Abstract
Enabling coordinated motion of multiple quadrotors is an active area of research in the field of small unmanned aerial vehicles (sUAVs). While there are many techniques found in the literature that address the problem, these studies are limited to simulation results and seldom account for wind disturbances. This paper presents the experimental validation of a decentralized planner based on multi-objective reinforcement learning (RL) that achieves waypoint-based flocking (separation, velocity alignment, and cohesion) for multiple quadrotors in the presence of wind gusts. The planner is learned using an object-focused, greatest mass, state-action-reward-state-action (OF-GM-SARSA) approach. The Dryden wind gust model is used to simulate wind gusts during hardware-in-the-loop (HWIL) tests. The hardware and software architecture developed for the multi-quadrotor flocking controller is described in detail. HWIL and outdoor flight tests results show that the trained RL planner can generalize the flocking behaviors learned in training to the real-world flight dynamics of the DJI M100 quadrotor in windy conditions.
Highlights
S MALL unmanned aerial vehicles are a growing class of vehicles that can perform complex tasks, especially in hard-to-reach areas
Most other publications provide an evaluation of their flocking approaches in the multi-sUAV simulation environments such as Ardupilot, Q-ground control, Gazebo, and ROS [34]–[36] or numerical simulation using Python and MATLAB [20], [22], [37]– [40] leaving a gap in the literature regarding the hardware/software approaches required for implementing flocking based motion planners in real-world outdoor flights
This study provides detailed discussions on the hardware/software implementation and validation of OF-GM-SARSA applied to a multi-sUAV system to learn flocking using HWIL and outdoor flight tests
Summary
S MALL unmanned aerial vehicles (sUAVs) are a growing class of vehicles that can perform complex tasks, especially in hard-to-reach areas. Most other publications provide an evaluation of their flocking approaches in the multi-sUAV simulation environments such as Ardupilot, Q-ground control, Gazebo, and ROS [34]–[36] or numerical simulation using Python and MATLAB [20], [22], [37]– [40] leaving a gap in the literature regarding the hardware/software approaches required for implementing flocking based motion planners in real-world outdoor flights. In this work, we leverage our previously developed OF-GM-SARSA-based path planner for flight testing the coordinated motion of multiple quadrotors to reach waypoints while maintaining the flocking behaviors. 2) Experimental evaluation and validation of a decentralized OF-GM-SARSA based hardware/software architecture via outdoor flight tests involving up to 4 DJI M100 quadrotors operating in the presence of natural wind gusts.
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