改进人工势场算法在智能体规避辐射源问题中的应用

Application of improved artificial potential field algorithm to agent evasion of radiation source

随着无人智能设备的广泛应用,避障问题成为任务能否完成的关键。核能领域内智能设备的使用需要考虑到高能辐射对其控制电路或信号传输的安全威胁,为提高智能设备在含辐射威胁的空间内的安全性能,应将危险辐射区域作为障碍规避的对象进行考虑。为解决智能体在辐射环境中执行智能监控、巡航等需要规避危险的问题,文中运用Python编程语言将该任务区域栅格化,并提出一种改进的人工势场算法,将其运用于辐射规避问题中。在改进算法中引入辐射剂量变化梯度作为势场算法中斥力部分的来源,对该方法生成路线的风险度进行评价。在仿真模拟实验中将所提出的改进算法与A*算法进行比较,结果表明,改进算法在处理智能体规避辐射源问题时可以获得航程合理且危险度较低的避障路线。

With the increasing application of unmanned intelligent devices,the obstacle avoidance has become the key to the completion of tasks.The use of smart devices in the field of nuclear energy needs to consider the safety threat of high-energy radiation to its control circuits or signal transmission.In order to solve the problem that agents need to avoid danger when executing intelligent monitoring and cruising in the radiation environment,the Python programming language is used to rasterize the task area,and an improved artificial potential field algorithm is proposed,which is applied to the radiation avoidance.In the improved algorithm,the Radiation exposure change gradient is introduced as the source of repulsion in the potential field algorithm,and the risk degree of the route generated by this method is evaluated.The proposed improved algorithm is compared with the A*algorithm in the simulation experiments.The results show that the improved algorithm can obtain a reasonable range and low risk obstacle avoidance route when dealing with the agents avoiding radiation sources.