Mobile robot visual navigation based on fuzzy logic and optical flow approaches

Abstract

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi-Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn-Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.

3D reconstruction study of motion fuzzy coded and non-coded targets based on iterative relaxation method

“…To solve the issue of the 3D-R systems getting stuck in an optimal location, they presented an extension and limit technique to remove the effect of the optimal situation from the representation and provide the ideal camera group and its associated relationships. The study [22] discussed the development of a fuzzy logic controller (FLC) and optical flow (OF) oriented interior navigational visual device for mobile robots. The suggested control system makes use of a video collection and processing algorithms in tandem with two Takagi-Sugeno fuzzy logic processors for avoiding obstacles and objective pursuit.…”

Corridor 3D Navigation of a Fully-Actuated Multirotor by Means of Bee-Inspired Optic Flow Regulation

“…One may find that OF can be confidently used in conjunction with other control and navigation techniques (e.g. Fuzzy Logic [12] and real-time way-point-based 3D local path planning [13]) to enhance the navigation capabilities of UAVs. In addition, the literature also suggests that, even for outdoors applications where GNSS are enable, OF is a powerful data source since it permits to improve the reliability of flight controllers [14], [15].…”

Novel Solution of Topological Recognition of Indoor Objects Based on Optical Flow and Planar Attributes

“…Especially, a number of optical flow-based solutions have been developed for visionbased robot navigation, e.g. [14] and [15]. These approaches, however, use complex computations based on quantitative (metric) decision making implementations.…”

“…The limitations of these solutions are poor recognition of situations and infirm robustness to image noise of the working environment. Some others can find out obstacle details from the optical flow [15,18] or indoor object recognition [19,20]. These approaches, however, are not able to detect both indoor landmarks and obstacles simultaneously.…”