The Design and Implementation of an Intelligent Guide Dog Robot Based on Multimodal Perception

Abstract

Aiming at the problems of traditional guide devices such as single environmental perception and poor terrain adaptability, this paper proposes an intelligent guide system based on a quadruped robot platform. Data fusion between millimeter-wave radar (with an accuracy of ± 0.1°) and an RGB-D camera is achieved through multi-sensor spatiotemporal registration technology, and a dataset suitable for guide dog robots is constructed. For the application scenario of edge-end guide dog robots, a lightweight CA-YOLOv11 target detection model integrated with an attention mechanism is innovatively adopted, achieving a comprehensive recognition accuracy of 95.8% in complex scenarios, which is 2.2% higher than that of the benchmark YOLOv11 network. The system supports navigation on complex terrains such as stairs (25 cm steps) and slopes (35° gradient), and the response time to sudden disturbances is shortened to 100 ms. Actual tests show that the navigation success rate reaches 95% in eight types of scenarios, the user satisfaction score is 4.8/5.0, and the cost is 50% lower than that of traditional guide dogs.