EXPLORING DEEP LEARNING APPROACHES FOR QUADROTOR OBSTACLE AVOIDANCE
Date
2018-05
Authors
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Publisher
University of Delaware
Abstract
This thesis seeks to address the speci c problem of quadrotor obstacle avoidance
for search and rescue applications. Unmanned Aerial Vehicles (UAV's), speci cally
quadrotors, are a robotic platform capable of high speed agile
ight while carrying sen-
sor and processing payloads. Their capabilities have potential for many applications
including inspection, photography, and search and rescue. While they are a capable
platform for many tasks, the algorithms designed for them still do not to utilize these
systems e ectively. Obstacle avoidance is an integral part of robotic navigation and a
basis for many tasks. This thesis approaches this problem from a learning perspective.
Deep learning speci cally has recently gained popularity for various robotics tasks, be-
cause it is e ective at addressing noisy data and is fast once trained. Due to quadrotors'
limited on board computing capabilities and noisy sensor input, deep neural networks
are an attractive solution to the obstacle avoidance problem. This thesis will formulate
and evaluate di erent approaches to deep learning for obstacle avoidance.
Description
Keywords
Computer Science, deep learning, quadrotor obstacle avoidance