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Conference

International Conference on Ubiquitous Robots and Ambient Intelligence 

About: International Conference on Ubiquitous Robots and Ambient Intelligence is an academic conference. The conference publishes majorly in the area(s): Robot & Mobile robot. Over the lifetime, 1669 publications have been published by the conference receiving 6849 citations.


Papers
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Proceedings ArticleDOI
26 Jun 2018
TL;DR: Deep Reinforcement Learning autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Q Network to a simulated car an urban environment using two types of sensor data as input: camera sensor and laser sensor in front of the car.
Abstract: Deep Reinforcement Learning has led us to newer possibilities in solving complex control and navigation related tasks. The paper presents Deep Reinforcement Learning autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Q Network to a simulated car an urban environment. The approach uses two types of sensor data as input: camera sensor and laser sensor in front of the car. It also designs a cost-efficient high-speed car prototype capable of running the same algorithm in real-time. The design uses a camera and a Hokuyo Lidar sensor in the car front. It uses embedded GPU (Nvidia-TX2) for running deep-learning algorithms based on sensor inputs.

99 citations

Proceedings ArticleDOI
17 Dec 2015
TL;DR: A new way to model, train and recognize different activities using advanced HMM is proposed, resulting up to the mean recognition of 57.69% over the state of the art methods using IM-DailyDepthActivity dataset.
Abstract: In this paper, a depth camera-based novel approach for human activity recognition is presented using robust depth silhouettes context features and advanced Hidden Markov Models (HMMs). During HAR framework, at first, depth maps are processed to identify human silhouettes from noisy background by considering frame differentiation constraints of human body motion and compute depth silhouette area for each activity to track human movements in a scene. From the depth silhouettes context features, temporal frames information are computed for intensity differentiation measurements, depth history features are used to store gradient orientation change in overall activity sequence and motion difference features are extracted for regional motion identification. Then, these features are processed by Principal component analysis for dimension reduction and k-mean clustering for code generation to make better activity representation. Finally, we proposed a new way to model, train and recognize different activities using advanced HMM. Experimental results show superior recognition rate, resulting up to the mean recognition of 57.69% over the state of the art methods using IM-DailyDepthActivity dataset. In addition, MSRAction3D dataset also showed some promising results.

58 citations

Proceedings ArticleDOI
01 Jun 2017
TL;DR: From the literature, some most interested issues such as potential applications, representation of multiple quadrotor formation, consensus, formation control, formation configurations, localization, etc are summarized.
Abstract: In this paper, we try to show the state of the art on the formation control of multiple quadrotors. From the literature, we summarize some most interested issues such as potential applications, representation of multiple quadrotor formation, consensus, formation control, formation configurations, localization, etc. According to these research, we point out the difficulties and the tendency of research in the field of multiple quadrotors control.

52 citations

Proceedings ArticleDOI
01 Nov 2011
TL;DR: To implement this work, Cortex-A8 series S5PV210 embedded processor and Android operating system are correlated and the robot has an autonomous and manual travel and is controlled by only smart phone.
Abstract: In this work, Android operating system based robot platform and smart phone operated control and monitoring system are introduced. To implement this work, Cortex-A8 series S5PV210 embedded processor and Android operating system are correlated. The robot has an autonomous and manual travel and is controlled by only smart phone. In the Android OS (Operating System), the camera image is compressed to JPEG format and the image file is delivered to a smart phone through 802.11x wireless LAN communication which utilizes TCP/IP communication socket programming. Later, the transferred image data are converted into BMP format, which enables a real time image display.

52 citations

Proceedings ArticleDOI
01 Aug 2016
TL;DR: 6 types of traffic sign images are trained by LeNet-5 convolutional neural network architecture and the recognition system nearly achieves real-time performance.
Abstract: TSR (Traffic Sign Recognition), a part of ADAS (Advanced Drive Assistance System), helps driver (or car) to recognize traffic signs ahead with using front camera. According to EURO NCAP rating policy, car should be able to warn the driver when the car's speed is above the set speed threshold. It is thought that various types of traffic sign should be recognized to get more detailed information of road. In this paper, 6 types of traffic sign images are trained by LeNet-5 convolutional neural network architecture. In the detection phase, light-weight color-based segmentation algorithm and Hough transform algorithm are applied to extract candidate regions of traffic signs. The recognition system nearly achieves real-time performance. On-line recognition test is performed on the KAIST campus road, and the result shows all 16 traffic signs are recognized successfully through the driving. The recognition system is implanted into autonomous vehicle ‘Eurecar’. Different types of traffic signs are trained consistently and development of clustering algorithm is considered as a future work for robust recognition system.

51 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
201999
2018127
2017251
2016225
2015170
2014172