題目：Intelligent Multi-Robot Cooperation for Target Searching and Foraging in Completely Unknown Environments
報告人： Simon X. Yang (楊先一) 教授，加拿大Guelph大學
時間：2013.5.4, 上午10:30
地點：信息工程學院（新校）信息樓330

主講人簡介：
Simon X. Yang(楊先一)，本科畢業于北京大學、碩士畢業于美國休斯頓大學（University of Huston）、博士畢業于加拿大阿爾伯塔大學(University of Alberta)。現為加拿大Guelph大學高級機器人與智能系統實驗室主任，終身教授，博士生導師。
主要研究領域：移動機器人路徑規劃與控制、多傳感器信息融合、無線傳感器網絡、智能計算與優化、多機器人系統等。國際雜志《IEEE Transactions On Neural Networks》、《IEEE Transactions On Systems, Man, And Cybernetics, Part B》、《International Journal of Robotics and Automation》、《Control and Intelligent Systems》副主編; 國際雜志《International Journal of Computational Intelligence and Applications》、《International Journal of Automation and Systems Engineering》、《Journal of Robotics》、《International Journal of Computing and Information Technology》、《International Journal of Information Acquisition》編委.

報告內容簡介：
Multi-robot cooperation can significantly improve work efficiency and provide with better robustness and adaptability than a single robot. This research focuses on the effective cooperation strategy for multi-robot systems. Target searching in completely unknown environments is a challenging topic in multi-robot exploration. The multi-robot system has no information about the environments except the total number of targets, and a target searching task is accomplished when all the targets are acquired. Autonomous and reasonable exploration is expected. In this research, a combined Option and MAXQ hierarchical reinforcement learning algorithm is firstly developed to promote the learning ability to handle tasks in new unknown environments. Though it can work in some situations, the indispensable learning process prevents it from efficiently dealing with dynamic tasks in unknown environments. A potential field-based particle swarm optimization (PPSO) approach is presented. A novel potential field-based fitness function is developed for the PSO algorithm structure to provide the exploration priority evaluation for undetected areas. The potential function is based on some designed cooperation rules. Furthermore, an improved PPSO approach with dynamic parameter tuning is applied to handle tasks in complex environments. As an extension, cooperative foraging tasks are investigated. In addition, fuzzy obstacle avoidance is integrated to improve the smoothness of the robot trajectory. The scheme is tested under different scenarios in simulation experiments to validate the flexibility and effectiveness. In simulation studies, scenarios with obstacles and uncertainties are considered to demonstrate the robustness and adaptability.