A Mixed Fast-RRT*-A* To Solve the Problem of Generating Nodes near Obstacles for Optimal Paths

Abstract

This paper will focus on the application of machine vision in mobile robots and take moving to the appropriate position and grasping the designated target as the task. This paper will describe and simulate the vision technology that mobile robots need to apply in the task process. This paper mainly uses the camera as the sensor. The difficulties of vision technology are mainly divided into three parts: scene depth information acquisition, positioning and mapping, and image processing. In order to obtain the depth information of the scene, this paper mainly introduces the depth information acquisition methods of monocular camera and binocular camera. In the aspect of localization and mapping, this paper mainly introduces the simulation of visual odometer to understand the basic process of mobile robot obtaining navigation map and its own route. Then, the gray gradient 2D maximum entropy algorithm is introduced to segment the scene and target, and extract features to judge the required target. Compared with other segmentation algorithms, the gray gradient 2D maximum entropy algorithm has higher segmentation accuracy, but the operation time is longer. This paper has simply optimized its operation efficiency. Finally, this paper describes the positioning method of grasping the object with a two degree of freedom manipulator using the knowledge of inverse kinematics. Because of the epidemic situation, schools cannot obtain experimental equipment. This paper mainly demonstrates the effectiveness of the algorithm through simulation.

A method of tracking and positioning of industrial robots

“…Researchers have proposed several solutions, such as fuzzy reflector-based localization and color reflector-based self-localization. The main drawbacks of these methods are that the amount of computation power they need is too large and the antiinterference ability is poor (Suwoyo, Hidayat, et al, 2022;Tai & Yeung, 2022).…”