A Method For Automated Drone Viewpoints to Support Remote Robot Manipulation

TL;DR

This paper introduces a nonlinear optimization approach for automated drone viewpoints that enhance remote robot telemanipulation by selecting optimal views and compensating for drone pose uncertainties, with minimal operator input.

Contribution

The paper presents a novel optimization-based method for selecting drone viewpoints that adapt to workspace conditions and drone uncertainties, improving teleoperation support.

Findings

Effective viewpoint selection improves telemanipulation tasks.

Sparse human input enables flexible viewpoint toggling.

Validation shows improved task performance in household and industrial scenarios.

Abstract

Drones can provide a minimally-constrained adapting camera view to support robot telemanipulation. Furthermore, the drone view can be automated to reduce the burden on the operator during teleoperation. However, existing approaches do not focus on two important aspects of using a drone as an automated view provider. The first is how the drone should select from a range of quality viewpoints within the workspace (e.g., opposite sides of an object). The second is how to compensate for unavoidable drone pose uncertainty in determining the viewpoint. In this paper, we provide a nonlinear optimization method that yields effective and adaptive drone viewpoints for telemanipulation with an articulated manipulator. Our first key idea is to use sparse human-in-the-loop input to toggle between multiple automatically-generated drone viewpoints. Our second key idea is to introduce optimization objectives that maintain a view of the manipulator while considering drone uncertainty and the impact on viewpoint occlusion and environment collisions. We provide an instantiation of our drone viewpoint method within a drone-manipulator remote teleoperation system. Finally, we provide an initial validation of our method in tasks where we complete common household and industrial manipulations.