Micro Air Vehicles (MAVs) will unlock their true potential once they can operate in groups. To this end, it is essential for them to estimate on-board the relative location of their neighbors. The challenge lies in limiting the mass and processing burden needed to enable this. We developed a relative localization method that only requires the MAVs to communicate via their wireless transceiver. Communication allows the exchange of on-board states (velocity, height, and orientation), while the signal-strength provides range data. These quantities are fused to provide a full relative location estimate. We used our method to tackle the problem of collision avoidance in tight areas. The system was tested with a team of AR.Drones flying in a 4mx4m area and with miniature drones of 50g in a 2mx2m area. The MAVs were able to track their relative positions and fly several minutes without collisions. Our implementation used Bluetooth to communicate between the drones. This featured significant noise and disturbances in signal-strength, which worsened as more drones were added. Simulation analysis suggests that results can improve with a more suitable transceiver module.
MAVs should be designed to be as efficient as possible to decrease mass and maximize flight-time. This means that they are often limited in sensing, computational power, and payload capabilities . Collision avoidance is important for mission success but it must not exhaust the already limited resources, which should remain free to pursue the real mission. Arguably, the simplest method to avoid collisions is to have the MAVs fly at different heights. However, experiments by @cite_8 have shown that MAV multi-rotors flying on top of each-other are subject to considerable aerodynamic disturbances. Furthermore, height sensor (e.g. sonar) readings could be disturbed. Based on this limitation, we conclude that lateral evasive maneuvers are needed, and these require relative location estimates between MAVs.
@cite_8
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The dynamic response and performance of a micro UAV is greatly influenced by its aerodynamics which in turn is affected by the interactions with features in the environment in close proximity. In the paper we address the modeling of quadrotor robots in different flight conditions that include relative wind velocity and proximity to the ground, the ceiling and other robots. We discuss the incorporation of these models into controllers and the use of a swarm of robots to map features in the environment from variations in the aerodynamics.