Real Time Navigation of Robot Using Path Memorizing Algorithm
Author(s):
B.Pranav Reddy, V.Tejaswi, B.Ganesha, M.Saiteja Goud, M.Hemanth
Keywords:
Automated Robot, Path Memorizing, Differential Drive, Ackerman’s Principle, IR remote.
Abstract
The real-time navigation of robots with a path memorizing algorithm is a crucial aspect of autonomous robotics. This study introduces an innovative approach to enhance robot navigation by incorporating a dynamic path memorization algorithm. Unlike traditional methods, this algorithm adapts to changing environments in real-time, allowing the robot to efficiently navigate through dynamic terrains.The core of the proposed algorithm lies in the robot's ability to memorize and update its path as it encounters new obstacles or alterations in the surroundings. This adaptability ensures that the robot can respond promptly to unforeseen challenges, making it suitable for applications in diverse and unpredictable environments. By continuously updating its path memory, the robot optimizes its trajectory based on real-time data, resulting in improved efficiency and safety.The algorithm utilizes sensor data, such as LiDAR and camera inputs, to perceive the environment and make informed decisions. Machine learning techniques are integrated to enhance the robot's ability to recognize and memorize complex patterns in the surroundings. This amalgamation of real-time data processing and machine learning empowers the robot to navigate seamlessly through intricate environments. The experimental results demonstrate the effectiveness of the proposed approach, showcasing superior performance compared to traditional navigation methods. The robot successfully adapts to dynamic scenarios, efficiently avoiding obstacles while reaching its destination. This research contributes to the advancement of autonomous robotics, providing a foundation for the development of intelligent and adaptable robotic systems capable of real-time navigation in complex environments.
Article Details
Unique Paper ID: 162080

Publication Volume & Issue: Volume 10, Issue 7

Page(s): 373 - 377
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