李兆博, 孙双蕾. 基于鸽群优化改进动态窗的多无人车协同编队避障控制[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.10.11.003
引用本文: 李兆博, 孙双蕾. 基于鸽群优化改进动态窗的多无人车协同编队避障控制[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.10.11.003
Multi-vehicle formation and obstacle avoidance control based on pigeon-inspired optimization and dynamic window approach[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.10.11.003
Citation: Multi-vehicle formation and obstacle avoidance control based on pigeon-inspired optimization and dynamic window approach[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.10.11.003

基于鸽群优化改进动态窗的多无人车协同编队避障控制

Multi-vehicle formation and obstacle avoidance control based on pigeon-inspired optimization and dynamic window approach

  • 摘要: 在复杂未知的战场环境中,无人车集群较单台无人车可承担更为复杂的任务,无人车协同编队避障行驶是群体智能领域研究热点之一。针对未知环境下无人车协同编队和避障控制问题,本文提出了基于改进鸽群优化和动态窗的无人车协同编队和避障控制方法。基本动态窗口法的路径评价函数包括方位角评价因子、障碍物评价因子、速度评价因子,在此基础增加了多无人车的方向协同因子和队形保持因子。当无人车协同感知到未知障碍物时,通过改进动态窗算法进行相对位置及速度的自适应协同调整,在实现躲避未知障碍物的同时保持队形相对稳定。同时,基于变权重鸽群优化算法对改进动态窗的路径评价函数各系数进行优化。最后,以三架无人车构成三角形编队避障为例,对变权重鸽群优化和改进动态窗算法进行仿真验证。结果表明,该方法能够有效避实现无人车之间以及无人车与未知障碍物之间的碰撞,且队形保持相对稳定。仿真结果验证了变权重鸽群优化和改进动态窗算法算法的可行性。

     

    Abstract: In complex and unknown battlefield environments, unmanned vehicle clusters can undertake more complex tasks than single unmanned vehicles. The collaborative formation and obstacle avoidance of unmanned vehicles is one of the research hotspots in the field of swarm intelligence. Aiming at the problem of multiple unmanned ground vehicle (UGV) formation and obstacle avoidance in an unknown environment, this paper proposes a UGV cooperative formation and obstacle avoidance control method based on improved dynamic window approach (DWA). Besides the azimuth evaluation factor, obstacle evaluation factor, and speed evaluation factor of the dynamic window method path evaluation function, has the direction coordination factor and formation maintenance factor are added, which realizes the collaborative adaptive adjustment by improving the dynamic window approach (DWA) algorithm. When multiple unmanned vehicles approach to unknown obstacles, the direction synergy factor in the path evaluation function is used to control the consistency of speed direction during the collaborative driving process of multiple unmanned vehicles. The formation preservation factor can automatically adjust the relative position and distance between multiple vehicles. The adaptive collaborative adjustment of the relative position and speed of each vehicle is carried out through the improved dynamic window algorithm. This method can keep the formation relatively stable while avoiding obstacles. The coefficients of each factor of the path evaluation function are optimized based on the improved Pigeon-inspired Optimization (PIO). Finally, the variable weight pigeon-inspired Optimization (PIO) and improved dynamic window approach (DWA) algorithm is simulated and verified by forming a triangular formation with three UAVs. The results show that the method can effectively avoid collisions between UAVs and obstacles and keep the formation stable, which has verified the feasibility of the improved dynamic window approach (DWA) algorithm.

     

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