Reinforcement Learning for Altitude Hold and Path Planning in a Quadcopter

Karthik, P B; Kumar, K. Hareesh; Fernandes, Vikrant; Arya, Kavi

doi:10.1109/iccar49639.2020.9108104

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Furthermore, Karthik et al, [11] an RL approach was proposed to overcome the stability issue of the quadrotor. The mechanism starts with the drone learning to hold the specified altitude using Q-Learning and then using Q-Learning and PID for the collision avoidance process.…”

Section: Related Workmentioning

confidence: 99%

Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

Bakar

Shamsudin

Rahim

et al. 2023

ARASET

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Nowadays, the advancement of drones is also factored in the development of a world surrounded by technologies. One of the aspects emphasized here is the difficulty of controlling the drone, and the system developed is still under full control by the users as well. Reinforcement Learning is used to enable the system to operate automatically, thus drone will learn the next movement based on the interaction between the agent and the environment. Through this study, Q-Learning and State-Action-Reward-State-Action (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. A comparison of both Q-learning and State-Action-Reward-State-Action (SARSA) based systems in autonomous drone application was performed for evaluation in this study. According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.

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Section: Related Workmentioning

confidence: 99%

Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

Bakar

Shamsudin

Rahim

et al. 2023

ARASET

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Performance of Reinforcement Learning Simulation: x86 v/s ARM

Pawanekar

Udgirkar

2021

Communications in Computer and Information Science

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Artificial Intelligence Applied to Drone Control: A State of the Art

Caballero-Martin,

Lopez-Guede,

Estevez

et al. 2024

Drones

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The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape.

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Reinforcement Learning for Altitude Hold and Path Planning in a Quadcopter

Cited by 3 publications

References 6 publications

Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

Performance of Reinforcement Learning Simulation: x86 v/s ARM

Artificial Intelligence Applied to Drone Control: A State of the Art

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