robot path finding using image stitching

Project Title

robot path finding using image stitching

Project Area of Specialization Artificial IntelligenceProject Summary

Starting from taking 3 images from the scene and aligning them through image stitching. We will perform Feature based panaromic image in matlab. As a result of image stitching we will get a panoramic image.Now for the robot to find path we will do segmentation of the path on the basis of color. The work focus on providing a path in an environment for a robot for its ease of movement, detecting and avoiding obstacles in an environment using a single camera . Robot moves by identifying free space on floor. Wherever floor is visible and free, it moves. Method for floor segmentation has been used.Robot change its direction with different angle and continues to move. Several numbers of photo-reflectors can be used for imaging processing image sensors can also be used. The sensors with a high resolution are required to sense the path.An Algorithm will be designed for the autonomous robot for path finding in the specified set of images , on which image stitching process has already been done.The objective is to provide an optimal path. Path finding involve ?nding a feasible path from the starting point to the target point.

Project Objectives

We will create such a scenario for the robot that it can find the path easily. We will have a set of pictures stitched together (like a panorama) using image registration and then by looking to that we will design a path for the robot. Image stitching is the process of aligning two or more images of the same scene. This process involves designating one image as the reference image, also called the fixed image, and applying geometric transformations or local displacements to the other images so that they align with the reference. For mobile robots, the aim of path finding is to ?nd a feasible path in a speci?c environment. The aim of the robot path finding is to search a safe path for the mobile robot. Also the path is required to be optimal.  This path begins at the starting point (S) and ends at the target point (T). We explain the aspects of the problem in the following text. They include the environmental modeling, the path form, and the de?nitions of objectives. In this idea, the workspace is considered to be a continuous two-dimensional space. In this continuous workspace, obstacles are assumed to be static polygons, and the robot is assumed to be a single point.A path consists of successive segments. By summing all the segments of this path, the length of the entire path can be obtained. We assume that path p= [S=p0,p1,p2, ...,pn,pn+1=T]contains a starting point (S), target point (T),and n consecutive points.

We will create such a scenario for the robot that it can find the path easily. We will have a set of pictures stitched together (like a panorama) using image registration and then by looking to that we will design a path for the robot.

Image stitching is the process of aligning two or more images of the same scene. This process involves designating one image as the reference image, also called the fixed image, and applying geometric transformations or local displacements to the other images so that they align with the reference.

For mobile robots, the aim of path finding is to ?nd a feasible path in a speci?c environment. The aim of the robot path finding is to search a safe path for the mobile robot. Also the path is required to be optimal. 

This path begins at the starting point (S) and ends at the target point (T). We explain the aspects of the problem in the following text. They include the environmental modeling, the path form, and the de?nitions of objectives.

In this idea, the workspace is considered to be a continuous two-dimensional space. In this continuous workspace, obstacles are assumed to be static polygons, and the robot is assumed to be a single point.A path consists of successive segments. By summing all the segments of this path, the length of

the entire path can be obtained. We assume that path

p= [S=p0,p1,p2, ...,pn,pn+1=T]contains a starting point (S), target point (T),and n consecutive points.  

Project Implementation Method

Techniques that are used in the robot path finding using image stitching in order to achieve improvements are algorithms presented to create a path for a robot while detecting and avoiding obstacles of different shapes in any specific environment. Here a camera is used to acquire an image of the environment. The image analysis is based on general processing in MATLAB.The images will undergo a process of image stitching.  Robot Navigation, path finding, Vision based Navigation, image stitching, Image Concatenation, segmentation.  For a mobile robot to navigate successfully to a goal whilst avoiding both static and dynamic obstacles is a challenging problem. While the problem is largely solved for robots equipped with active range-finding devices, for a variety of reasons, the task still remains challenging for robots equipped only with vision sensors. Vision is an attractive sensor as it helps in the design of economically viable systems with simpler sensor limitations. It facilitates passive sensing of the environment and provides valuable semantic information about the scene that is unavailable to other sensors. Several different approaches to avoiding obstacles have been developed in recent years, some of which are computationally intensive. With the help of a camera, a path can be generated for the robot.

Techniques that are used in the robot path finding using image stitching in order to achieve improvements are algorithms presented to create a path for a robot while detecting and avoiding obstacles of different shapes in any specific environment. Here a camera is used to acquire an image of the environment. The image analysis is based on general processing in MATLAB.The images will undergo a process of image stitching.

 Robot Navigation, path finding, Vision based Navigation, image stitching, Image Concatenation, segmentation.

 For a mobile robot to navigate successfully to a goal whilst avoiding both static and dynamic obstacles is a challenging problem. While the problem is largely solved for robots equipped with active range-finding devices, for a variety of reasons, the task still remains challenging for robots equipped only with vision sensors. Vision is an attractive sensor as it helps in the design of economically viable systems with simpler sensor limitations. It facilitates passive sensing of the environment and provides valuable semantic information about the scene that is unavailable to other sensors. Several different approaches to avoiding obstacles have been developed in recent years, some of which are computationally intensive. With the help of a camera, a path can be generated for the robot. 

Benefits of the Project

Until now, many methods have been used for path finding of mobile robots. Among these strategies, the geometry space method such as Artificial Potential Field , Agoraphobic Algorithm, and Vector Field Histogram. These methods give the heading angle for avoiding obstacles. The strategy of dynamic windows , this approach is a velocity-based local planner that calculates the optimal collision-free velocity for a mobile robot. Another method used named turning point searching algorithm which consists of finding a point around which the mobile robot turns without hitting obstacles.

Until now, many methods have been used for path finding of mobile robots. Among these strategies, the geometry space method such as Artificial Potential Field , Agoraphobic Algorithm, and Vector Field Histogram. These methods give the heading angle for avoiding obstacles. The strategy of dynamic windows , this approach is a velocity-based local planner that calculates the optimal collision-free velocity for a mobile robot. Another method used named turning point searching algorithm which consists of finding a point around which the mobile robot turns without hitting obstacles.

Technical Details of Final Deliverable

For a mobile robot to navigate successfully to a goal whilst avoiding both static and dynamic obstacles is a challenging problem. While the problem is largely solved for robots equipped with active range-finding devices, for a variety of reasons, the task still remains challenging for robots equipped only with vision sensors. Vision is an attractive sensor as it helps in the design of economically viable systems with simpler sensor limitations. It facilitates passive sensing of the environment and provides valuable semantic information about the scene that is unavailable to other sensors. Several different approaches to avoiding obstacles have been developed in recent years, some of which are computationally intensive. With the help of a camera, a path can be generated for the robot. 

Final Deliverable of the Project Software SystemType of Industry IT Technologies Artificial Intelligence(AI)Sustainable Development Goals Industry, Innovation and InfrastructureRequired Resources

Techniques that are used in the robot path finding using image stitching in order to achieve improvements are algorithms presented to create a path for a robot while detecting and avoiding obstacles of different shapes in any specific environment. Here a camera is used to acquire an image of the environment. The image analysis is based on general processing in MATLAB.The images will undergo a process of image stitching.  Robot Navigation, path finding, Vision based Navigation, image stitching, Image Concatenation, segmentation.  For a mobile robot to navigate successfully to a goal whilst avoiding both static and dynamic obstacles is a challenging problem. While the problem is largely solved for robots equipped with active range-finding devices, for a variety of reasons, the task still remains challenging for robots equipped only with vision sensors. Vision is an attractive sensor as it helps in the design of economically viable systems with simpler sensor limitations. It facilitates passive sensing of the environment and provides valuable semantic information about the scene that is unavailable to other sensors. Several different approaches to avoiding obstacles have been developed in recent years, some of which are computationally intensive. With the help of a camera, a path can be generated for the robot.