Multi-Sensor Information Fusion and its Application in Robots

2013 ◽  
Vol 443 ◽  
pp. 299-302
Author(s):  
Ran Zhang ◽  
Jing Zi Wei
Keyword(s):  
Mobile Robots ◽  
Information Fusion ◽  
Accurate Information ◽  
Simulation Experiments ◽  
Multiple Sensors ◽  
Fusion Methods ◽  
Systematic Structure ◽  
Sensor Information ◽  
Real Time Information ◽  
Time Information

Multi-sensor Information Fusion can get more accurate information required by the system through fusing redundant, complementary, or more real-time information provided by multiple sensors. This paper, with the emphasis on the MIF technology, combining its application in mobile robots, is discussed from two aspects, namely theory and simulation experiments. First of all, this paper expounds the basic principle of MIF technology, systematic structure and gradation of information fusion and information fusion methods; Secondly, based on the theory of neural network integration, it discusses the application of MIF technology in the field of robots , providing an effective method for navigation and obstacle avoidance of mobile robots.

Implementation of Multi-Sensor Information Fusion in the Mobile Robot Control

2013 ◽  
Vol 448-453 ◽  
pp. 3549-3552
Author(s):  
Guo Qing Qiu ◽  
Yong Can Yu ◽  
Ming Li ◽  
Yi Long
Keyword(s):  
Mobile Robot ◽  
Information Fusion ◽  
Control Method ◽  
Ccd Camera ◽  
Accurate Information ◽  
Real Time Control ◽  
Multiple Sensors ◽  
Time Control ◽  
Fuzzy Neural ◽  
Sensor Information

Multi-sensor information fusion is that fuses information of multiple sensors gained through use of redundant, complementary, or timelier information in a system can provide more reliable and accurate information. Under the research of mobile robot environmental information, a control method of fuzzy neural network based on T-S (Takagi-Sugeno) type is given, it can fuses effectively collected information from multiple ultrasonic sensors and a CCD camera, and realize the real-time control for mobile robot. The results on mobile robot obstacles avoidance verified the effectiveness of the method.

scholarly journals Research on multi-sensor information fusion and intelligent optimization algorithm and related topics of mobile robots

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yuan Guo ◽  
Xiaoyan Fang ◽  
Zhenbiao Dong ◽  
Honglin Mi
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Information Fusion ◽  
Intelligent System ◽  
High Reliability ◽  
High Tech ◽  
Intelligent Optimization ◽  
Excellent Research ◽  
Sensor Information ◽  
Equalization Method

AbstractResearch on mobile robots began in the late 1960s. Mobile robots are a typical autonomous intelligent system and a hot spot in the high-tech field. They are the intersection of multiple technical disciplines such as computer artificial intelligence, robotics, control theory and electronic technology. The product not only has potentially very attractive application value and commercial value, but the research on it is also a challenge to intelligent technology. The development of mobile robots provides excellent research for various intelligent technologies and solutions. This dissertation aims to study the research of multi-sensor information fusion and intelligent optimization methods and the methods of applying them to mobile robot related technologies, and in-depth study of the construction of mobile robot maps from the perspective of multi-sensor information fusion. And, in order to achieve this function, combined with autonomous exploration and other related theories and algorithms, combined with the Robot Operating System (ROS). This paper proposes the area equalization method, equalization method, fuzzy neural network and other methods to promote the realization of related technologies. At the same time, this paper conducts simulation research based on the SLAM comprehensive experiment of the JNPF-4WD square mobile robot. On this basis, the high precision and high reliability of robot positioning are further realized. The experimental results in this paper show that the maximum error of the X-axis and Y-axis, FastSLAM algorithm is smaller than EKF algorithm, and the improved FASTSALM algorithm error is further reduced compared with the original FastSLAM algorithm, the value is less than 0.1.

scholarly journals A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

2020 ◽  
Author(s):  
Lifan Sun ◽  
Yuting Chang ◽  
Jiexin Pu ◽  
Haofang Yu ◽  
Zhe Yang
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Combination Method ◽  
Uncertain Information ◽  
Combination Rule ◽  
Simulation Experiments ◽  
Sensor Information ◽  
Bodies Of Evidence ◽  
Probability Distance ◽  
Deng Entropy

The Dempster-Shafer (D-S) theory is widely applied in various fields involved with multi-sensor information fusion for radar target tracking, which offers a useful tool for decision-making. However, the application of D-S evidence theory has some limitations when evidences are conflicting. This paper proposed a new method combining the Pignistic probability distance and the Deng entropy to address the problem. First, the Pignistic probability distance is applied to measure the conflict degree of evidences. Then, the uncertain information is measured by introducing the Deng entropy. Finally, the evidence correction factor is calculated for modifying the bodies of evidence, and the Dempster’s combination rule is adopted for evidence fusion. Simulation experiments illustrate the effectiveness of the proposed method dealing with conflicting evidences.

Based on D - S Evidence Theory of Solution Concentration Detection Method Research

2014 ◽  
Vol 494-495 ◽  
pp. 869-872
Author(s):  
Xian Bao Wang ◽  
Shi Hai Zhao ◽  
Guo Wei
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Solution Concentration ◽  
Sensor Data ◽  
Fusion Technology ◽  
Feedback Information ◽  
Multiple Sensors ◽  
Multi Sensor Data Fusion ◽  
Single Sensor ◽  
Sensor Information

According to the theory of multi-sensor information fusion technology, based on D - S evidence theory to fuse of multiple sensors feedback information from different angles for detecting solution concentration, and achieving the same judgment; This system uses of D - S evidence theory of multi-sensor data fusion method, not only make up the disadvantages of using a single sensor, but also largely reduce the uncertainty of the judgment. Additionally this system improves the rapidity and accuracy of the solution concentration detection, and broadens the application field of multi-sensor information fusion technology.

scholarly journals Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Hongling Wang ◽  
Chengjin Zhang ◽  
Yong Song ◽  
Bao Pang
Keyword(s):  
Mobile Robots ◽  
Information Fusion ◽  
Indoor Environment ◽  
Simultaneous Localization And Mapping ◽  
Search And Rescue ◽  
The Novel ◽  
Multiple Robots ◽  
Localization And Mapping ◽  
Fusion Methods ◽  
Proprioceptive Sensors

The first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing simultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion approaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors’ measurements and mobile robots’ trajectories, are proposed. The novel integration particle filter (IPF) and optimal improved EKF (IEKF) algorithms are derived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists of multirobots and multisensors (MAM); multiple robots mount on-board laser range finder (LRF) sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM) is compared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real observation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment and outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed information-fusion methods in improving SLAM performances adapting to SAR scenarios.

scholarly journals PSIXPORT: Mobile App for Ecological Momentary Assessment of Psychological Dimensions in Sport Injury

2021 ◽  
Vol 12 ◽  
Author(s):  
Luis J. González-Barato ◽  
Víctor J. Rubio ◽  
José Manuel Hernández ◽  
Iván Sánchez-Iglesias
Keyword(s):  
Real Time ◽  
Cognitive Appraisals ◽  
Mobile App ◽  
Accurate Information ◽  
Psychological Variables ◽  
Rehabilitation Process ◽  
Ecological Momentary ◽  
Real Time Information ◽  
Time Information ◽  
Injured Athletes

Retrospective self-reports have been commonly used to assess psychological variables such as feelings, thoughts, or emotions. Nevertheless, this method presents serious limitations to gather accurate information about variables that change over time. The Ecological Momentary Assessment (EMA) approach has been used to deal with some of the limitations these retrospective assessment methods present, and for gathering real-time information about dynamic psychological variables, such as feelings, thoughts, or behaviors. In the sports injury rehabilitation context, athletes' thoughts, feelings, behaviors, and pain perceptions during the rehabilitation process can influence the outcomes of this process. These responses change over different stages of the rehabilitation and taking them into account can help therapists to adapt the rehabilitation process and increasing their effectiveness. With this aim, an EMA mobile app (PSIXPORT) was designed to gather real-time information about severely injured athletes' cognitive appraisals, emotional responses, behaviors, and pain perceptions during their rehabilitation process. The goals of this study were to evaluate Psixport's ability to gather real-time information about injured athletes' psychological responses during the rehabilitation, to test the users' perceived usability of Psixport, and to compare the reliability and differences between real-time data gathered with Psixport and the data gathered through the one-time retrospective method. Twenty-eight severely injured athletes (10 men and 18 women) were assessed using Psixport, a retrospective questionnaire, and the uMARS usability test. Results showed that Psixport can be considered as a good tool to gather information about injured athletes' cognitive appraisals, emotional responses, behaviors, and pain perceptions. Moreover, multiple data assessments gathered with the app showed to be more accurate information about injured athletes' psychological responses than one-time retrospective reports.

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