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Conference Papers Year : 2021

I2SL: Learn How to Swarm Autonomous Quadrotors Using Iterative Imitation Supervised Learning

Abstract

In this paper, a decentralized controller for a quadrotor swarm is presented following the leader-follower principle. The quadrotors embedding the decentralized controller follow a remotely controlled leader. The controller, governing the behavior of a set of followers is learned using an Iterative Imitation supervised learning approach. The novelty of this approach is to build complex policies supporting the flocking behavior for a set of quadrotors while requiring only COTS (Commercial Off The Shelf) wireless sensors. In the first iteration, a set of trajectories is generated using the well-known Reynolds flocking model (adapted by Schilling et al, 2018, to add a migration term); the logs are exploited to enable the follower quadrotor controller to achieve the migration function. In the further iterations, the learned controller is exploited in combination with the Reynolds model; the logs generated are then exploited to learn a follower quadrotor controller achieving both the migration and the flocking functions, as robust as the Reynolds model. The validation of the approach using a Software In The Loop (SITL) environment relying on the Gazebo simulator, confirms that the learned controller enables the followers to accurately follow the leader while collectively satisfying the swarm properties.
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Dates and versions

hal-03399149 , version 1 (23-10-2021)

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Omar Shrit, Michèle Sebag. I2SL: Learn How to Swarm Autonomous Quadrotors Using Iterative Imitation Supervised Learning. EPIA 2021 - 20th EPIA Conference on Artificial Intelligence, Sep 2021, Virtual, Portugal. pp.418-432, ⟨10.1007/978-3-030-86230-5_33⟩. ⟨hal-03399149⟩
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