital platform.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Interactive Sound Generation to Aid Visually Impaired People via Object Detection Using Touch Screen Sensor

Kurniati¹,

Yang²,

Chen³

et al. 2021

Sensors and Materials

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Along with the invention of portable devices, such as smartphones and tablets, computerbased touch screen sensor-assistive technologies have become significantly more affordable than traditional tactile graphics. The sensor panel in these technologies allows users to receive visual and auditorial responses via interaction with the device. However, visually impaired individuals (with a lack or loss of ability to see) will not find visual responses useful when using tablets or smartphones. Therefore, in this paper we propose a system that helps visually impaired people comprehend information on electronic devices with the help of auditory action feedback. We develop a multimedia system for sound production from a given image via object detection. In this study, YOLO (You Only Look Once) is used in object detection for sonification. A pretrained model is used; thus, a wider range of object classification can be identified. The system generates the corresponding sound when an object on the sensor screen is touched. The purpose of our research is to aid visually impaired people to perceive information of a picture shown on the device by touching the detected object. The device was tested by simulating visually impaired people by blindfolding people with normal vision, who filled out questionnaires on its performance. The results indicate that most of the users found that the sound presented by the device was helpful for telling them what the shown image was.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Interactive Sound Generation to Aid Visually Impaired People via Object Detection Using Touch Screen Sensor

Kurniati¹,

Yang²,

Chen³

et al. 2021

Sensors and Materials

View full text Add to dashboard Cite

show abstract

“…The end-to-end paradigm directly maps the sensor data and the vehicle state information into navigation control actions, which integrating perception and control of navigation frameworks. The deep reinforcement learning method [15] and the deep inverse reinforcement learning method [16] are the most popular methods in this approach. In this case, the reward function is learned from the expert demonstrations or defined by humans, which then is used to generate navigation action sequences.…”

Section: Related Workmentioning

confidence: 99%

An autonomous navigation approach for unmanned vehicle in outdoor unstructured terrain with dynamic and negative obstacles

Zhou

Zhang

et al. 2022

Robotica

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At present, the study on autonomous unmanned ground vehicle navigation in an unstructured environment is still facing great challenges and is of great significance in scenarios where search and rescue robots, planetary exploration robots, and agricultural robots are needed. In this paper, we proposed an autonomous navigation method for unstructured environments based on terrain constraints. Efficient path search and trajectory optimization on octree map are proposed to generate trajectories, which can effectively avoid various obstacles in off-road environments, such as dynamic obstacles and negative obstacles, to reach the specified destination. We have conducted empirical experiments in both simulated and real environments, and the results show that our approach achieved superior performance in dynamic obstacle avoidance tasks and mapless navigation tasks compared to the traditional 2-dimensional or 2.5-dimensional navigation methods.

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“…Deep learning as a field of research has burgeoned in the early 21st century. Its technological developments are diverse and have been applied to many research topics [ 11 , 12 , 13 , 14 , 15 ]. The present paper features deep learning techniques to resolve questions that chatbots cannot answer due to environmental contexts.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Context-Aware Based Chatbot Model: An Application of Humanoid Companion Robot

Kuo

Lin

et al. 2021

Sensors

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In aspect of the natural language processing field, previous studies have generally analyzed sound signals and provided related responses. However, in various conversation scenarios, image information is still vital. Without the image information, misunderstanding may occur, and lead to wrong responses. In order to address this problem, this study proposes a recurrent neural network (RNNs) based multi-sensor context-aware chatbot technology. The proposed chatbot model incorporates image information with sound signals and gives appropriate responses to the user. In order to improve the performance of the proposed model, the long short-term memory (LSTM) structure is replaced by gated recurrent unit (GRU). Moreover, a VGG16 model is also chosen for a feature extractor for the image information. The experimental results demonstrate that the integrative technology of sound and image information, which are obtained by the image sensor and sound sensor in a companion robot, is helpful for the chatbot model proposed in this study. The feasibility of the proposed technology was also confirmed in the experiment.

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Incremental Learning for Autonomous Navigation of Mobile Robots based on Deep Reinforcement Learning

Cited by 48 publications

References 31 publications

Interactive Sound Generation to Aid Visually Impaired People via Object Detection Using Touch Screen Sensor

Interactive Sound Generation to Aid Visually Impaired People via Object Detection Using Touch Screen Sensor

An autonomous navigation approach for unmanned vehicle in outdoor unstructured terrain with dynamic and negative obstacles

Multi-Sensor Context-Aware Based Chatbot Model: An Application of Humanoid Companion Robot

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