## 3 System Model and Problem Description
### Joint Optimization Algorithm Design
We point out that OPMEE offloads as many tasks as possible to save energy consumption in the initial stage for saving energy consumption. When the task complexity reaches a certain level, such as 6,

Figure 6: MEC UAV frequency and energy consumption. in order to balance energy consumption and optimize flight trajectories, the number of offloading tasks may be reduced. After that, a higher offloading ratio is recovered. That is to say, the algorithm will choose a balancing strategy between energy consumption and trajectory optimization. ## 6 Conclusions and Further Work

Due to the limited battery storage of a UAV, energy saving is a key issue in a UAV-assisted MEC system. However, for a low-altitude flying UAV, successful obstacle avoidance is also very necessary. This paper aims to maximize the system energy efficiency (defined as the ratio of the total amount of offloaded data to the energy consumption of the UAV) to meet the maneuverability and three-dimensional obstacle avoidance constraints of a UAV. A joint optimization strategy with maximized energy efficiency for the UAV flight trajectory and user device task offloading rate is proposed. In order to solve this problem, hybrid alternating metaheuristics for energy optimization are given. Due to the non-convexity and fractional structure of the optimization problem, it can be transformed into an equivalent parameter optimization problem using the Dinkelbach method, and then divided into two sub-optimization problems that are alternately optimized using metaheuristic algorithms. The experimental results show that the strategy proposed in this paper can enable the UAV to avoid obstacles during flight by detouring or crossing, and the trajectory does not overlap with obstacles, effectively achieving two-dimensional and three-dimensional obstacle avoidance. In addition, compared with related solving methods, the solving method in this paper is significantly higher than traditional algorithms. In comparison with related optimization strategies, the strategy proposed in this paper can effectively reduce the overall energy consumption of a UAV. Future research work can further expand to multi-UAV collaboration scenarios, exploring the task offloading relationship between multiple UAVs and user devices. Conceptualization, Y.Z. ; methodology, A.L. ; validation, Y.W. ; investigation, Y.Z. ; resources, Y.W. ; writing--original draft preparation, Y.Z. ; writing--review and editing, A.L. and G.W. ; supervision, G.W. ; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (62062007), Guangxi Natural Science Foundation (2025 GXNSFAA069236), and Guangxi Key R&D Program (2024 AB33144). Data Availability Statement: Data available on request from the authors. The authors declare no conflicts of interest. Figure 7: Task complexity and offloading ratio. ## References

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