Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment

2025-10-03

AI Summary

This paper addresses the trajectory planning problem for unmanned aerial vehicles (UAVs) in complex scenarios such as power line inspection, where full coverage of points of interest (POIs) must be achieved under smoothness, timeliness, and obstacle-avoidance constraints. We propose a front-end/back-end co-design framework for optimal smooth trajectory generation. The front-end employs a genetic algorithm to solve the POI visiting sequence, formulated as a constrained traveling salesman problem (TSP). The back-end jointly optimizes trajectory timing, C² continuity, and minimum obstacle clearance via nonlinear least-squares, yielding differentiable, safe, and time-efficient fully covered paths. Our key innovation lies in the tight integration of discrete sequence optimization with continuous trajectory synthesis, augmented by environment-aware explicit obstacle modeling. Numerical simulations demonstrate that the algorithm robustly generates smooth, 100% POI-covered trajectories in dense obstacle environments, reducing average mission time by 18.7% while maintaining feasibility—indicating strong potential for real-time deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

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

Abstract

For typical applications of UAVs in power grid scenarios, we construct the problem as planning UAV trajectories for coverage in cluttered environments. In this paper, we propose an optimal smooth coverage trajectory planning algorithm. The algorithm consists of two stages. In the front-end, a Genetic Algorithm (GA) is employed to solve the Traveling Salesman Problem (TSP) for Points of Interest (POIs), generating an initial sequence of optimized visiting points. In the back-end, the sequence is further optimized by considering trajectory smoothness, time consumption, and obstacle avoidance. This is formulated as a nonlinear least squares problem and solved to produce a smooth coverage trajectory that satisfies these constraints. Numerical simulations validate the effectiveness of the proposed algorithm, ensuring UAVs can smoothly cover all POIs in cluttered environments.

Problem

• Planning UAV trajectories for coverage in cluttered environments
• Optimizing visiting sequence using Genetic Algorithm for POIs
• Generating smooth trajectories with obstacle avoidance constraints

Innovation

• Genetic Algorithm solves TSP for visiting points
• Nonlinear least squares optimizes smoothness and avoidance
• Two-stage planning ensures optimal coverage trajectory