An Algorithm for Low-Altitude Tourist Route Planning with Improved Centered Collision Optimizer

XU Hong; LI Tingting; FANG Zhixiang; FENG Mingxiang

doi:10.13203/j.whugis20260122

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An Algorithm for Low-Altitude Tourist Route Planning with Improved Centered Collision Optimizer

Cartography and GIS | 更新时间：2026-10-15

- An Algorithm for Low-Altitude Tourist Route Planning with Improved Centered Collision Optimizer
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 11, Pages: 2502-2516(2026)
- 作者机构：
  
  1.武汉科技大学城市建设学院，湖北武汉，430065
  2.武汉大学测绘遥感信息工程全国重点实验室，湖北武汉，430079
  3.武汉市规划研究院，湖北武汉，430014
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20260122
  CLC： P208
- Received：16 April 2026，
  
  Published：05 November 2026
- 稿件说明：
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徐虹,李婷婷,方志祥，等.利用改进中心碰撞优化的低空旅游航线规划算法[J].武汉大学学报（信息科学版）,2026,51(11):2502-2516.

XU Hong,LI Tingting,FANG Zhixiang,et al.An Algorithm for Low-Altitude Tourist Route Planning with Improved Centered Collision Optimizer[J].Geomatics and Information Science of Wuhan University,2026,51(11):2502-2516.
徐虹,李婷婷,方志祥，等.利用改进中心碰撞优化的低空旅游航线规划算法[J].武汉大学学报（信息科学版）,2026,51(11):2502-2516. DOI：10.13203/j.whugis20260122

XU Hong,LI Tingting,FANG Zhixiang,et al.An Algorithm for Low-Altitude Tourist Route Planning with Improved Centered Collision Optimizer[J].Geomatics and Information Science of Wuhan University,2026,51(11):2502-2516. DOI：10.13203/j.whugis20260122

摘要

随着低空空域的逐步开放和电动垂直起降飞行器技术的快速发展，低空旅游作为一种新兴的旅游方式展现出巨大潜力。城市环境下低空飞行旅游航线规划面临景观价值难以量化、建筑密集、特殊功能区禁飞或限飞导致可行域破碎等难题。为此，构建了路径规划的目标函数，提出了基于改进中心碰撞优化（improved centered collision optimizer，ICCO）的低空旅游航线规划算法。该算法采用了基于Sobol序列和反向学习的初始化策略、Levy飞行位置更新策略、边界修正机制以及差分进化等策略，能有效优化初始路径种群的分布，保证算法收敛稳定性以及增强算法的局部搜索能力。实验结果表明，ICCO生成的路径平滑且不违反约束，其求解精度、收敛速度、稳定性和约束处理能力都优于其他对比算法；所提方法能够规划出具有最小目标函数解的路径，有效协调美观、低扰、平稳、经济四大需求，为低空旅游的实践应用提供关键支撑。

Abstract

Objectives

With the gradual opening of low-altitude airspace and the rapid advancement of electric vertical takeoff and landing technologies

low-altitude tourism has developed into a promising new form of travel. However

route planning for low-altitude sightseeing in urban environments faces multiple difficulties: The difficulty in quantifying landscape appreciation values

dense building clusters

fragmented feasible flight zones caused by no-fly or restricted-flight regulations over special functional areas

and the unstable performance of traditional metaheuristic algorithms in such complex scenarios. A low-altitude tourism route planning algorithm based on an improved centered collision optimizer is proposed to obtain a multi-objective balanced optimal path in complex environments.

Methods

For low-altitude tourism scenarios

an objective function is established incorporating four key dimensions: landscape appreciation value

noise impact

flight cost

and flight smoothness. Aiming at the drawbacks of the original centered collision optimizer (CCO) (including uneven initial population distribution

crude boundary handling

and insufficient local search capability)

an improved CCO (ICCO) is proposed with four major improvement strategies: Optimizing the initial population using Sobol sequence and opposition-based learning

employing a Levy flight position update strategy to enhance global search capability

a boundary correction mechanism to improve convergence stability

and a differential evolution strategy to strengthen local fine-tuning ability. For large-scale tourist airspace

a block-wise planning followed by smooth connection strategy is adopted: The ICCO algorithm is used to compute the optimal path for each sub-region

and the segmented paths are further integrated into a complete flight route using cubic B-spline smooth connection techniques.

Results

In comparative experiments conducted across six urban sub-regions (

) wit

h varying terrain complexity and constraint density

ICCO consistently found the optimal solution fastest and outperformed genetic algorithm

particle swarm optimization

grey wolf optimizer

and the original CCO in convergence speed

solution accuracy

and stability. The standard deviation of ICCO was the smallest across all environments

and in environment

it reached zero

indicating perfect repeatability. In environment

which featured the most complex terrain and demanded the highest multi-objective balance

ICCO achieved an optimal fitness value of -1 098.20

which was 26.77% lower than genetic algorithm

8.30% lower than grey wolf optimizer

47.72% lower than particle swarm optimization

and 0.24% lower than CCO. Ablation experiments confirmed that each of the four improvement strategies contributed positively to the overall performance. A preliminary assembly of the routes generated by ICCO revealed angle violations at the connection nodes (climb angles or turn angles). After smoothing these nodes using B-spline interpolation

a complete low-altitude tourism route satisfying all angle constraints was obtained.

Conclusions

The combination of improvement strategies enhances the algorithm's performance. The proposed low-altitude tourism route planning method based on the ICCO algorithm can effectively generate smooth

economical

and visually appealing low-altitude tourism routes. It possesses high practical value and can provide reliable technical support for the practical implementation of low-altitude tourism.

关键词

Keywords

references

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