Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning

Bayerlein, Harald; Theile, Mirco; Caccamo, Marco; Gesbert, David

doi:10.1109/ojcoms.2021.3081996

Cited by 109 publications

(52 citation statements)

References 27 publications

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“…Combining two optimization methods also has been studied [39]. Recently, with the development of deep learning, studies on the path planning using the RL have mainly been proposed [3], [6], [7], [9], [10], [11], [14], [15], [16], [17], [40], [41], [42]. They have supposed the specific scenario and set an environment to apply the agent in the path planning.…”

Section: Path Planningmentioning

confidence: 99%

“…They have supposed the specific scenario and set an environment to apply the agent in the path planning. Especially, they have applied their study to robotics [3], [7], [16], drone [4], [5], [6], [8], [9], [42], and ship [43], [44]. Also, they have focused on the one single goal of the agent to reach the target point, avoiding obstacles.…”

Section: Path Planningmentioning

confidence: 99%

“…P ATH planning is a method to find an optimal route from the starting point to the target point. It has been widely used in various fields such as robotics [1], [2], [3], drone [4], [5], [6], [7], [8], [9], military service [10], [11], and self-driving car [12], [13]. Recently, reinforcement learning (RL) has been mainly studied for the path planning [3], [7], [9], [10], [11], [14], [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

“…It has been widely used in various fields such as robotics [1], [2], [3], drone [4], [5], [6], [7], [8], [9], military service [10], [11], and self-driving car [12], [13]. Recently, reinforcement learning (RL) has been mainly studied for the path planning [3], [7], [9], [10], [11], [14], [15], [16], [17]. To get an optimal solution, it is essential to give enough reward for an agent to reach the goal and to set up a specific environment.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning

Lee¹

2022

Preprint

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The aim of path planning is to reach the goal from starting point by searching for an agent's route. In the path planning, the routes may vary depending on the number of variables such that it is important for the agent to reach various goals. Numerous studies, however, have dealt with a single goal that is predefined by the user. In the present study, I propose a novel reinforcement learning framework for a fully controllable agent in the path planning. To do this, I propose a bi-directional memory editing to obtain various bi-directional trajectories of the agent, in which the agent's behavior and sub-goals are trained on the goal-conditioned RL. As for the agent's to move in various directions, I utilize the sub-goals dedicated network, separated from a policy network. Lastly, I present the reward shaping to shorten the number of steps for the agent to reach the goal. In the experimental result, the agent was able to reach the various goals that have never been visited by the agent in the training. We confirmed that the agent could perform difficult missions such as a round trip and the agent used the shorter route with the reward shaping.

show abstract

Section: Path Planningmentioning

confidence: 99%

Section: Path Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning

Lee¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Regarding the research on multi-UAV co-location [24][25][26][27][28][29], Victor [30] proposed a UAV co-location algorithm in a global navigation satellite system (GNSS) environment, which uses a platform where the satellite signals are interfered, except for on the ground level, for navigation. However, the limitations of the ground control station technology posed a problem.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Factor Graph Co-location Algorithm

Zhang

Jia

2022

Preprint

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To tackle the difficulty and high cost of UAV positioning in a disaster environment, this study proposes a multi-UAV coordinated positioning algorithm based on a factor graph (MUAV-FGC). A factor graph model is designed for coordinated positioning of the master-slave UAVs. In the positioning process, the influence of the speed and heading errors of the UAV on the positioning accuracy was analyzed, and the expected and variance values of the variables were used as the information transmitted between the factors. The error of the cooperative positioning algorithm was derived based on the factor graph. The coordinated positioning of multiple drones and the positioning accuracy improved. The simulation results show that, compared with the traditional extended Kalman filter (EKF) algorithm, the proposed MUAV-FGC positioning algorithm reduces the mean positioning and root mean square error by approximately 18.86% and 17.54%, respectively, thus proving its effectiveness.

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A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Pasandideh

Costa

Kunst

et al. 2023

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs), also known as drones, communicate, collaborate, and form flying ad hoc networks (FANETs) to perform many different missions, ranging from delivery tasks to agriculture applications. Recently, FANETs have been integrated with different technologies, such as artificial intelligence (AI), virtual reality, and Internet of Things. Such new avenues for the use of UAVs directly impact the research on FANETs and cause some major challenges, such as security and physical layer issues, resource management, and UAV positioning issues that need to be addressed. Several researchers have been working for the last few years to propose AI and machine learning (ML)‐based solutions for different use cases in UAV‐based networks. They present the limitations of the existing research work and highlight some possible future works on FANETs. However, exhibiting the trends in the UAV research papers in a quantitative manner is still required to motivate researchers to rethink the research on FANETs. Therefore, this study covers more than 170 scientific publications extracted from five trusted academic databases published from 2013 to 2021 to provide a thorough overview of the main research and development statistics in the area of FANETs, the open challenges existing in this area and the ML‐based solutions to solve these challenges. In addition, the investigation of emerging technologies integrated with FANETs, as well as the simulation tools employed for evaluating FANETs' performance are discussed. Moreover, the future research directions in the area of FANETs are considered within a prospective vision discussion.

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Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning

Cited by 109 publications

References 27 publications

A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning

A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning

Multi-UAV Factor Graph Co-location Algorithm

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

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