scholarly journals A hand-drawn map-based navigation method for mobile robots using objectness measure

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984633 ◽  
Author(s):  
Jie Niu ◽  
Kun Qian
Keyword(s):  
Mobile Robots ◽  
Autonomous Navigation ◽  
Saliency Map ◽  
Experimental Results ◽  
Environment Map ◽  
Environmental Cognition ◽  
Candidate Regions ◽  
Cognitive Environment ◽  
Candidate Object ◽  
Navigation Method

Correct cognition of the environment is the premise of mobile robots to realize autonomous navigation control tasks. The inconsistency caused by time-varying environmental information is a bottleneck for the development and application of cognitive environment technologies. In this article, we propose an environmental cognition method that uses a hand-drawn map. Firstly, we use the single skeleton refinement and fuzzy c-means algorithms to segment the image. Then, we select candidate regions combining the saliency map. At the same time, we use the superpixels straddling method to filter the windows. The final candidate object regions are obtained based on a fusion of saliency segmentation and superpixels clustering. Based on the above objectness estimation results, we use a human–computer interaction method to construct an inaccurate hand-drawn environment map for navigation. The experimental results from PASCAL VOC2007 validate the efficacy of the proposed objectness measure method, where our result of 41.2% on mean average precision is the best of the tested methods. Furthermore, the experimental results of robot navigation in the actual scene also verified the effectiveness of the proposed approach.

scholarly journals A Bionic Spatial Cognition Model and Method for Robots Based on the Hippocampus Mechanism

2022 ◽  
Vol 15 ◽  
Author(s):  
Jinsheng Yuan ◽  
Wei Guo ◽  
Fusheng Zha ◽  
Pengfei Wang ◽  
Mantian Li ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Spatial Cognition ◽  
Calculation Method ◽  
Autonomous Navigation ◽  
Path Integration ◽  
Motion Information ◽  
Robot Perception ◽  
Environmental Cognition ◽  
Biological Principle ◽  
Cognition Model

The hippocampus and its accessory are the main areas for spatial cognition. It can integrate paths and form environmental cognition based on motion information and then realize positioning and navigation. Learning from the hippocampus mechanism is a crucial way forward for research in robot perception, so it is crucial to building a calculation method that conforms to the biological principle. In addition, it should be easy to implement on a robot. This paper proposes a bionic cognition model and method for mobile robots, which can realize precise path integration and cognition of space. Our research can provide the basis for the cognition of the environment and autonomous navigation for bionic robots.

Teaching-Playback Navigation Without a Consistent Map

2018 ◽  
Vol 30 (4) ◽  
pp. 591-597 ◽  
Author(s):  
Naoki Akai ◽  
Luis Yoichi Morales ◽  
Hiroshi Murase ◽  
◽  
Keyword(s):  
Autonomous Navigation ◽  
Large Scale ◽  
Mobile Robot Navigation ◽  
Autonomous Mobile Robot ◽  
Environment Map ◽  
Localization And Mapping ◽  
Mapping Process ◽  
Line Processes ◽  
Target Path ◽  
Navigation Method

This paper presents a teaching-playback navigation method that does not require a consistent map built using simultaneous localization and mapping (SLAM). Many open source projects related to autonomous navigation including SLAM have been made available recently; however, autonomous mobile robot navigation in large-scale environments is still difficult because it is difficult to build a consistent map. The navigation method presented in this paper uses several partial maps to represent an environment map. In other words, the complex mapping process is not necessary to begin autonomous navigation. In addition, the trajectory that the robot travels in the mapping phase can be directly used as a target path. As a result, teaching-playback autonomous navigation can be achieved without any off-line processes. We tested the navigation method using log data taken in the environment of the Tsukuba Challenge and the testing results show its performance. We provide source code for the navigation method, which includes modules required for autonomous navigation (https://github.com/NaokiAkai/AutoNavi).

scholarly journals Tsukuba Challenge 2019: Task Settings and Experimental Results

2020 ◽  
Vol 32 (6) ◽  
pp. 1104-1111
Author(s):  
Yoshitaka Hara ◽  
Tetsuo Tomizawa ◽  
Hisashi Date ◽  
Yoji Kuroda ◽  
Takashi Tsubouchi ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Real World ◽  
Autonomous Navigation ◽  
Experimental Results ◽  
The City

This paper overviews Tsukuba Challenge 2019. The Tsukuba Challenge is an experiment for autonomous navigation of mobile robots on public walkways. Navigation tasks through pedestrian paths in the city are given. Participating teams develop their own robot hardware and software. We describe the aim of the task settings and the analysis of the experimental results for all the teams. We studied the records of real-world experiments of Tsukuba Challenge 2019.

Development of Autonomous Mobile Robot Based on Accurate Map in the Tsukuba Challenge 2014

2015 ◽  
Vol 27 (4) ◽  
pp. 346-355 ◽  
Author(s):  
Junji Eguchi ◽  
◽  
Koichi Ozaki
Keyword(s):  
Mobile Robots ◽  
Global Positioning System ◽  
Autonomous Navigation ◽  
Control Method ◽  
Autonomous Mobile Robots ◽  
Graphical Interface ◽  
Velocity Control ◽  
Laser Scanners ◽  
Global Positioning ◽  
Navigation Method

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/04.jpg"" width=""300"" /> Navigation method for mobile robots</div> We describe a navigation method for autonomous mobile robots and detail knowledge obtained through Tsukuba Challenge 2014 trial runs. The challenge requires robots to navigate autonomously 1.4 km in an urban area and to search for five persons in three areas. Accurate maps are important tools in localization on complex courses in autonomous outdoor navigation. We constructed an occupancy grid map using laser scanners, gyro-assisted odometry and a differential global positioning system (DGPS). In this study, we use maps as a graphical interface. Namely, by using maps, we give environmental information, untravelable low objects such as curb stones, and areas in nonsearches for “target” persons. For the purpose of increasing the map reusability, we developed a waypoint editor, which can modify waypoints on maps to fit a course to a situation. We also developed a velocity control method that the robot uses to follow pedestrians and other robot by keeping safety distance on the course. As a result, our robot took part five of seven official trial runs to get to the goal. This indicates that the autonomous navigation method was stable in the Tsukuba Challenge 2014 urban environment. </span>

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

Algorithm of Text Categorization Based on Cloud Computing

2013 ◽  
Vol 311 ◽  
pp. 158-163 ◽  
Author(s):  
Li Qin Huang ◽  
Li Qun Lin ◽  
Yan Huang Liu
Keyword(s):  
Cloud Computing ◽  
Text Categorization ◽  
Experimental Results ◽  
Support Vector ◽  
Computing Environment ◽  
Mapreduce Framework ◽  
Cloud Computing Environment ◽  
Environment Map ◽  
Vector Machines ◽  
Parallel Text

MapReduce framework of cloud computing has an effective way to achieve massive text categorization. In this paper a distributed parallel text training algorithm in cloud computing environment based on multi-class Support Vector Machines(SVM) is designed. In cloud computing environment Map tasks realize distributing various types of samples and Reduce tasks realize the specific SVM training. Experimental results show that the execution time of text training decreases with the number of Reduce tasks increasing. Also a parallel text classifying based on cloud computing is designed and implemented, which classify the unknown type texts. Experimental results show that the speed of text classifying increases with the number of Map tasks increasing.

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