Abstract

We present a range-based solution for indoor relative localization by micro air vehicles (MAVs), achieving sufficient accuracy for leader–follower flight. Moving forward from previous work, we removed the dependency on a common heading measurement by the MAVs, making the relative localization accuracy independent of magnetometer readings. We found that this restricts the relative maneuvers that guarantee observability, and also that higher accuracy range measurements are required to rectify the missing heading information, yet both disadvantages can be tackled. Our implementation uses ultra wideband, for both range measurements between MAVs and sharing their velocities, accelerations, yaw rates, and height with each other. We showcased our implementation on a total of three Parrot Bebop 2.0 MAVs and performed leader–follower flight in a real-world indoor environment. The follower MAVs were autonomous and used only on-board sensors to track the same trajectory as the leader. They could follow the leader MAV in close proximity for the entire durations of the flights.

Highlights

  • Swarm robotics offers to make micro air vehicle (MAV) applications more robust, flexible, and scalable (Sahin 2005; Brambilla et al 2013)

  • In order to study these intuitive conditions in further detail, we evaluated how the observability of the system is affected once the relative position p between the MAVs changes

  • The relative localization insights in this paper have been aimed at leader–follower flight, yet they extend to other applications of MAVs in the real world

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Summary

Introduction

Swarm robotics offers to make micro air vehicle (MAV) applications more robust, flexible, and scalable (Sahin 2005; Brambilla et al 2013). Despite the promising results of range-based solutions, a drawback of the solutions by Coppola et al (2018) and by Guo et al (2017) is that the MAVs need knowledge of a common frame orientation. This is established by having each MAV measure their heading with respect to North, which would be typically done with magnetometers.

Observability of the relative localization filter
Preliminaries
Reference frames
Observability analysis with a common heading reference
Observability analysis without a common heading reference
Comparison of the two systems
Verification through Simulations
Filter design
Kinematic noisy range measurements study of observable situation
Leader–follower flight considerations
Leader–follower formation control design
Experimental set-up
Leader–follower flight with one follower
Leader–follower flight with velocity and height information from a MCS
Leader–follower flight with only on-board measurements
Leader–follower flight with two followers
Comparison of flights
Discussion
Remarks on scalability
Conclusion
Findings
Future work
A Derivation of intuitive condition 3 for B

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