Obstacle Avoidance Drone by Deep Reinforcement Learning and Its Racing with Human Pilot

Shin, Sang-Yun; Kang, Yong-Won; Kim, Yong-Guk

doi:10.3390/app9245571

Cited by 38 publications

(18 citation statements)

References 30 publications

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“…Several other works existing in the literature are quite interesting. For instance, environment exploration and obstacle avoidance problems for UAVs are solved via different RL methods with both continuous and discrete space action in [96]- [107]. In [108] the optimal deployment of UAVs that minimizes several parameters such as transmission power, caching, and the number of UAVs, is achieved through RL.…”

Section: ) Update Rulementioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

111

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Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.

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Section: ) Update Rulementioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

111

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“…Moreover, this area of research is directly related to drone control problems, where RL has been applied to design drones with obstacle avoidance. The data obtained from the sensor module mounted on the drone are used to configure the environment and state of the RL model, and the drone is controlled by designing an algorithm to maximize the reward value obtained from operation [12,23]. RL is also used to design energy management systems to determine the balance between agents and optimal scheduling strategies.…”

Section: Learning From Demonstrationmentioning

confidence: 99%

Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network

Kim

Lee

2020

Applied Sciences

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Complex problems require considerable work, extensive computation, and the development of effective solution methods. Recently, physical hardware- and software-based technologies have been utilized to support problem solving with computers. However, problem solving often involves human expertise and guidance. In these cases, accurate human evaluations and diagnoses must be communicated to the system, which should be done using a series of real numbers. In previous studies, only binary numbers have been used for this purpose. Hence, to achieve this objective, this paper proposes a new method of learning complex network topologies that coexist and compete in the same environment and interfere with the learning objectives of the others. Considering the special problem of reinforcement learning in an environment in which multiple network topologies coexist, we propose a policy that properly computes and updates the rewards derived from quantitative human evaluation and computes together with the rewards of the system. The rewards derived from the quantitative human evaluation are designed to be updated quickly and easily in an adaptive manner. Our new framework was applied to a basketball game for validation and demonstrated greater effectiveness than the existing methods.

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“…As UAVs are becoming more important to the world, the adoption of Artificial Intelligence (AI) for these platforms has also increased with better hardware leading to improved processing speed and better-performing algorithms. The combination of these two technologies have resulted in autonomously guided UAVs, which achieve performance comparable to human operators if not better [2].…”

Section: Introductionmentioning

confidence: 99%

Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning

Thomas¹,

Aouf²,

Spencer³

2022

Preprint

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The danger of adversarial attacks to unprotected Uncrewed Aerial Vehicle (UAV) agents operating in public is growing.Adopting AI-based techniques and more specifically Deep Learning (DL) approaches to control and guide these UAVs can be beneficial in terms of performance but add more concerns regarding the safety of those techniques and their vulnerability against adversarial attacks causing the chances of collisions going up as the agent becomes confused. This paper proposes an innovative approach based on the explainability of DL methods to build an efficient detector that will protect these DL schemes and thus the UAVs adopting them from potential attacks. The agent is adopting a Deep Reinforcement Learning (DRL) scheme for guidance and planning. It is formed and trained with a Deep Deterministic Policy Gradient (DDPG) with Prioritised Experience Replay (PER) DRL scheme that utilises Artificial Potential Field (APF) to improve training times and obstacle avoidance performance. The adversarial attacks are generated by Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) algorithms and reduced obstacle course completion rates from 80% to 35%. A Realistic Synthetic environment for UAV explainable DRL based planning and guidance including obstacles and adversarial attacks is built. Two adversarial attack detectors are proposed. The first one adopts a Convolutional Neural Network (CNN) architecture and achieves an accuracy in detection of 80%. The second detector is developed based on a Long Short Term Memory (LSTM) network and achieves an accuracy of 91% with much faster computing times when compared to the CNN based detector.

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Obstacle Avoidance Drone by Deep Reinforcement Learning and Its Racing with Human Pilot

Cited by 38 publications

References 30 publications

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network

Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning

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