Introduction
Robot soccer grows rapidly in recent years, including FIRA and ROBOCUP two major events which greatly promoted the humanoid soccer robot technology development and international exchanges . However, the current level of development point of view, humanoid soccer robot technology is still far from reaching human expectations.
Objectives
1.
This paper will focus on the the capability of vision and decision of
Androsot.
2.
Try to improve the motion planning method.
Method/solution
The current Androsot uses semi-autonomous control mode, which means that the robot has no independent capability of vision and decision. Therefore, Androsot system consists of four subsystems include robots, vision, decision and communication. Humanoid robot in robot system has 16 degrees of freedom, and no vision or decision system. So the visual and decision capacity is depending on the external global vision and centralized control decision making. The digital camera, which is hanging on the central top of the field, and the image processing program constitute the vision system. The camera captures the global image continuously. The program processes the image and transfers the position data to decision system. The decision system records and analyzes the data, then makes the strategy according to the position data. After that, the decision system converts the strategy to robot command and push to communication system. At last the communication system sends the command to the robot and the robot acts automatically. This method will focuses on the vision system and finds that light influences the system obviously. When the light intensity changes, in order to recognize the object correctly, the vision system must capture the color patch repeatedly. This means a lot of unnecessary time waste. Therefore, the vision system can get improvement in adaptability of the light intensity In this method, color database adaptive algorithm is designed according to computer vision theory, color theory and the relevant experiment data. This algorithm calculates the average light intensity in the field, and maps it to the color database. Then the

color database can be generated automatically. Color database adaptive algorithm is able to handle the light problem generally. However, the light intensity comes to the extreme situation, the vision system stops working.
Therefore, the improved color database adaptive algorithm must be design. The improve algorithm designs a function to evaluate the image, which is captured by the camera. The improved algorithm also designs a method to adjust the inner parameter of the camera according to result of the function. If the color shifts, adjust the white balance parameter; if the light intensity is not proper, adjust the shutter or the gain parameter. When the result of the function is in acceptable range, the improved algorithm is finished and then the vision system is able to work normally. The improved algorithm achieves the adaptability to light intensity of extreme situation.
This time we will focus on humanoid soccer robot motion planning methods. First, the analysis of the humanoid robot soccer body structure , the design of the humanoid soccer robot control system. Application to establish homogeneous coordinates representation of the robot kinematics model . Based on Lagrange equation to establish a single support phase kinetic model . Secondly, the humanoid robot soccer action unit planning research. To give policy-based motion planning , the robot movement during the main action were classified and defined . Several typical basic gait was planned, also analyzed the other basic action unit gait planning a major problem. For typical humanoid robot soccer fall process, given humanoid robot soccer fall of self-protection strategies . Finally, humanoid soccer robot path planning methods must be study. Taking into account the given path , humanoid robot soccer robot mechanism constraints on movement efficiency , proposed the concept of path tortuosity , path planning and optimization process as the objective function . Using ACO algorithm to solve the problem of path optimization . Proposed based on the best travel crossover operation improved ant colony algorithm , experimental verification, improved algorithm to improve the convergence speed and search efficiency.
Conclusion
In planning the improvement that we must take to make a better solution is the main thing about androsot. In Androsot the vision, motion, and decision making is the best way to improve the way it is.