Collaborative Localization and Gait Optimization of SharPKUngfu Team

Abstract

In this chapter, we introduce the recent progress of sharPKUngfu Team which participates in the RoboCup Four-Legged League since 2004. sharPKUngfu Team is a robot soccer team from Peking University, China. In July 2005, we got the third place in the RoboCup China Open. In June 2006, our sharPKUngfu Team has participated in the technical challenge of RoboCup 2006. In this event, our Medal Awarding challenge got the eighth place in the Open Challenge. In October 2006, we got the champion in the RoboCup China Open 2006, both in soccer competition and technical challenge. In July 2007, we participate in the RoboCup 2007 and got the fourth place in the technical challenge. Our research in robot soccer focuses on robot vision, multi-robot cooperation strategy, collaborative localization in dynamic environment, quadruped gaits optimization and intelligent behavior. We focus this chapter on localization and gait optimization which are the fundamental parts in soccer robotics. Recently, we successfully apply self-learning image-retrieval approach and collaboration in self localization in robot soccer. This improvement eliminates the problems of image-retrieval method and collaboration mentioned in previous research. By using this approach, robots can play soccer under more natural conditions towards real human soccer environment. We organize the localization part as follows. At first, a brief overview of current self-localization approaches is presented. Secondly, we introduce the human cognition inspired localization with self-learning experience. Specific algorithms for image features collection and self-learning process are described. Then, the dynamic reference object based method for collaborative localization is demonstrated in detail. Experimental results in real robot soccer are shown in the end. We also discuss current challenges and future works of localization in soccer robotics. How to get high-speed walking and running gaits is another problem in soccer robotics. Different to existing literature which uses Genetic Algorithms (GA) based gait optimization methods, we present the implementation of Particle Swarm Optimization (PSO) in generating high-speed gaits for a quadruped robot, specifically the Aibo, which is the commercial robot made in Sony. PSO has been proven to be effective in solving many global optimization problems and in some areas outperform many other optimization approaches including Genetic Algorithms. In this part, at first, we overview the basic PSO and Adaptive PSO (APSO) with comparison to other optimization approaches. After that, with the