Research on UAV Applications in Public Administration: Based on an Improved RRT Algorithm

2025-08-15

AI Summary

Unmanned aerial vehicles (UAVs) face critical challenges in public management applications—such as emergency response and traffic monitoring—including high energy consumption, difficulty in dynamic obstacle avoidance, and stringent airspace constraints. Method: This paper proposes an enhanced dynamic Rapidly-exploring Random Tree (dRRT) algorithm integrating goal-biased sampling, adaptive step-size adjustment, obstacle circumvention prioritization, and B-spline-based trajectory smoothing. Contribution/Results: Evaluated in a 500 m³ three-dimensional urban simulation environment, the algorithm achieves a 100% path planning success rate, with an average computation time of only 0.01468 seconds—outperforming conventional RRT, A*, and ant colony optimization. The generated trajectories are shorter, require fewer waypoints, and exhibit a maximum yaw angle under 45°, significantly improving planning efficiency, flight safety, and trajectory smoothness. This work advances UAVs from isolated technical tools toward reliable, infrastructure-grade components for intelligent urban governance.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This study investigates the application of unmanned aerial vehicles (UAVs) in public management, focusing on optimizing path planning to address challenges such as energy consumption, obstacle avoidance, and airspace constraints. As UAVs transition from 'technical tools' to 'governance infrastructure', driven by advancements in low-altitude economy policies and smart city demands, efficient path planning becomes critical. The research proposes an enhanced Rapidly-exploring Random Tree algorithm (dRRT), incorporating four strategies: Target Bias (to accelerate convergence), Dynamic Step Size (to balance exploration and obstacle navigation), Detour Priority (to prioritize horizontal detours over vertical ascents), and B-spline smoothing (to enhance path smoothness). Simulations in a 500 m3 urban environment with randomized buildings demonstrate dRRT's superiority over traditional RRT, A*, and Ant Colony Optimization (ACO). Results show dRRT achieves a 100% success rate with an average runtime of 0.01468s, shorter path lengths, fewer waypoints, and smoother trajectories (maximum yaw angles <45°). Despite improvements, limitations include increased computational overhead from added mechanisms and potential local optima due to goal biasing. The study highlights dRRT's potential for efficient UAV deployment in public management scenarios like emergency response and traffic monitoring, while underscoring the need for integration with real-time obstacle avoidance frameworks. This work contributes to interdisciplinary advancements in urban governance, robotics, and computational optimization.

Problem

• Optimizing UAV path planning for energy efficiency and obstacle avoidance
• Addressing airspace constraints in urban public management applications
• Improving Rapidly-exploring Random Tree algorithm for smoother trajectories

Innovation

• Enhanced RRT algorithm with four strategies
• Dynamic step size balances exploration and navigation
• B-spline smoothing for improved path smoothness