# Motion Control Method of Bionic Robot Dog Based on Vision and Navigation Information
## 1 Introduction  
In the process of humans' subjective initiative, there are many dangerous situations that human beings cannot safely perform, such as forest fire inspection, planet detection, military material transportation, earthquake relief, nuclear field experiments with pollution, mine clearance and explosion removal, and deep-sea field exploration. Biomimes play an irreplaceable role in performing this series of difficult and dangerous tasks [1]. However, the relevant technology for bionic mechanical dogs is currently not mature, and all countries are continuously increasing their research and development investments in this field. Therefore, the research on bionic mechanical dogs has great practical significance for improving national soft and hard strength and human security, and it has important practical application value. Bionic robot technology is a product that covers many disciplines. Robots can currently be divided into wheeled robots, crawler robots, foot robots and so on according to the difference in the actuator [2]. The bionic robot dog studied in this paper belongs to the foot-type robots. Because the landing point of the bionic robot dog is discrete and controllable, this feature makes the bionic robot dog able to cope with different geographical environments. In addition, the unique bionic leg structure of the bionic robot dog greatly increases the degree of freedom of the whole system, so the bionic robot dog is more stable and flexible in complex road conditions [3]. The bionic robot dog has a high requirement for the accuracy of pose positioning and tracking and needs to reach a millimeter level oferror to achieve accurate control [4]. Therefore, it is necessary to optimize the control of the robot dog, reduce the geometric error of the robot dog, and improve the positioning and tracking ability of the robot dog's end posture [5]. Research on robust control of robot dogs is of great significance to improving the operating accuracy and efficiency of robot dogs [6]. The bionic robot dog is affected by spatial disturbance factors in the process of determining the pose, and the kinematic model of the robot dog is a multicoupling nonlinear model, which is difficult to control effectively and is prone to control errors [7]. Accurate visual navigation and calibration solve the kinematic equations of the robot dog, realize the optimal solution of the controlled object parameters of the robot dog, and realize robust control. The traditional robust control methods for bionic robot dogs mainly include the fuzzy PID control method, pose fusion filter control method, synovial integral control method, inversion control method, etc., so that the controller parameters are adaptively adjusted in a limited number of linear models, and combined with kinematic loop weighting, the robot dog's end pose positioning is achieved, and a certain control efficiency is achieved [8]. Some scholars have proposed a bionic robot dog control algorithm based on a variable structure PID fuzzy neural network. The Smith structure is used to design the object structure model of bionic robot dog control, and the forward three-layer adaptive PID neural network model is used as a learner to realize the bionic robot dog [9]. The control optimization improves the position and pose control accuracy of the robot dog, but the calculation cost of the algorithm is high, and the real-time performance of the robot dog position and pose adjustment is poor [10]. Some scholars have used the improved closed-loop detection algorithm based on the spatial position uncertainty constraint to control the motion state of the robot dog [11]. The outer control loop is designed with the idea of synchronous phase modulation control, and the closed-loop detection model is designed with a DC/AC inverter model. The tracking and identification of the dog path can improve the robustness of the bionic robot dog grasping control, but this method is vulnerable to small disturbance factors, resulting in output measurement error [12].