多移动机器人路径规划研究算法——基于分布式实时仿真系统

Research on Path Planning Algorithms for Multi-Mobile Robots——Based on Distributed Real-time Simulation System

采摘机器人路径规划是采摘机器人研究领域的核心内容之一,具有复杂性、约束性和非线性等特点。蚁群算法是最近发展起来的仿生优化算法,在解决许多复杂问题方面已经展现出优异的性能和巨大的发展潜力。为此,将蚁群算法引入到了多采摘机器人路径规划系统中,并利用分布式实时仿真系统对其可行性进行了仿真计算。结果表明:采用蚁群算法可以实现多采摘机器人的路径规划,且与遗传算法相比计算速度更快、精度更高,将其引入到采摘机器人的路径规划系统中,对于实现采摘机器人群体导航能力具有重要的作用。

The path planning of picking robot is one of the core contents in the research field of picking robot,which has the characteristics of complexity,constraints and non-linearity.Ant colony algorithm(ACO)is a bionic optimization algorithm developed in recent decades.It has shown its excellent performance and great development potential in solving many complex problems.It introduced ant colony algorithm into the path planning system of multi-picking robots,and its feasibility is simulated by using distributed real-time simulation system.The simulation results show that it can be realized for the path planning of multi-picking robots by using ant colony algorithm.Compared with genetic algorithm,the ant colony algorithm has faster calculation speed and higher calculation accuracy,so it is introduced into the picking machine.In the human path planning system,it plays an important role in realizing the group navigation ability of the picking robot.