Dynamical Trajectory Planning of Disturbance Consciousness for Air-Land Bimodal Unmanned Aerial Vehicles

Air-land bimodal vehicles provide a promising solution for navigating complex environments by combining the flexibility of aerial locomotion with the energy efficiency of ground mobility. To enhance the robustness of trajectory planning under environmental disturbances, this paper presents a disturbance-aware planning framework that incorporates real-time disturbance estimation into both path searching and trajectory optimization. A key component of the framework is a disturbance-adaptive safety boundary adjustment mechanism, which dynamically modifies the vehicle's feasible dynamic boundaries based on estimated disturbances to ensure trajectory feasibility. Leveraging the dynamics model of the bimodal vehicle, the proposed approach achieves adaptive and reliable motion planning across different terrains and operating conditions. A series of real-world experiments and benchmark comparisons on a custom-built platform validate the effectiveness and robustness of the method, demonstrating improvements in tracking accuracy, task efficiency, and energy performance under both ground and aerial disturbances.