考虑观测次数的无人机交通巡视时空网络模型

针对传统道路信息检测方式不能获得实时连续的道路信息的问题,提出使用无人机进行路网巡视的方法。通过时空路网建立多机飞行路径优化模型,解决无人机的路径优化问题。模型可分别以完成所有任务条件下最小化所有飞机总飞行时间或最小化单机的最长飞行时间为优化目标,不仅利用时空网络技术细致刻画了无人机在巡视过程中的飞行轨迹,将动态路径规划转化为静态路径规划,而且还加入了对重点路段多次巡视和多次巡视的时间间隔约束。对某一案例进行分析的结果表明,与不考虑巡视次数的路径规划相比,无人机的总飞行时间和单机飞行时间分别增加15.87%和15.15%,即可完成对2条重要路段巡视3次的任务目标。算例分析表明,优化后的巡视路径更加切合实际需要。

Unmanned Aerial Vehicle Traffic Patrol Space-Time Network Model Considering the Number of Observations

Based on the problem that the traditional road information detection method cannot obtain real-time continuous road information, a method of using the unmanned aerial vehicle (UAV) to conduct road network inspection was proposed. The optimization model for multi-machine flight path was established to solve the path optimization problem of the UAV through the space-time road network. The optimization objective for the new model was to minimize the total flight time of all aircraft or the maximum flight time of each aircraft under the condition of the completion of all mission. The new model not only transformed the dynamic path plan into a static path plan by using time-space network technology to meticulously depict the flight path of the UAV during the patrol process, but also added multiple patrols of key road sections and time interval constraints for multiple patrols. Based on the analysis of a case in the text, results show that, in comparison with the path planning without considering the number of patrols, the total and single flight time of the UAV increases by 15.87% and 15.15% respectively. The mission goal of patrolling two important road sections for three times can be completed. The analysis of the example shows that the optimized patrol path is more realistic to practical needs.