Design and implemention of a simple fuzzy algorithm for obstacle avoidance navigation of a mobile robot in dynamic environment

Author(s):  
Iraj Hassanzadeh ◽  
Hamid Ghadiri ◽  
Reza Dalayimilan
2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Wang ◽  
Ming Wang ◽  
Yong Guan ◽  
Xiaojuan Li

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.


2014 ◽  
Vol 548-549 ◽  
pp. 922-927
Author(s):  
Bayanjargal Baasandorj ◽  
Aamir Reyaz ◽  
Park Joung Ho ◽  
Cha Wang Cheol ◽  
Deok Jin Lee ◽  
...  

This paper presents a method of solving the problem of mobile robot Obstacle avoidance and path planning in an unknown dynamic environment. A linear model of the two-wheeled nonholonomic robot controlled using Model predictive control controller. For obstacle avoidance Fuzzy logic control is used. The ultrasonic sensors are used for positioning and identifying an obstacle. The proposed method is successfully tested in simulations. Obstacle avoiding technique is very useful in real life, this technique can also use as a vision belt of blind people by changing the IR sensor by a kinetic sensor ,which is on type of microwave sensor whose sensing range is very high and the output of this sensor vary in according to the object position changes.


2022 ◽  
Vol 2146 (1) ◽  
pp. 012023
Author(s):  
Binghua Guo ◽  
Nan Guo

Abstract With the continuous development of intelligent algorithms, mobile robot (hereinafter referred to as MR) technology is gradually mature, which has been widely used in a variety of industries, such as industry, agriculture, medical treatment, service and so on. With the improvement of intelligent level, people have higher and higher requirements for MRs, which requires MRs to constantly adapt to different environments, especially dynamic environments. In the dynamic environment, obstacle avoidance technology has become the focus of intelligent robot research, which needs to continuously develop a variety of algorithms. By combining a variety of algorithms, we can realize obstacle avoidance and PP (hereinafter referred to as PP) of MR, which can realize obstacle avoidance more efficiently, in real time and intelligently. Multi algorithm fusion of MR has become the main trend of obstacle avoidance in the future, which will realize PP and optimization. Firstly, this paper analyzes the differences between traditional algorithms and intelligent algorithms. Then, the kinematics model and PP algorithm of MR are analyzed. Finally, the simulation is carried out.


10.5772/54427 ◽  
2013 ◽  
Vol 10 (1) ◽  
pp. 37 ◽  
Author(s):  
Mohammed Faisal ◽  
Ramdane Hedjar ◽  
Mansour Al Sulaiman ◽  
Khalid Al-Mutib

2014 ◽  
Vol 541-542 ◽  
pp. 1072-1078
Author(s):  
Yi Zhang ◽  
Xue Rong Tong ◽  
Yuan Luo

In order to solve the problem of the dynamic obstacle avoidance of the mobile robot in indoor environment, a new approach based on depth information is presented in this paper. The depth information of surrounding environment was collected and used to set the robots obstacle avoidance warning area by a Kinect sensor. When the moving obstacle accessed into the warning area, the robots obstacle avoidance direction was determined preliminary by the obstacles position, and then an improved Kalman filter algorithm was used to optimize the avoidance path. Experiments show that this approach can overcome the potential problem of path selection, and realize the mobile robot obstacle avoidance behavior in the dynamic environment.


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