Authors:
Sana Ikli
1
and
Ilhem Quenel
2
Affiliations:
1
Hybrid Intelligence, Capgemini Engineering, 4 Avenue Didier Daurat, 31700 Blagnac, France
;
2
Applied AI program pilote, Capgemini Engineering, 4 Avenue Didier Daurat, 31700 Blagnac, France
Keyword(s):
Drone Navigation, Reinforcement Learning, Machine Learning, Drone Simulators.
Abstract:
Unmanned Aerial Vehicles, also known as drones, are deployed in various applications such as security and surveillance. They also have the key benefit of being able to operate without a human pilot, which make them suitable to access difficult areas. During autonomous flights, drones can crash or collide with an obstacle. To prevent such situation, they need an obstacle-avoidance solution. In this work, we are interested in the navigation with obstacle avoidance of a single drone. The goal is to autonomously navigate from an origin to a destination point, including takeoff, without crashing. Reinforcement learning is a valid solution to this problem. Indeed, these approaches, coupled with deep learning, are used to tackle complex problems in robotics. However, the works in the literature using reinforcement learning for drone navigation usually simplify the problem into 2-D navigation. We propose to extend these approaches to complete 3-D navigation by using a state-of-the-art algori
thm: proximal policy optimization. To create realistic drone environments, we will use a 3-D simulator called Pybullet. Results show that the drone successfully takes off and navigates to the indicated point. We provide in this paper a link to our video demonstration of the drone performing navigation tasks.
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