UAV-guided multi-vehicle cooperative passage control on narrow and curved roads
Article Text (Baidu Translation)
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摘要: 针对含密集车流与狭窄车道等灾害应急运输场景中地面车辆感知能力不足、路径规划困难及协同响应不及时等问题，提出了一种基于无人机引导的地面车辆预设时间协同控制方法。以无人机广域感知与路径规划能力为依托，获取了可引导地面交通的路径信息，并将其通过无线通信传输至地面领航车辆，再由车队内部双向通信实现信息互联；设计了基于指数间距策略的扩展前瞻零初始耦合误差动态，以在消除协同算法设计限制的同时，有效避免误差累积与弯道切角行为；基于该误差动态，结合反演控制技术与预设时间引理，构造了分布式车辆控制器，其可保证预设时间单载具稳定性、队列网格稳定性与交通流稳定性存在，实现对响应效率及交通平滑等的综合提升。结果表明：提出的控制方法在狭窄道路与弯曲路径等多种复杂情况下，均可于预设时间(5 s)内实现路径的精确跟踪及协同误差的快速收敛，并通过队列网格稳定性和交通流稳定性评判指标可得其能有效抑制因信息传递造成的波动扩散与交通拥堵，显著提升交通安全与流畅性。综上，该方法具有良好的工程适用性和推广价值，可为智能交通系统中灾害应急运输等场景提供理论与技术支持。Abstract: In response to insufficient perception capability, path planning difficulty, and delayed cooperative response of ground vehicles in disaster emergency transportation scenarios involving dense traffic flow and narrow lanes, a predefined-time cooperative control method was proposed for ground vehicles guided by an unmanned aerial vehicle (UAV). The wide-area perception and path planning capability of the UAV was utilized to obtain guiding path information for ground traffic and transmit them to the leading vehicle via wireless communication. Bidirectional inter-vehicle communication was then employed to realize information sharing within the vehicle platoon. An extended look-ahead zero-initial coupled error dynamics based on an exponential spacing policy was designed to remove constraints in cooperative control design and effectively prevent error accumulation and cutting-corner behavior on curved roads. Based on the proposed error dynamics, a distributed vehicle controller was developed using backstepping control and the predefined-time stability lemma. Therefore, the predefined-time stability of individual vehicles, platoon mesh stability, and the existence of traffic flow stability was guaranteed. As a result, cooperative response efficiency and traffic smoothness were improved. Results show that the proposed method achieves accurate path tracking and fast convergence of cooperative errors within the predefined time of 5 s under various complex conditions, including narrow roads and curved paths. Evaluation results based on platoon mesh stability and traffic flow stability indicate that the proposed approach effectively suppresses disturbance propagation caused by information transmission and traffic congestion. Traffic safety and flow efficiency are thus significantly enhanced. Therefore, the proposed method demonstrates good engineering applicability and practical potential. The theoretical and technical support can be provided for disaster emergency transportation in intelligent transportation systems.
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图 8 系统在前瞻零初始耦合误差动态下的运行轨迹
Figure 8. Motion trajectories of the system under the look-ahead zero-initial coupled error dynamics
图 9 扩展前瞻零初始误差动态下的纵向误差
Figure 9. Longitudinal errors under the extended look-ahead zero-initial error dynamics
图 10 扩展前瞻零初始误差动态下的横向误差
Figure 10. Lateral errors under the extended look-ahead zero-initial error dynamics
图 22 交通流速率关于交通密度的梯度
Figure 22. Gradient of traffic flow rate with respect to traffic density
图 25 Δ=1 m时交通流速率关于交通密度的梯度
Figure 25. Gradient of traffic flow rate with respect to traffic density under Δ=1 m
图 28 新设定初始条件下的交通流速率关于交通密度的梯度
Figure 28. Gradient of traffic flow rate with respect to traffic density under newly set initial conditions
图 31 大规模载具群的交通流速率关于交通密度的梯度
Figure 31. Gradient of traffic flow rate with respect to traffic density for large-scale vehicle groups
图 35 新场景1下的交通流速率关于交通密度的梯度
Figure 35. Gradient of traffic flow rate with respect to traffic density under a new scenario 1
图 39 新场景2下的交通流速率关于交通密度的梯度
Figure 39. Gradient of traffic flow rate with respect to traffic density under a new scenario 2
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