Deep Reinforcement Learning Based Online Area Covering Autonomous Robot

Saha, Olimpiya; Ren, Guohua; Heydari, Javad; Ganapathy, V.; Shah, Mohak

doi:10.1109/icara51699.2021.9376477

“…In our work, we assume that the speed is constant and we control only the steering angle and braking. Similar to the work in [15], we rely on neural networks to model the motion patterns of active speakers.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks

Afifi¹

2022

Preprint

0

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<div>We develop a Deep Reinforcement Learning (DeepRL) based multi-agent algorithm to efficiently control</div><div>autonomous vehicles in the context of Wireless Sensor Networks (WSNs). In contrast to other applications, WSNs</div><div>have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the</div><div>quality of sensed data. Second, quality of service (QoS) which is used to measure the network’s performance. As</div><div>a use case, we consider wireless acoustic sensor networks; a group of speakers move inside a room and there</div><div>are microphones installed on vehicles for streaming the audio data. We formulate an appropriate Markov Decision</div><div>Process (MDP) and present, besides a centralized solution, a multi-agent Deep Q-learning solution to control the vehicles. We compare the proposed solutions to a naive heuristic and two different real-world implementations: microphones being hold or preinstalled. We show using simulations that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation and the proposed heuristic. Additionally, we provide theoretical analysis of the performance with respect to WSNs dynamics, such as speed, rooms dimensions and speaker’s talking time.</div>

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“…In our work, we assume that the speed is constant and we control only the steering angle and braking. Similar to the work in [15], we rely on neural networks to model the motion patterns of active speakers.…”

Section: Related Workmentioning

confidence: 99%

Multi-agent Reinforcement Learning for Autonomous Vehicles in Wireless Sensor Networks

Afifi¹

2021

Preprint

0

View full text Add to dashboard Cite

<div>We develop a Deep Reinforcement Learning (DeepRL) based multi-agent algorithm to efficiently control</div><div>autonomous vehicles in the context of Wireless Sensor Networks (WSNs). In contrast to other applications, WSNs</div><div>have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the</div><div>quality of sensed data. Second, quality of service (QoS) which is used to measure the network’s performance. As</div><div>a use case, we consider wireless acoustic sensor networks; a group of speakers move inside a room and there</div><div>are microphones installed on vehicles for streaming the audio data. We formulate an appropriate Markov Decision</div><div>Process (MDP) and present, besides a centralized solution, a multi-agent Deep Q-learning solution to control the vehicles. We compare the proposed solutions to a naive heuristic and two different real-world implementations: microphones being hold or preinstalled. We show using simulations that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation and the proposed heuristic. Additionally, we provide theoretical analysis of the performance with respect to WSNs dynamics, such as speed, rooms dimensions and speaker’s talking time.</div>

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A Novel MPC Formulation for Dynamic Target Tracking with Increased Area Coverage for Search-and-Rescue Robots

Baglioni,

Jamshidnejad

2024

J Intell Robot Syst

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Deep Reinforcement Learning Based Online Area Covering Autonomous Robot

Cited by 5 publications

References 21 publications

Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks

Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks

Multi-agent Reinforcement Learning for Autonomous Vehicles in Wireless Sensor Networks

A Novel MPC Formulation for Dynamic Target Tracking with Increased Area Coverage for Search-and-Rescue Robots

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