  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.
