Route planning of unmanned aerial vehicle in post-disaster underground based on improved algorithm
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摘要: 针对传统算法应用在煤矿灾后井下环境侦测的无人机航迹规划中存在搜索点冗余、遇到突发威胁时实时性较差等问题，提出了一种逆向变权重稀疏算法。根据无人机自身性能约束及灾后井下威胁模型，从目标点到起始点进行全局静态航迹规划，避免大量无效搜索；根据无人机执行任务的需要设置不同权重系数，得到侧重航程或安全的航迹；通过引入次目标点策略，仅对被突发威胁覆盖的航迹进行修正，可在短时间内有效避开突发威胁。仿真结果表明，利用该算法进行航迹规划用时较短，无人机受到的威胁较小，可有效保障航迹规划的实时性和安全性。Abstract: In view of problems of redundant search point and poor real-time performance when encountering sudden threat existed in application of traditional algorithm in route planning of unmanned aerial vehicle in environment detection for post-disaster underground, a reverse variable weight sparse algorithm was proposed. According to performance constraint of unmanned aerial vehicle and threat model in post-disaster underground, global static route planning is carried out from target point to start point, so as to avoid a large number of invalid searches. Different weight coefficients are set according to mission requirement of unmanned aerial vehicle, so as to obtain route focuses on distance or safety. Only the path covered by sudden threat is corrected by introducing sub-target point strategy, so as to avoid sudden threat effectively in short time. The simulation results show that the algorithm used in route planning can save time of route planning and reduce threat to unmanned aerial vehicle, which can effectively guarantee real-time performance and safety of route planning.
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