Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment

TL;DR

The paper tackles efficient, collision-free coverage planning for quadrotors in cluttered environments typical of power-grid inspections. It introduces a two-stage framework: a Genetic Algorithm solves the NP-hard Traveling Salesman Problem for POIs to obtain an initial visitation order, followed by an Optimized B-Spline trajectory that enforces obstacle avoidance and dynamic smoothness via a nonlinear least-squares objective with EDT-based constraints. Key contributions include integrating TSP optimization with obstacle-aware trajectory smoothing, explicit time-scaling, and a cost composition that combines $oldsymbol{ extepsilon}_{ ext{s}}$, $oldsymbol{ extepsilon}_{ ext{e}}$, $oldsymbol{ extepsilon}_{ ext{b}}$, $oldsymbol{ extepsilon}_{ ext{t}}$, and $oldsymbol{ extepsilon}_{ ext{p}}$; 2D numerical simulations validate smooth, collision-free coverage with modest attitude changes (maximum $<20^ op$). The approach offers a scalable, practical framework for autonomous UAV inspections in industrial settings, with potential for real-time deployment in cluttered environments.

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.

Figures (7)

• Figure 1: Sketch map of unmanned system operating inspection in the power grid system.
• Figure 2: Coordinates of the quadrotors
• Figure 3: Overview of the proposed planning algorithm.
• Figure 4: Maps for operating. (a) The top view of the map for operating. (b) Cost map generated through EDT.
• Figure 5: The trajectory generated in the full cycle numerical simulation. The green line denotes the planned trajectory. The blue line denotes the estimated UAV trajectory.