# Target Recognition and Navigation Path Optimization Based on NAO Robot
## 1 Introduction  
In recent years, the technology of the fully autonomous humanoid robot has developed rapidly and become the most popular research field. Robots can be used to replace workers to finish a lot of complex work and detect harsh environments. How to make robots integrate well into human life is also a hot topic [1]. Robot-based target location and navigation path planning are the most important functions of humanoid robots. The NAO robot as a bipedal robot compared to multi-legged robots and wheeled robots has the advantage of flexibility in a low gait environment, and for some specific tasks, such as target object grasping, etc., it can perform more like a human [2]. Many research results have been on target location and navigation path optimization based on the NAO robot. For example, the camera calibration is carried out to determine the parameters which define the relationship between the reference 3D coordinate system and the camera coordinate frame. These parameters are combined with the transformation and the parameters related to the camera [3]. According to the recognized objects in the robot database, the robot's monocular camera can realize the object recognition and avoid obstacles through some measures. In actual experiments, the Nao robot can effectively perform actions in a messy environment [4], improving the gait planning under complex working conditions to savecomputing resources and improving the utilization of the hardware system [5]. Aiming at the problem of navigation path optimization, researchers have proposed many methods, such as the SLAM theory for the recognition in [6] and the localization in an unknown environment or the algorithms based on the artificial intelligence for navigation in [7; 8; 9]. The SLAM theory is useful to help robots build maps in an unknown environment, so it was adopted in this paper. The ant colony optimization algorithm (ACO) is proposed to find the optimal path in [10; 11; 12; 13; 14]. Meanwhile, the genetic algorithm (GA) is proposed to solve the path optimization problem in [15]. Furthermore, more optimization algorithms such as the particle swarm optimization algorithm (PSO) and the neural network algorithm are also applied to solve the path optimization problem [16; 17; 18]. However, the disadvantage of these traditional optimization algorithms is that it is easy to fall into the local optimum, and the convergence speed is slow. The best optimization results of these traditional optimization algorithms may not be found, and they need a long time to be explored. To get more efficient and stable optimization results, the bi-directional parallel search strategy and the simulated annealing algorithm are introduced to improve the traditional artificial bee colony algorithm (ABC) in this paper. By improving the artificial bee colony algorithm (IABC), not only the optimal path can be obtained based on the built environment map, but also the convergence speed is faster than other optimization algorithms [19; 20; 21]. This paper mainly takes the Nao robot as the research platform to solve the problems of the robot space target localization and navigation path optimization. The main contribution of this paper is presented as follows: (1) The K-means algorithm and Q-learning algorithm are combined to make the NAO robot avoid obstacles in the process of moving. The former algorithm is used to classify the obstacle and calculate its accurate distance, and the latter algorithm is adopted to help the robot to build an environment map. (2) The improved artificial bee colony algorithm (IABC) is proposed based on the bi-directional parallel search strategy and simulated annealing algorithm after the navigation map built. The IABC algorithm is applied to find an optimal path in the navigation map and has faster convergence and better solutions compared to the traditional optimization algorithm. (3) A positioning model based on monocular vision is established according to the principle of small hole perspective.