Authors: M Devendar Reddy, S Akhil Reddy, Anand Jawdekar, N Saiprem,, B UdayKiran Reddy
Abstract: The results of the experiment indicate that combining reinforcement learning with vision-based techniques can offer signifi- cant improvements in autonomous naviga- tion [2],[5]. Scale-Invariant Feature Trans- form (SIFT) was particularly effective in recognizing both the delivery target and potholes with a high degree of accuracy [7],[10], ensuring reliable performance under varying conditions. Canny edge detection and the Hough Line Transform proved to be highly efficient tools for lane identification [4],[6], allowing the robot to maintain pre- cise lane alignment during movement. Fur- thermore, IMU-based orientation correction provided additional robustness, preventing errors caused by yaw drift and other orien- tation issues [7]. Collectively, these meth- ods enabled the robot to adapt dynami- cally to its environment and demonstrate consistent success across repeated trials [2]. These findings suggest that the proposed framework not only addresses the imme- diate problem of pothole detection [9],[10] but also enhances the overall safety and reliability of autonomous vehicles. Look- ing ahead, the study shows strong poten- tial for real-world applications, as it pro- vides a scalable and practical solution that can be integrated into future self-driving systems to improve passenger safety, vehi- cle durability, and overall traffic efficiency [5]. Autonomous driving continues to be one of the most promising innova- tions in intelligent transportation sys- tems, but real-world challenges such as potholes still pose serious risks to safety and efficiency [2],[5]. This study explores the application of rein- forcement learning for addressing the issue of pothole detection and avoid- ance in self-driving cars [2]. To evalu- ate the framework, a detailed robot simulation was built in the Webots environment, making use of Python programming and OpenCV for vision processing [8]. Within this setup, the robot was designed to complete three key tasks: it first identifies a delivery target symbolized by a gnome placed in the environment, then transitions into lane-following mode to maintain safe navigation, and finally responds appropriately by halting when a pot- hole is detected on its path [8]. Each of these components plays a crucial role in ensuring safe and reliable op- eration. The framework integrates several technologies, including real- time computer vision for object detec- tion, IMU sensor feedback for orien- tation correction, and motor control for smooth navigation [7]. These el- ements work together to enable the robot to perceive its surroundings, adapt to hazards, and make sequen- tial decisions that reduce the risk of accidents [2]. The results of the experiment in- dicate that combining reinforcement learning with vision-based techniques can offer significant improvements in autonomous navigation [2],[5]. Scale- Invariant Feature Transform (SIFT) was particularly effective in recogniz- ing both the delivery target and pot- holes with a high degree of accuracy [7],[10], ensuring reliable performance under varying conditions. Canny edge detection and the Hough Line Trans- form proved to be highly efficient tools for lane identification [4],[6], al- lowing the robot to maintain pre- cise lane alignment during movement. Furthermore, IMU-based orientation correction provided additional robust- ness, preventing errors caused by yaw drift and other orientation issues [7]. Collectively, these methods enabled the robot to adapt dynamically to its environment and demonstrate consis- tent success across repeated trials [2]. These findings suggest that the pro- posed framework not only addresses the immediate problem of pothole de- tection [9],[10] but also enhances the overall safety and reliability of au- tonomous vehicles. Looking ahead, the study shows strong potential for real-world applications, as it provides a scalable and practical solution that can be integrated into future self- driving systems to improve passenger safety, vehicle durability, and overall traffic efficiency [5].
DOI: https://doi.org/10.5281/zenodo.17174389
