Multi-UAV Visual Coverage of Partially Known 3D Surfaces: Voronoi-based Initialization to Improve Local Optimizers

TL;DR

This paper presents a Voronoi-based initialization method for multi-UAV visual coverage of 3D surfaces, improving local optimization outcomes by leveraging partial environment knowledge.

Contribution

It introduces a Voronoi-based initialization strategy to enhance local optimization for UAV coverage tasks with sparse environment data.

Findings

Voronoi initialization improves coverage optimization results.

Simulation shows better performance with Voronoi-based starting points.

Method effectively utilizes partial environment knowledge for UAV deployment.

Abstract

In this paper we study the problem of steering a team of Unmanned Aerial Vehicles (UAVs) toward a static configuration which maximizes the visibility of a 3D environment. The UAVs are assumed to be equipped with visual sensors constrained by a maximum sensing range and the prior knowledge on the environment is considered to be very sparse. To solve this problem on-line, derivative-free measurement-based optimization algorithms can be adopted, even though they are strongly limited by local optimality. To overcome this limitation, we propose to exploit the partial initial knowledge on the environment to find suitable initial configurations from which the agents start the local optimization. In particular, a constrained centroidal Voronoi tessellation on a coarse approximation of the surface to cover is proposed. The behavior of the agent is so based on a two-step optimization approach, where a stochastic optimization algorithm based on the on-line acquired information follows the geometrical-based initialization. The algorithm performance is evaluated in simulation and in particular the improvement on the solution brought by the Voronoi tessellation with respect to different initializations is analyzed.