Article
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 , ISSUE 1, 1–25
Article
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 , ISSUE 2, 295–314
Article
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 , ISSUE 1, 108–114
Article
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 , ISSUE 2, 1354–1378
Research Article
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 , ISSUE 3, 551–566
Research Article
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 , ISSUE 5, 1–6