Gesture Recognition And Swarm-Intelligent Path Planning For The Micro Soccer Robot System

Robot soccer system, one of the hotspots in the field of artificial intelligence (AI)in recent years, is a typical multi-agent system and it provides a standard testingplatform for evaluation of Artificial Intelligence theories and algorithms. FIRA(Federation of International Robot-soccer Association) MiroSot (Micro Robot WorldCup Soccer Tournament) is one of the most popular robot soccer tournaments. MiroSotis a kind of centralized control robot soccer systems which includes four subsystems toget the field situation rapidly and accurately and make reasonable decision, such as:vision subsystem, decision-making subsystem, communication subsystem and robotsubsystem. Many researchers have devoted to this project because it is involved inmany research fields, such as robotics, computer vision, sensor-fusion, real-timereasoning, path planning and motion control, wireless communication, machine learning,autonomous agents, multi-agent collaboration, and so on. Rapid robust gesturerecognition algorithm and swarm-intelligent path planning algorithm are well studied inthis dissertation for their importance to high decision-making.The main contributions of this dissertation are summarized as follows:(1) The system architecture and working principle of MiroSot, and the structure,fucction and hardware design of all four MiroSot subsystems, including visionsubsystem, decision-making subsystem, communication subsystem and robotsubsystem, are introduced respectively. A common framework of mobile robotpath planning is proposed. The description and characteristics of mobile robot pathplanning problem, the traditional methods and the up-to-date methods areintroduced, and the advantages and disadvantages of these algorithms are analyzed.Furthermore, the trend of mobile robot path planning is described.(2) A color tag recognition algorithm based on the shorter axis segmentation andsuccessive approximation technique (SASA) is proposed. By analyzing thesymmetry of color tag, a shorter-axis based successive approximation algorithm isproposed. The experiment results show that the proposed gesture recognitionalgorithm is of lower computing complexity and higher recognition accuracy.(3) A novel phase-correlation based gesture recognition algorithm (PCGR) in MiroSot is proposed,and the Octa-log-polar Fourier transform (OLPFT) is presented to improve the computingspeed and precision. In the proposed algorithm, the object image and the referenced image are transformed into log-polar coordinate space after FFT, by which rotation and scaling inCartesian coordinate space can be reduced to 2-D translation in log-polar coordinate space.The orientation of the robots can be estimated by phase correlation technique. Octa-log-polarFourier transform (OLPFT) is proposed to estimate the log-polar DFT. The OLPFT estimatesthe DFT on octa-log-polar grid, which is geometrically more similar to the log-polar grid thanpseudo-log-polar grid which can lead to higher accuracy. Experimental results show that theproposed algorithm has high precision and robustness.(4) A novel Particle Swarm Optimization Algorithm inspired by Lotka-Volterra Model(LVPSO) is proposed in this paper to avoid the premature convergence problem.Path planning is an optimistic computation problem essentially. The famousLotka-Volterra Model in Ecology is introduced into basic particle swarmoptimization algorithm. Two different cooperative-competitive schemes have beendiscussed. The diversity of particles is increased by intraspecific and interspecificcompetition, and the ability for particles to break away from the local extremum isimproved remarkably. The experimental results show that the proposed LVPSOalgorithm can converge in higher speed and higher precision by optimizing fivetypical benchmark functions.(5) A novel path planning approach using LVPSO and Ferguson splines (LVPSOFS)was proposed to get an optimal smooth path for a micro soccer robot. In theproposed algorithm, a path is described as a string of Ferguson splines. The pathplanning is then equivalent to optimization of parameters of particular cubicFerguson splines. The proposed Particle Swarm Optimization Algorithm inspiredby Lotka-Volterra Model (LVPSO) was introduced to optimize the path for its fastconvergence and global search character. Experimental results prove therationality and practicability of the proposed algorithm, which can getconvergence rapidly, with a collision-avoiding smooth optimal path being plannedfleetly.