Abstract
This paper describes a 3D path planning system that is able to provide a solution trajectory for the automatic control of a robot. The proposed system uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Our proposal includes a collision-free path planner based on the Rapidly-exploring Random Trees variant (RRT*), for a safe and optimal navigation of robots in 3D spaces. Results on RGB-D segmentation and recognition, point cloud processing, and comparisons between different RRT* algorithms, are presented.
Highlights
Robotic systems have been playing an important role in the modern world
Our approach consists of a path planning system that obtains the environment description from a Kinect V2 point cloud, recognizes the start and goal point by computer vision techniques and solves the path planning issue using a variant of the RRT algorithm
As we can see in the results tables, the RRT* Goal algorithm significantly decreases the time of finding a feasible solution (Goal time), providing more time and computational resources to the path obstacles scenario scenario and and 600
Summary
Robotic systems have been playing an important role in the modern world. They are applied in several areas: in manufacturing, entertainment, the toy industry, the medical field, exploration, military and multimedia applications [1,2,3]. Navigation includes algorithms for perception and motion estimation, and for path planning and optimization in order to connect the start point with the goal point. Several techniques, such as proximity sensors, have been used for perception. Our approach consists of a path planning system that obtains the environment description from a Kinect V2 point cloud, recognizes the start and goal point by computer vision techniques and solves the path planning issue using a variant of the RRT algorithm. Experimental results and conclusions are presented in the last two sections
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