Vision-Based Autonomous Drone Control using Supervised Learning in Simulation

• URL: http://arxiv.org/abs/2009.04298v1
• Date: Wed, 9 Sep 2020 13:45:41 GMT
• Title: Vision-Based Autonomous Drone Control using Supervised Learning in Simulation
• Authors: Max Christl
• Abstract summary: We propose a vision-based control approach using Supervised Learning for autonomous navigation and landing of MAVs in indoor environments. We trained a Convolutional Neural Network (CNN) that maps low resolution image and sensor input to high-level control commands. Our approach requires shorter training times than similar Reinforcement Learning approaches and can potentially overcome the limitations of manual data collection faced by comparable Supervised Learning approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Limited power and computational resources, absence of high-end sensor equipment and GPS-denied environments are challenges faced by autonomous micro areal vehicles (MAVs). We address these challenges in the context of autonomous navigation and landing of MAVs in indoor environments and propose a vision-based control approach using Supervised Learning. To achieve this, we collected data samples in a simulation environment which were labelled according to the optimal control command determined by a path planning algorithm. Based on these data samples, we trained a Convolutional Neural Network (CNN) that maps low resolution image and sensor input to high-level control commands. We have observed promising results in both obstructed and non-obstructed simulation environments, showing that our model is capable of successfully navigating a MAV towards a landing platform. Our approach requires shorter training times than similar Reinforcement Learning approaches and can potentially overcome the limitations of manual data collection faced by comparable Supervised Learning approaches.

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