时空运动耦合的地空协同智能配送规划方法

Trajectory planning method of heterogeneous multi-robot cooperative intelligent transportation under spatiotemporal and motion constraints

摘要:

为提升时空运动耦合约束的物流配送效率和灵活性,通过结合无人机高机动和无人车强运载能力,构建了异构空地协同智能物资配送系统。设计了一种多层级多尺度的多机物资配送轨迹规划方法(multi-layer multi-scale multi-robot transportation trajectory planning,M³TTP),基于改进蚁群算法与概率路线图算法相结合,以优化多目标障碍约束下的旅行商问题轨迹求解结果,实现多尺度的分层任务分配决策规划。实验结果表明,考虑实际环境中空间障碍物和动态时序约束条件,通过改进的末端启发式算法构建邻接矩阵,可以实现小尺度的已知路网信息下求解最优配送路径。提出的异构协同任务作业时间同步机制,以Dubins算法将规划的轨迹均匀离散,提高了地空配送任务中多机协同效率。设计的M³TTP方法可以增强系统在时空运动耦合约束下的适应性和配送效率。

Abstract:

To enhance the efficiency and flexibility of logistics distribution under spatiotemporal motion coupling constraints, a heterogeneous aerial-ground collaborative intelligent transportation system is developed, which integrates the high mobility of unmanned aerial vehicles with the strong carrying capacity of unmanned ground vehicles. A multi-layer multi-scale multi-robot transportation trajectory planning (M³TTP) method is designed, which combines an improved ant colony optimization (ACO) algorithm with the probabilistic roadmap (PRM) algorithm. This method optimizes the trajectory solution for the traveling salesman problem (TSP) under multi-objective obstacle constraints, to achieve multi-scale hierarchical task allocation and decision planning.The simulation results demonstrate that, considering the real-world spatial obstacle and dynamic time sequence constraints, an adjacency matrix is constructed using an improved terminal heuristic algorithm. This enables the planning of an optimal transportation path based on detailed information from a small-scale and well-known road network.A time synchronization mechanism is proposed to address the challenges of heterogeneous collaborative path planning. The discrete Dubins path planning algorithm is presented to improve the efficiency of collaborative tasks. The proposed M³TTP method enhances both the adaptability and delivery efficiency of the system under spatiotemporal motion coupling constraints.