Star-Convex Constrained Optimization for Visibility Planning with Application to Aerial Inspection

TL;DR

This paper introduces a star-convex constrained optimization approach for visibility planning in robotic inspection tasks, ensuring visibility guarantees through modeling the visible space as star convex polytopes and transforming the problem for efficient solution.

Contribution

The paper presents a novel visibility guaranteed planner using star-convex constrained optimization, directly modeling visible space and transforming the problem for efficient trajectory planning.

Findings

The method guarantees visibility in inspection tasks.

The approach is efficient and scalable.

Experimental results validate the visibility guarantees.

Abstract

The visible capability is critical in many robot applications, such as inspection and surveillance, etc. Without the assurance of the visibility to targets, some tasks end up not being complete or even failing. In this paper, we propose a visibility guaranteed planner by star-convex constrained optimization. The visible space is modeled as star convex polytope (SCP) by nature and is generated by finding the visible points directly on point cloud. By exploiting the properties of the SCP, the visibility constraint is formulated for trajectory optimization. The trajectory is confined in the safe and visible flight corridor which consists of convex polytopes and SCPs. We further make a relaxation to the visibility constraints and transform the constrained trajectory optimization problem into an unconstrained one that can be reliably and efficiently solved. To validate the capability of the proposed planner, we present the practical application in site inspection. The experimental results show that the method is efficient, scalable, and visibility guaranteed, presenting the prospect of application to various other applications in the future.