Multi-UAV cooperative path planning method integrating risk avoidance and combined strategy
Article Text (Baidu Translation)
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摘要: 针对低空经济发展过程中无人机在城市复杂环境运行时面临的安全性、经济损失、社会影响及运行效率等挑战，提出了一种融合风险规避与组合策略的多无人机协同路径规划方法。首先，基于真实城市环境构建三维栅格地图，融合多源风险信息，建立了风险评估与动态风险地图模型；其次，针对单架无人机，基于动态风险地图改进路径规划算法，引导无人机主动规避高风险区域，实现了路径总风险与长度的协同最小化；再次，针对多无人机间的冲突问题，综合考虑冲突风险、路径长度、路径风险和剩余路程比等指标，构建无人机优先级计算模型，并制定组合冲突消解策略，实现了多无人机协同路径规划。试验结果表明：所提改进算法相较于Dijkstra算法、蚁群优化算法和粒子群优化算法，路径风险分别降低了6.59%、25.94%和20.24%，路径长度分别减少了9.80%、11.94%和9.54%；在多无人机协同规划中，所设计的组合冲突消解策略针对5、10、15架无人机不同规模场景，其计算耗时较路径重规划策略分别降低了24.56%、27.42%和36.42%，任务耗时较起点等待策略分别减少了2.83%、3.29%和4.09%。该方法可高效解决无人机间的冲突，最终生成兼具安全性与经济性的协同飞行路径。Abstract: To address challenges such as safety, economic loss, social impact, and operational efficiency faced by unmanned aerial vehicles (UAVs) operating in complex urban environments during the development of the low-altitude economy, a multi-UAV cooperative path planning method integrating risk avoidance and a combined strategy was proposed. First, based on the real urban environment, a three-dimensional grid map was constructed, and multi-source risk information was integrated to establish a risk assessment and dynamic risk map model. Second, for a single UAV, the path planning algorithm was improved based on the dynamic risk map to guide the aircraft to actively avoid high-risk areas, achieving the synergistic minimization of total path risk and length. Third, to address the conflict problem among multiple UAVs, a priority calculation model was constructed by comprehensively considering indices such as conflict risk, path length, path risk, and remaining distance ratio, and a combined conflict resolution strategy was formulated to achieve multi-UAV cooperative path planning. Experimental results indicate that compared with the Dijkstra algorithm, ant colony algorithm, and particle swarm optimization algorithm, the proposed improved algorithm reduce the path risk by 6.59%, 25.94%, and 20.24%, respectively and decrease the path length by 9.80%, 11.94%, and 9.54%, respectively. In the multi-UAV cooperative planning, for scenarios with 5, 10, and 15 UAVs, the calculation times of the designed combined conflict resolution strategy is reduced by 24.56%, 27.42%, and 36.42%, respectively, compared with the path replanning strategy; the task durations is reduced by 2.83%, 3.29%, and 4.09%, respectively, compared with the starting point waiting strategy. The method can efficiently resolve conflicts between UAVs and finally generate cooperative flight paths with both safety and cost-effectiveness.
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Key words:
- low-altitude traffic /
- multi-UAV /
- conflict resolution /
- path planning /
- dynamic risk map
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图 5 在9:00的试验人口和车辆密度分布
Figure 5. Distributions of experimental population and vehicle density at 9:00
图 6 在10:00的试验环境人口和车辆密度分布
Figure 6. Distributions of experimental population and vehicle density at 10:00
表 1 算法参数及环境参数设置
Table 1. Settings of algorithm parameters and environmental parameters
参数 取值 参数 取值 无人机质量/kg 1.388 空气密度/(kg·m-3) 1.225 无人机故障率 6.04×10-5 重力加速度/(m·s-2) 9.8 50%致死率的撞击能量/MJ 1.0 阻力系数 0.3 撞击致死能量阈值/J 232 车辆限速/(km·h-1) 50 容许值[40]/(°) 30 最小直飞距离/m 10 最大飞行高度/m 120 最远飞行距离/km 15 最大航向角/(°) ±180 最大俯仰角/(°) ±45 
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表 2 单机路径规划结果对比分析
Table 2. Comparison and analysis of path planning results for single UAV
方法 路径长度/m 路径总风险 改进A*算法 9 805.62 2 424.39 Dijkstra算法 10 870.45 2 595.30 ACO算法 11 134.72 3 273.58 PSO算法 10 839.74 3 039.49
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表 3 无人机冲突检测结果及优先级排序
Table 3. Results of UAVs conflict detection and priority ranking
无人机数量/架 冲突无人机编号及优先级排序 5 (1, 4) 10 (1, 4)、(1, 5)、(5, 4)、(2, 10) 15 (3, 10)、(3, 4)、(8, 7)、(12, 11)
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表 4 不同冲突消解策略结果对比分析
Table 4. Comparison and analysis of results of different conflict resolution strategies
无人机数量/架 冲突消解策略 任务耗时/s 计算耗时/s 5 路径重规划+起点等待 622.31 8.51 路径重规划 610.12 11.28 起点等待 640.45 3.27 10 路径重规划+起点等待 674.88 14.32 路径重规划 659.34 19.73 起点等待 697.83 5.58 15 路径重规划+起点等待 676.42 16.48 路径重规划 658.73 25.92 起点等待 705.25 6.49
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