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Proximal Policy Optimization Based Autonomous Navigation in Dynamic Environment Using LiDAR-Camera Fusion Technique

SeherCollege of CS and Technology, Nanjing University of Aeronautics and Astronautics,Nanjing,ChinaSibghat Ullah BazaiBalochistan University of Information Technology, Engineering & Management Sciences (BUITEMS),Department of Computer Engineering,Quetta,PakistanAlamgir NaushadNational University of Sciences and Technology (NUST),Department of Computer Science,Quetta,PakistanUzair Aslam BhattiHainan Agricultural SchoolAnorgul AshirovaHayitov Abdulla NurmatovichUrgench State University,Department of Transports Systems,Urgench city,Uzbekistan

2025en

Ochiq kirish: yoʻq

ABI

Annotatsiya

Smart robots are being deployed to autonomously navigate complex and dynamic indoor environments. Autonomous navigation in unknown and dynamic environments is a major challenge for robots, especially when it comes to making safe decisions in complex environment. In this research, we use the Proximal Policy Optimization (PPO) algorithm combined with LiDAR-camera sensor fusion to address this problem. While using only 3D LiDAR or a camera often leads to failure in complex scenes, fusing the two sensors provides a much clearer and more reliable understanding of the environment. This improved perception helps the robot avoid dynamic obstacles and make safer navigation choices. This research results show a clear improvement in both training performance and safety: the robot achieves a 75% average success rate across episodes of training and identifies important environmental features with 80% probability. Overall, this research offers a practical and effective solution for safe autonomous navigation in challenging, complex environments.

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Mavzular

Robotic Path Planning Algorithms Robotics and Sensor-Based Localization

Identifikatorlar

DOI: 10.1109/dlcv65218.2025.11088840

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