Trajectory Tracking for Aerial Robots: an Optimization-Based Planning and Control Approach
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Trajectory Tracking for Aerial Robots: an Optimization-Based Planning and Control Approach Jose Luis Sanchez-Lopez1
· Manuel Castillo-Lopez1
· Miguel A. Olivares-Mendez1
· Holger Voos1,2
Received: 30 September 2019 / Accepted: 14 April 2020 © Springer Nature B.V. 2020
Abstract In this work, we present an optimization-based trajectory tracking solution for multirotor aerial robots given a geometrically feasible path. A trajectory planner generates a minimum-time kinematically and dynamically feasible trajectory that includes not only standard restrictions such as continuity and limits on the trajectory, constraints in the waypoints, and maximum distance between the planned trajectory and the given path, but also restrictions in the actuators of the aerial robot based on its dynamic model, guaranteeing that the planned trajectory is achievable. Our novel compact multi-phase trajectory definition, as a set of two different kinds of polynomials, provides a higher semantic encoding of the trajectory, which allows calculating an optimal solution but following a predefined simple profile. A Model Predictive Controller ensures that the planned trajectory is tracked by the aerial robot with the smallest deviation. Its novel formulation takes as inputs all the magnitudes of the planned trajectory (i.e. position and heading, velocity, and acceleration) to generate the control commands, demonstrating through in-lab real flights an improvement of the tracking performance when compared with a controller that only uses the planned position and heading. To support our optimization-based solution, we discuss the most commonly used representations of orientations, as well as both the difference as well as the scalar error between two rotations, in both tridimensional and bidimensional spaces SO(3) and SO(2). We demonstrate that quaternions and error-quaternions have some advantages when compared to other formulations. Keywords Trajectory tracking · Trajectory planning · Aerial robotics · Multirotor · UAV · MAV · Remotely operated vehicles · Mobile robots · Model predictive control · Optimization
1 Introduction 1.1 Motivation Multiple studies, [57], foresee a great number of civilian applications of multirotor aerial robots (also called drones or Unmanned Aerial Vehicles, UAVs), such as their integration in smart cities [40], or aerial inspection [9], among others. Most of these applications are either under research and development as prototypes or they are still simply concepts. Jose Luis Sanchez-Lopez
[email protected] 1
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust 29, University of Luxembourg, Avenue J. F. Kennedy, L-1855, Luxembourg, Luxembourg
2
Facult´e des Sciences, de la Technologie et de la Communication, University of Luxembourg, Luxembourg, Luxembourg
Only a few of them have already become a reality that some service provider companies are commercially exploiting by use the multirotor aerial vehicles in a remotely piloted way with a low-level
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