A Novel Edge Detection Algorithm for Mobile Robot Path Planning

Abstract

A novel detection algorithm for vision systems has been proposed based on combined fuzzy image processing and bacterial algorithm. This combination aims to increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time robot navigation system, where it has been applied to detect the robot and obstacles in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the robot and obstacles. Moreover, the genetic algorithm (GA) has been modified and applied to produce time-based trajectory for the optimal path. It is based on proposing and enhancing the searching ability of the robot to move towards the optimal path solution. Many scenarios have been adopted in indoor environment to verify the capability of the new algorithm in terms of detection efficiency and computational time.

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

“…A detection algorithm for vision systems that combines fuzzy image processing algorithm, bacterial algorithm, GA and A * was presented in [282] to address path planning problem. The fuzzy image processing algorithm was used to detect the edges of the images of the robot's environment.…”

Challenges for Novice Developers in Rough Terrain Rescue Robots: A Survey on Motion Control Systems

“…This collision detection algorithm depends mainly on the friction and dynamic models for functioning, which means that it can be applied to any rescue task. The friction and dynamic models are the major determinants for system accuracy [31].…”

Measurement-Based Power Optimization Technique for OpenCV on Heterogeneous Multicore Processor

“…As the evaluations are made by actual measurements performed at the target board, information on the phenotype should be transferred to the target board (line 30). Then, the measurement results are obtained at line 31 through the blocking TCP/IP read from the target board, and these evaluation results are delivered to the DSE engine (lines [32][33].…”

“…Among them, for the purposes of evaluation, we selected two popular examples written in C++: (1) the canny edge detection application, and (2) the squeezeNet [31] application (for deep neural networks). The canny edge detection is a popular and important application that is directly used in video object segmentation and tracking [32] or robot path planning [33]. It takes 1920 × 1080 images as input, and processes them through ConvertColor() and Canny().…”