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

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.