基于PSO算法的RoboCup2D机器人研究

对机器人体系结构、动作学习及行为的组织方式进行了研究,以演化计算为基本方法,以RoboCup2D为平台,设计了基于PSO算法的足球机器人的体系结构,解决感知、动作、和规划问题；在训练环境下,形成感知规则,优化感知相关参数,得到对信息高效快速的感知方法,并根据指定的粒度、功能、参数,对RoboCup2D机器人的原子动作进行了组合优化,得到一组带参数和执行效果描述的粒子动作；最后在赛场环境和任务驱动下,搜索粒子动作并进行组织规划,得到完成特定任务的机器人行为；RoboCup2D仿真实验表明,演化计算方法不仅能利用原子动作进行组合优化,得到适应于不同条件的粒子动作,而且能通过其在线搜索粒子动作,动态组成机器人行为；基于演化计算的足球机器人能更好地完成跑位、截球、带球、传球等任务,具有更强的适应性。

Research on RoboCup2D Robot Planning Based on PSO

In order to build intelligent robots to accomplish soccer game tasks, this paper introduces evolutionary computing in agent architecture for perception, planning, and action: (1) an architecture based on PSO is proposed, which made up of 4 levels: atomic action, combo action, behavior and policy. (2) by offline training, agents format perception rules and relevant parameters, to optimize perception method for the position, orientation and other information; (3) according to the granularity, functions, and parameters manually specified, PSO builds a set of combo actions, which described by atomic actions, parameters and execution results; (4) according to game environment and a few task rules, PSO searches for task, behavior, and combo actions, as a whole, to accomplish the game tasks. The simulation experiments on RoboCup2D platform show that, agent based on PSO is a robust and flexible robot control method: given evaluation methods and implementation frames, it is able to learn rapidly in real environment, and displays planning behavior without the use of classical planning techniques.