Academic department under which the project should be listed

SPCEET - Robotics and Mechatronics Engineering

Faculty Sponsor Name

Muhammad Hassan Tanveer

No Human subjects involved

Abstract (300 words maximum)

The aim of this article is to provide an obstacle avoidance solution for navigating a robot from one point to another. The robots that are being considered are the NAOHumanoid robot and the wheeled robot Rosbot 2.0. This article's main purpose is to understand how robots work together to minimize positioning errors. The image processing robot (NAO) will be able to instruct the wheeled bot (Rosbot) to navigate around obstacles more accurately by incorporating Inverse Perspective Mapping methods (IPM) and the A-Star Algorithm. This approach demonstrates the ability to improve robot collaboration in order to diagnose various collisions and develop various path-following methods. Despite knowing that odometry is a widely used technique for calculating robot position, it has some disadvantages in the long run. To combat this, we utilized Inverse Perspective Mapping and the A-Star algorithm to find solutions to these weak points. When IPM is used, computing the position of the obstacles in relation to the wheeled robot improves the accuracy of the path network. The path network can also be evaluated using the A-Star algorithm to find the most direct route. The appeal of this technique is that it simplifies the navigation process by analyzing data from a birds-eye perspective and computing decisions that will inform the wheeled robot to take the shortest path. A series of experiments in a static environment is used to evaluate the proposed approach's performance.

Project Type

Event

Included in

Robotics Commons

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Cooperative localization between robots using vision and path planning algorithm

The aim of this article is to provide an obstacle avoidance solution for navigating a robot from one point to another. The robots that are being considered are the NAOHumanoid robot and the wheeled robot Rosbot 2.0. This article's main purpose is to understand how robots work together to minimize positioning errors. The image processing robot (NAO) will be able to instruct the wheeled bot (Rosbot) to navigate around obstacles more accurately by incorporating Inverse Perspective Mapping methods (IPM) and the A-Star Algorithm. This approach demonstrates the ability to improve robot collaboration in order to diagnose various collisions and develop various path-following methods. Despite knowing that odometry is a widely used technique for calculating robot position, it has some disadvantages in the long run. To combat this, we utilized Inverse Perspective Mapping and the A-Star algorithm to find solutions to these weak points. When IPM is used, computing the position of the obstacles in relation to the wheeled robot improves the accuracy of the path network. The path network can also be evaluated using the A-Star algorithm to find the most direct route. The appeal of this technique is that it simplifies the navigation process by analyzing data from a birds-eye perspective and computing decisions that will inform the wheeled robot to take the shortest path. A series of experiments in a static environment is used to evaluate the proposed approach's performance.