Autonomous Navigation of Drones Using Reinforcement Learning

Jacob, Billy; Kaushik, Abhishek; Velavan, Pankaj

doi:10.1007/978-981-16-7220-0_10

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…It provides an overview of the current state of the art, challenges, and trends in using reinforcement learning for drone navigation. The paper [36] explores the autonomous navigation of drones using reinforcement learning. It likely provides insights into how reinforcement learning methods are applied to enable drones to navigate autonomously.…”

Section: Existing Workmentioning

confidence: 99%

A proximal policy optimization with curiosity algorithm for virtual drone navigation

Das,

Khan,

Paul

2024

Eng. Res. Express

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The drone sector is witnessing a surge in demand for advanced models tailored to address critical applications such as disaster management and intelligent warehouse deliveries. Employing simulation-based experiments with virtual drone navigation is considered a best practice before deploying physical models. Nonetheless, the current state-of-the-art virtual drone navigation system lacks accuracy and introduces notable increments in simulation time. In order to mitigate these issues, this paper introduces a deep reinforcement learning-based drone agent, designed to autonomously navigate within a constrained virtual environment. The proposed drone agent utilizes realistic drone physics in order to ensure flight within the virtual environment. The work uniquely combines & optimizes both control algorithms and physical dynamics, making the model more robust and versatile than others. The integration of curiosity-driven learning with physics-based modeling potentially increases the model’s readiness for real-world application, compared to theoretical approaches. The extensive simulation results validate the remarkable speed and accuracy of the proposed scheme compared to baseline works. The trained agent exhibits strength and versatility, enabling it to deal with the numerous targets and obstacles encountered in human environments.

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Section: Existing Workmentioning

confidence: 99%

A proximal policy optimization with curiosity algorithm for virtual drone navigation

Das,

Khan,

Paul

2024

Eng. Res. Express

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“…Consequently, artificial intelligence techniques have been utilized in the provisioning of civilian and military services [2]. In this context, drones have been adopted for express shipping and delivery [3][4][5], natural disaster prevention [6,7], geographical mapping [8], search and rescue operations [9], aerial photography for journalism and film making [10], providing essential materials [11], border control surveillance [12], and building safety inspection [13]. Even though drone technology offers a multitude of benefits, it raises mixed concerns when it comes to how it will be used in the future.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network

Solaiman,

Alsuwat,

Alharthi

2023

ASI

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In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively.

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Technological Innovations in Interactive Marketing: Enhancing Customer Experience at the New Retail Age

Jasrotia

2023

The Palgrave Handbook of Interactive Marketing

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Autonomous Navigation of Drones Using Reinforcement Learning

Cited by 4 publications

References 20 publications

A proximal policy optimization with curiosity algorithm for virtual drone navigation

A proximal policy optimization with curiosity algorithm for virtual drone navigation

Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network

Technological Innovations in Interactive Marketing: Enhancing Customer Experience at the New Retail Age

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