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  • In Jour Smart Sensing And Intelligent Systems
  • International Journal Advanced Network Monitoring Controls

 

Article | 01-March-2015

AN ASSISTANCE SYSTEM FOR BUILDING INTELLIGENT SPACES BASED ON MAPSHARING AMONG A MOBILE ROBOT AND DISTRIBUTED SENSORS

configuration of the proposed system is introduced. The proposed system consists of map building systems of the mobile robot and the distributed sensors. A global map from the robot and local maps from the distributed sensors are compared. Then, the local maps of the distributed sensors are associated with the global map and the positions of the distributed sensors are estimated in the global map. For improvement of map matching, angle differences between maps are evaluated. Some experimental results in an

Fumitaka Hashikawa, Kazuyuki Morioka

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 1–25

Article | 01-June-2012

Spatial-temporal Collaborative Sequential Monte Carlo for Mobile Robot Localization in Distributed Intelligent Environments

In this paper, a spatial-temporal collaborative sequential Monte Carlo architecture for mobile robot localization is designed to well suites intelligent environment for service robotic system. A proposed algorithm, namely Distributed Proportional Allocation-Augmented Particle Filter (DPA-APF), resolves the sensor collaboration problem by the processes of augmented sampling, inter-node resampling, inner-node resampling and particle exchange. These procedures exploit data parallelism and

Kun Qian, Xudong Ma, Xian Zhong Dai, Fang Fang

International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 2, 295–314

Article | 07-April-2018

Behavior Control Algorithm for Mobile Robot Based on Q-Learning

In order to adapt to navigation in unknown environment, the mobile robot must have intelligent abilities, such as environment cognition, behavior decision and learning. The navigation control algorithm is researched based on Q learning method in this paper. Firstly, the corresponding environment state space is divided. The action sets mapping with states are set. And the reward function is designed which combines discrete reward returns and continuous reward. The feasibility of this algorithm

Shiqiang Yang, Congxiao Li

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 1, 108–114

Article | 01-June-2015

DESIGN OF AN INTELLIGENT CAR FOR SEARCHING TRACK AND AVOIDING OBSTACLES

With the rapid development of informational and intelligent technology, the mobile robot has become an important branch of robot. In this paper, through UP-InnoSTARTM robot kits for the chassis and MultiFLEXTM 2-PXA270 controller for the control core, we design an intelligent car. It uses infrared proximity sensors to detect obstacles and gray-scale sensors to search the track. And the main functions are: autonomously recognizing the trajectory, searching it steadily, making quick judgment and

Degang Yang, Tingya Liu, Chunyan Hu

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1354–1378

Research Article | 01-September-2017

GRID-EDGE-DEPTH MAP BUILDING EMPLOYING SAD WITH SOBEL EDGE DETECTOR

values. In order to detect the obstacles, Sobel edge detection was implemented. The edge detection image was compared and substituted with the depth map which is resulting edge-depth map. The edge-depth map was divided into 25 grids (5 grids horizontal and 5 grids vertical). Finally, the minimum depth of detected obstacles for each grid was calculated. This process was resulting in a grid-edge-depth map (GED map). The proposal has been tested with a mobile robot in 5x3 meters living environment

Budi Rahmani, Agus Harjoko, Tri Kuntoro Priyambodo

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 3, 551–566

Research Article | 15-February-2020

Laser Profilometry Aiding Smart Vehicle Control

In this paper a fast method based on laser triangulation for the estimation of the pose of a mobile robot is presented. The system is made of a camera and a laser source, placed on the front side of a robot, the Smoov ASRV, which is an autonomous vehicle able to carry, store and retrieve pallets in a smart warehouse constituted by metallic shelves. Therefore, the movements of the robot are constrained into allowed paths made of rails, arranged with several apertures that allow the robot change

Cosimo Patruno, Roberto Marani, Massimiliano Nitti, Tiziana D’Orazio, Ettore Stella

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6

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