Abstract:

Mobile robots are frequently utilized in the surveillance sector for both industrial and military purposes. The ability to navigate stairs is crucial for carrying out surveillance jobs like urban search and rescue operations. The research paper shows that the design methodology for a six-wheeled rover robot that can adapt to various stairs and maintain its stability based on the robot's specifications, kinematics restrictions, the maximum height, and the lowest step length needed to climb up and down the stairs is proposed. Based on a Raspberry Pi, camera, and LIDAR distance sensor, the suggested robot has the capacity to measure the stair height before starting to climb. A Convolutional Neural Networks (CNN) deep learning model is developed for the purpose of stair recognition. Additionally, stair alignment was estimated using statistical filtering on pictures and LIDAR distance reading. The robot can then decide whether it can climb the stairs or not based on its kinematics limitations and the height of the stairs as measured by our system. Result shows that our stair detection algorithm achieved an accuracy of 99.46% and a mean average precision of 99.64%. The proposed AI-Powered Robot-based stair recognition system, according to final results, effectively climbed stairs with a height range between 13 and 23 cm.

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