Communications of the ACM

Fundamental Concepts
of Reactive Control for
Autonomous Drones

By Luca Mottola and Kamin Whitehouse

Abstract

Autonomous drones represent a new breed of mobile computing system. Compared to smartphones and connected cars that only opportunistically sense or communicate, drones allow motion control to become part of the application logic. The efficiency of their movements is largely dictated by the low-level control enabling their autonomous operation based on high-level inputs. Existing implementations of such low-level control operate in a time-triggered fashion. In contrast, we conceive a notion of reactive control that allows drones to execute the low-level control logic only upon recognizing the need to, based on the influence of the environment onto the drone operation. As a result, reactive control can dynamically adapt the control rate. This brings fundamental benefits, including more accurate motion control, extended lifetime, and better quality of service in end-user applications. Based on 260+ hours of real-world experiments using three aerial drones, three different control logic, and three hardware platforms, we demonstrate, for example, up to 41% improvements in motion accuracy and up to 22% improvements in flight time.

1. INTRODUCTION

Robot vehicle platforms, often called “drones,” offer exciting new opportunities for mobile computing. While many mobile systems, such as smartphones and connected cars, simply respond to device mobility, drones allow computer systems to actively control device location. Such a feature enables interactions with the physical world to happen in new ways and with new-found scale, efficiency, or precision. 4, 8, 18

Autopilots. Figure 1 schematically illustrates the hardware and software components in modern drone platforms. Key to their operation is the autopilot software implementing the low-level motion control. The control loop processes high-level commands coming from a Ground-Control Station (GCS) as well as various sensor inputs, such as, accelerations and Global Positioning System (GPS) coordinates, to operate actuators such as electrical motors that set the 3D orientation of the drone.

Together with the mechanical design, the autopilot soft-
ware is crucial to determine a drone’s performance along
a number of essential metrics. For example, the low-level
control directly influences the quality of the shots when
using drones for imagery applications. 17, 18 Further, it is
partly responsible for the overall energy efficiency, as a
The original version of this paper is entitled “Reactive
Control of Autonomous Drones” and was published in
Proceedings of the 14th ACM International Conference on
Mobile Systems, Applications, and Services, Singapore,
June 2016.

drone’s lifetime is often a result of how streamlined is the autopilot operation. 5, 24

Unsurprisingly, most existing autopilots employ Proportional-Integral-Derivative (PID) 2 designs. Processing is thus time-triggered: every T time units, sensors are probed, control decisions are computed, and commands are sent to the actuators. Such a deterministic operation simplifies implementations and allows designers to directly rely on a vast body of existing literature. 2

Reactive control. Based on a handful of key observations, a fundamental leap of abstraction, and an unconventional use of recent advances in programming languages, we conceive a notion of reactive control that allows autopilots to significantly improve a drone’s performance in both motion accuracy and energy consumption. Rather than periodically triggering the control logic, we only run the control logic upon recognizing the need to. Depending on the influence of the environment onto the drone operation, for example, due to wind gusts or pressure gradients, control may run more or less frequently, regardless of the the fixed rate of a corresponding time-triggered implementation. As a result, reactive control dynamically adapts the control rate.

Reactive control yields several advantages, including more timely and adaptive control decisions leading to improved motion accuracy and energy efficiency. As it

Ground-control station

Autopilot

Figure 1. Hardware and software components in modern drone platforms. Users configure high-level mission parameters at the ground-control station (GCS), whereas the autopilot software implements the low-level motion control aboard the drone.