On a Sensor-Based Navigation for a Mobile Robot

1996 ◽  
Vol 8 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Hiroshi Noborio ◽  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Unknown Environment ◽  
Navigation Algorithms ◽  
On Line ◽  
Sensor Information ◽  
Made In

The sensor-based navigation for a mobile robot is a problem of how to select a sequence of sensor-based behaviors between start and goal positions. If a mobile robot does not know its 2-d environment completely or partially, it is obliged to rely on sensor information reflected from closer obstacles in order to avoid them on-line. In the on-line framework, we should consider how a mobile robot reaches its goal position in an uncertain 2-d world. Therefore we will study some previous sensor-based navigation algorithms for mobile robots. Our motivations are to ascertain the convergence of a mobile robot to its goal position, compare the lengths of sensor-based sequences made in the previous algorithms, and decrease the length of a sequence of sensor-based motions, which is generated between start and goal positions by a sensor-feedback obstacle avoidance. Because the mobile robot itself, its sensors, and its environment usually have several uncertainties, it is notable as to how a mobile robot arrives at or near its goal in overcoming such uncertainties. It is demonstrated that the sensor-based navigation still has an enormous potential as an actual navigation of a mobile robot in a completely or partially unknown environment.

Information Fusion of Ultrasonic Sensor Based on RBF Network in Obstacle-Avoidance System of Mobile Robot

2010 ◽  
Vol 20-23 ◽  
pp. 791-795
Author(s):  
Wei Huang ◽  
Yi Xin Yin ◽  
Shan Ding ◽  
Jie Dong ◽  
Xue Ming Ma ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Information Fusion ◽  
Simulation Experiment ◽  
Rbf Networks ◽  
Important Method ◽  
Rbf Network ◽  
Sensor Information ◽  
Made In

Artificial neural networks are applied to multi-sensor information fusion (MSIF) in obstacle-avoidance system of mobile robot. BP and RBF networks are presented, and comparison is made in the simulation experiment. Results show that RBF network is more effective to deal with information of multi-sensor. It can become an important method for multi-sensor information fusion.

An Integrated Algorithm for Autonomous Navigation of a Mobile Robot in an Unknown Environment

2008 ◽  
Vol 12 (4) ◽  
pp. 328-335 ◽  
Author(s):  
Lee Gim Hee ◽  
◽  
Marcelo H. Ang Jr. ◽  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Hybrid Method ◽  
Autonomous Navigation ◽  
Prior Information ◽  
Optimal Path ◽  
Unknown Environment ◽  
Global Path Planning ◽  
Local Navigation ◽  
Navigation Algorithms

Global path planning algorithms are good in planning an optimal path in a known environment, but would fail in an unknown environment and when reacting to dynamic and unforeseen obstacles. Conversely, local navigation algorithms perform well in reacting to dynamic and unforeseen obstacles but are susceptible to local minima failures. A hybrid integration of both the global path planning and local navigation algorithms would allow a mobile robot to find an optimal path and react to any dynamic and unforeseen obstacles during an operation. However, the hybrid method requires the robot to possess full or partial prior information of the environment for path planning and would fail in a totally unknown environment. The integrated algorithm proposed and implemented in this paper incorporates an autonomous exploration technique into the hybrid method. The algorithm gives a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment.

scholarly journals Modeling and Analysis of the Obstacle-Avoidance Strategies for a Mobile Robot in a Dynamic Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Wang ◽  
Ming Wang ◽  
Yong Guan ◽  
Xiaojuan Li
Keyword(s):  
Energy Consumption ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Decision Process ◽  
Dynamic Environment ◽  
Formal Models ◽  
Modeling And Analysis ◽  
Avoidance Strategies ◽  
Markov Decision

Obstacle avoidance is a key performance of mobile robots. However, its experimental verification is rather difficult, due to the probabilistic behaviors of both the robots and the obstacles. This paper presents the Markov Decision Process based probabilistic formal models for three obstacle-avoidance strategies of a mobile robot in an uncertain dynamic environment. The models are employed to make analyses in PRISM, and the correctness of the analysis results is verified by MATLAB simulations. Finally, the minimum time and the energy consumption are determined by further analyses in PRISM, which prove to be useful in finding the optimal strategy. The present work provides a foundation for the probabilistic formal verification of more complicated obstacle-avoidance strategies.

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.

Using Global Appearance Descriptors to Solve Topological Visual SLAM

2019 ◽  
pp. 1127-1140
Author(s):  
Lorenzo Fernández Rojo ◽  
Luis Paya ◽  
Francisco Amoros ◽  
Oscar Reinoso
Keyword(s):  
Computer Vision ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Simultaneous Localization And Mapping ◽  
Visual Slam ◽  
Sources Of Information ◽  
Topological Approach ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Omnidirectional Images

Mobile robots have extended to many different environments, where they have to move autonomously to fulfill an assigned task. With this aim, it is necessary that the robot builds a model of the environment and estimates its position using this model. These two problems are often faced simultaneously. This process is known as SLAM (simultaneous localization and mapping) and is very common since when a robot begins moving in a previously unknown environment it must start generating a model from the scratch while it estimates its position simultaneously. This chapter is focused on the use of computer vision to solve this problem. The main objective is to develop and test an algorithm to solve the SLAM problem using two sources of information: (1) the global appearance of omnidirectional images captured by a camera mounted on the mobile robot and (2) the robot internal odometry. A hybrid metric-topological approach is proposed to solve the SLAM problem.

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