This technology integrates a fuzzy obstacle avoidance algorithm with reinforcement learning to boost quadruped robot mobility and stability. Using LiDAR and IMU, the fuzzy system adjusts speed and direction in real time to avoid collisions. Reinforcement learning further ensures stable movement under uncertainty, supporting real-time obstacle avoidance, mapping, and path planning, thus demonstrating seamless integration of perception and control.

This system integrates a proprietary fuzzy obstacle avoidance algorithm with reinforcement learning to enhance quadruped robot stability and obstacle avoidance in complex environments. Using real-time perception, the fuzzy algorithm detects obstacles and adjusts speed and direction, while reinforcement learning supports mapping and planning. By unifying perception, decision-making, and control, the system establishes a reliable foundation for navigation under dynamic and uncertain conditions.

This technology integrates reinforcement learning control with fuzzy obstacle avoidance, enabling applications in intelligent inspection, energy facility maintenance, disaster relief, warehouse management, and logistics. With real-time perception, path planning, and dynamic decision-making, quadruped robots achieve stable autonomy in complex environments. It reduces human risk, boosts efficiency, drives broad industrial adoption, fostering intelligent upgrades and sustained innovation.