Energy-Optimal Spatial Iterative Learning within a Virtual Tube
For UAV operators with limited onboard energy, this work offers a computationally efficient, model-free method to reduce energy consumption, though it is an incremental improvement over existing iterative learning approaches.
This paper proposes a model-free online iterative learning framework to minimize energy consumption for UAVs, achieving 50-60 times faster computation than a model-based benchmark while maintaining low computational complexity of O(n).
Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are severely constrained. Although energy-efficient trajectory planning and control have been widely studied, most existing approaches rely on accurate system models and computationally expensive optimization procedures. This paper proposes a model-free online iterative learning (IL) framework to minimize energy consumption. Without requiring explicit models of UAV dynamics or energy consumption, the proposed method improves energy efficiency while maintaining a low computational cost. The per-iteration computational complexity is O(n), where n denotes the number of path points. In the tested cases, the proposed method is approximately 50--60 times faster than the model-based IPOPT benchmark. Simulation results and real-world flight experiments across multiple UAV platforms validate the effectiveness, computational efficiency, and practical applicability of the proposed approach.