Robust, Perception Based Control with Quadrotors

TL;DR

This paper presents a novel robust perception-based control method for quadrotors that explicitly accounts for perception errors, demonstrated through real hardware implementation and a new cost function for easier controller tuning.

Contribution

It introduces the first real hardware implementation of a robust perception-based controller using a data-driven perception model and a new cost function for controller robustness.

Findings

Demonstrated benefits of the approach in hardware and simulation.

Identified limitations and trade-offs of perception-based robustness.

Provided a practical method for tuning robust controllers.

Abstract

Traditionally, controllers and state estimators in robotic systems are designed independently. Controllers are often designed assuming perfect state estimation. However, state estimation methods such as Visual Inertial Odometry (VIO) drift over time and can cause the system to misbehave. While state estimation error can be corrected with the aid of GPS or motion capture, these complementary sensors are not always available or reliable. Recent work has shown that this issue can be dealt with by synthesizing robust controllers using a data-driven characterization of the perception error, and can bound the system's response to state estimation error using a robustness constraint. We investigate the application of this robust perception-based approach to a quadrotor model using VIO for state estimation and demonstrate the benefits and drawbacks of using this technique in simulation and hardware. Additionally, to make tuning easier, we introduce a new cost function to use in the control synthesis which allows one to take an existing controller and "robustify" it. To the best of our knowledge, this is the first robust perception-based controller implemented in real hardware, as well as one utilizing a data-driven perception model. We believe this as an important step towards safe, robust robots that explicitly account for the inherent dependence between perception and control.