基于边缘计算的智能混合机器人框架及路径规划设计

Intelligent hybrid robot framework and path planning based on edge computing

在智能混合机器人控制系统中,路径规划算法复杂度高、对环境变化适应性弱、控制功能单一,导致在实际应用中延时大、控制难度高,无法适应复杂环境。针对以上问题,提出智能混合机器人边缘计算的方法,在靠近用户或数据源的位置提供网络、计算、存储服务,对当前环境进行精准定位。通过对蚁群算法(Ant Colony Optimization,ACO)、烟花算法(Fireworks Algorithm,FWA)和A*算法进行比较,确定A*算法进行路径规划能够更快迭代出最短路径轨迹。同时,采用三次多项插值原理对机器人手臂运动轨迹进行优化,使智能混合机器人能够更加快速地定位到物体,准确进行抓取,从而能适用于更多的场景需求。

In the intelligent hybrid robot control system,the path planning algorithm has high complexity,weak adaptability to environmental changes,and single control function,which leads to large delay and high control difficulty in practical use,and cannot adapt to complex environment.To solve the above problems,an intelligent hybrid robot edge computing method is proposed,which provides network,computing and storage services near users or data sources,and accurately locates the current environment.By comparing ant colony optimization(ACO),fireworks algorithm(FWA)and A*algorithm,it is determined that the path planning of A*algorithm can iterate the shortest path trajectory faster.Meanwhile,cubic multinomial interpolation is used to optimize the motion trajectory of the robot arm,so that the intelligent hybrid robot can locate the object more quickly and grasp it accurately,so that it can be applied to more scene requirements.