Enhanced path planning for robot navigation in Gaussian noise environments with YOLO v10 and depth deterministic strategies

Facing Gaussian noise from severe rain and haze, we employ multi-scale YOLO v10 for obstacle detection amidst high noise and DDPG (Deep Deterministic Policy Gradient) for enhanced path planning. YOLO v10 is used to annotate obstacles, and different input scales are set to deal with Gaussian noise. Recognition accuracy is optimized using cross entropy loss function. The DDPG algorithm is applied to update the path planning strategy, define the state space of robot position, velocity, and Laser Radar (LiDAR) readings, and output possible movement directions and velocities as action outputs. A reward function is designed to motivate the agent to learn. The weighted average method is used to fuse the visual information of YOLO v10 with LiDAR data. The robot’s status and surrounding environment are tracked through real-time feedback mechanism. The path planning is dynamically adjusted, and an intelligent navigation system is built. The research results show that the average recognition rate of the system for obstacles is 0.95, and the success rate of avoidance under extreme weather conditions of heavy rainfall is 88.0%. The system can effectively cope with various Gaussian noise environments and achieve precise path planning decisions.