A Trajectory Planning Scheme for Collaborative Aerial Transportation Systems by Graph-Based Searching and Cable Tension Optimization

Leveraging the combined capabilities of multiple unmanned aerial vehicles (UAVs), the collaborative aerial transportation system efficiently manages heavy payload transport tasks. However, in obstacle-rich environments, the system faces complex collision risks at multiple levels: internal coupling collisions, where UAVs may decrease their separation distance during obstacle avoidance, and interference between the UAV, cable, and payload; as well as external collisions, where environmental obstacles impose strict geometric constraints on the system’s movement in narrow spaces. The system must also ensure that both dynamic feasibility and cable tension constraints (to avoid slack or breakage), resulting in a highly nonlinear and nonconvex optimization problem. Furthermore, rapid acceleration or sharp turns during obstacle avoidance can increase the risk of secondary collisions. To address these challenges, this article presents a novel hierarchical collision avoidance framework that integrates obstacle-aware graph searching with constrained cable tension optimization. In the first stage, a 3-D motion-primitive graph search algorithm is proposed to generate smooth, collision-free trajectories, where the search is guided by a heuristic function that jointly optimizes trajectory smoothness and transportation time. In the second stage, cable tension optimization is formulated as a nonlinear problem, ensuring tension feasibility and preventing inter-UAV collisions. Extensive experiments demonstrate that the proposed method improves trajectory smoothness by at least 26.8% and reduces trajectory length by at least 2.4%, compared to baseline methods.