Real-time path planning of drilling robot using global and local sensor information fusion: mining-oriented validation

This paper introduces a new path planning system designed for drilling robots operating in challenging environments prone to rockbursts. The system effectively combines global and local path information from optical and laser sensors to achieve real-time obstacle avoidance. The global path is planned using an improved ant colony algorithm, while local path optimization is achieved through a pheromone updating model and the TEB (Time Elastic Band) algorithm. This approach allows for efficient and dynamic path adjustments. The system’s effectiveness was validated through experiments conducted on a purpose-built platform. The results show that the proposed method significantly improves path planning efficiency, reducing path length by 4.80% and runtime by 27.56% compared to existing methods. This makes it a promising solution for safe and efficient drilling operations in hazardous environments.