The rapid acquisition of spatial information over affected areas is critical for emergency decision-making and disaster assessment in complex environments. Unmanned aerial vehicles (UAVs) have become an important tool for disaster information acquisition due to their rapid deployment and flexible observation capabilities. However, multi-UAV remote sensing information acquisition in complex obstacle-laden environments still faces challenges such as insufficient coverage efficiency, limited obstacle avoidance capability, and weak cooperation. This paper proposes a multi-UAV cooperative remote sensing coverage method based on an improved rotational potential field (IRPF). A disaster area model is first constructed. The artificial potential field is then enhanced by integrating separation forces and rotational guidance mechanisms to improve obstacle avoidance and information acquisition capability in complex environments, while coverage feedback is incorporated to enable dynamic observation region allocation. The experimental results show that the proposed method achieves a multi-run success rate of 100.0% over 20 independent experiments, with a final coverage rate of 93.3% in the representative scenario. The UAV system reaches the predefined 85% coverage threshold at step 106 and maintains zero collisions throughout the entire process. This method provides an effective approach for multi-UAV cooperative coverage planning in simulated complex environments, providing a potential approach for cooperative coverage planning in simulated disaster environments.

Metaheuristic algorithms have become effective tools for solving complex engineering optimization problems. The Parrot Optimization (PO) algorithm is a recently proposed method with promising performance; however, it still suffers from premature convergence and slow convergence when handling complex tasks. To address these issues, this paper proposes an improved variant termed the Elite Cauchy Multi-scale Multi-directional Parrot Optimization algorithm (ECMPO). The proposed ECMPO incorporates three coordinated strategies. First, an elite learning strategy is introduced before the behavioral update phase to guide the population toward promising regions and accelerate convergence. Second, after the behavioral update, individuals are adaptively processed based on their fitness: elite individuals perform a multi-scale multi-directional search to enhance local exploitation. Third, non-elite individuals undergo Cauchy mutation to increase population diversity and strengthen global exploration. These strategies enable ECMPO to achieve a better balance between exploration and exploitation. To evaluate its performance, ECMPO is examined through ablation studies on the CEC2017 benchmark functions and further compared with eleven algorithms on the CEC2020–2022 benchmark suites. Finally, it is applied to six constrained engineering design problems. The results demonstrate that ECMPO exhibits superior performance compared with competitive algorithms. ECMPO shows strong robustness and applicability in practical engineering optimization tasks.