UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning

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

doi:10.1109/iros45743.2020.9340934

Cited by 76 publications

(53 citation statements)

References 31 publications

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“…Several studies based on RL have been made to accomplish the coverage task, i.e. avoiding collision [291], balancing the coverage ratio and energy usage [326], and is beneficial for view planning in Many CPP algorithms and methodologies have been presented in the field of robotics research. However, there revolves many constraints and technical issues awaiting to be explored and addressed.…”

Section: Discussion and Future Research Directionmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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Section: Discussion and Future Research Directionmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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“…UAVs' high mobility leverages the importance of prediction tasks, which are useful in path planning and collision avoidance, for instance, [155,156] highlighted the use of RL/DRL in collision avoidance, whereas [157,158] studied path planning. Also, path planning and collision avoidance are fundamental aspects of USV guidance and navigation systems, for instance, [159,160] used DRL techniques for path planning whereas [161,162] used DRL for USV collision avoidance.…”

Section: Integrating Unmanned Aerial and Surface Vehiclesmentioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

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“…A recent work by Theile et al [27] addressed the coverage path planning problem using a DDQN. Their network architecture interprets 3-channel map-like input through convolutional layers to generate the observation.…”

Section: B Background Of Reinforcement Learning Algorithmsmentioning

confidence: 99%

Adaptive UAV Swarm Mission Planning by Temporal Difference Learning

Gopalakrishnan

Al‐Rubaye

Tsourdos

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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The prevalence of Unmanned Aerial Vehicles in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by first incorporating a greater capacity for human-machine teaming in the design of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, to maximize the efficiency with which missions are carried out, the two problems of global mission planning: task assignment/routing and path planning, were solved together, for small problem sizes, by an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, modelfree Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.

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UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning

Cited by 76 publications

References 31 publications

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Adaptive UAV Swarm Mission Planning by Temporal Difference Learning

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