Mobile Robot Environment Map Building, Trajectory Tracking and Collision Avoidance Applications

Saidi, Sabrina Mohand; Mellah, Rabah

doi:10.1109/icaee47123.2019.9014773

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…The majority of research has been focused on applying dynamic models to build and implement mobile robot control (Sharma and Jain, 2016;Park et al, 2017;Sun et al, 2016). Others may not see the need to use the dynamic model or may lack access to specific information essential for its use, hence the kinematic model is widely used (Krivić et al, 2011;MohandSaidi and Mellah, 2019;Lang et al, 2010).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

Saidi¹,

Mellah²,

Fekik

et al. 2022

International Journal of Service Science, Management, Engineering, and Technology

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In this work, the differential mobile robot is controlled utilizing fuzzy PID speed control, which combines fuzzy control with conventional PID control in real time. The path may be convoluted, and the surrounding environment may contain a range of arbitrary shape and size obstacles. A monocular camera is used to detect obstacles during the navigation process. To enable a robot to travel within an indoor space while avoiding obstacles, a basic image processing approach based on area of interest was used. The goal of this research is to develop a fuzzy PID speed controller on a real robot, as well as a simple and efficient visual obstacle avoidance system. MATLAB is used to implement the control system. GUIDE (graphical user interface development environment) has enabled the creation of graphical user interfaces. These interfaces make it easy to manipulate the system in real time and capture live video. The proposed methodologies are tested on a non-holonomic dr robot i90 mobile robot, and the results are satisfactory.

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