Coverage Path Planning for SAR-UAV in Search Area Coverage Tasks Based on Deep Reinforcement Learning

Lu, Haijun; Yang, Yu-Jun; Tao, Rentuo; Chen, Yawei

doi:10.1109/icus55513.2022.9987002

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…The relevance map is combined with a binary mask, which tells us which states have been visited, before being passed to a Convolutional Neural Network (CNN), which is used to extract features from images. UAVs that utilize tile-coded maps have also been useful in SAR applications Lu et al (2022) . The target area is represented as a grid of sub-areas.…”

Section: Methodologies In Active Environmental Monitoringmentioning

confidence: 99%

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A survey on autonomous environmental monitoring approaches: towards unifying active sensing and reinforcement learning

Mansfield,

Montazeri

2024

Front. Robot. AI

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The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment’s changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area.

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Section: Methodologies In Active Environmental Monitoringmentioning

confidence: 99%

“…In Lu et al (2022), they point out the limitation of the field of view for visual sensors used in SAR UAVs. In the case of a camera, at lower altitudes less of the target area will be in the frame and there is a need for an efficient coverage algorithm to make sure every section of the target area is scanned.…”

Section: Coverage and Patrollingmentioning

confidence: 99%

A survey on autonomous environmental monitoring approaches: towards unifying active sensing and reinforcement learning

Mansfield,

Montazeri

2024

Front. Robot. AI

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“…Their solution to the sparse reward problem for large environments with complex observation spaces and a simple reward function is not documented. Similarly, Haiyang et al [23] propose a CPP algorithm based on Deep RL to handle the search area coverage problem for UAVs equipped with Synthetic Aperture Radar (SAR) sensors (SAR-UAV). But, as opposed to our approach, they do not use the airborne sensor (i.e., SAR) as an integral part of the RL-based optimization process.…”

Section: Cpp Using Rlmentioning

confidence: 99%

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Alpdemir,

Sezgin

2024

Neural Comput & Applic

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Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment and a novel RL framework. Our results suggest that combination of a coarse navigation scheme and an RL-based training procedure based on GPR scan returns can lead to a more efficient target discovery procedure for host platforms.

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“…Static or dynamic deployment. As the static deployment method [18] obtains the geographical location information around the trapped target in advance, it always adopts a centralized decision when arranging to send a suitable UAV to the relay placement area for optimal networking. Conversely, the dynamic deployment method [19] always solves the problems of an unknown environment, which include the unknown obstacle, the unknown trapped target, and even an uncertain searching region.…”

mentioning

confidence: 99%

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Li,

Hao,

Wen

et al. 2024

Drones

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In the event of a sudden natural disaster, the damaged communication infrastructure cannot provide a necessary network service for vehicles. Unfortunately, this is the critical moment when the occupants of trapped vehicles need to urgently use the vehicular network’s emergency service. How to efficiently connect the trapped vehicle to the base station is the challenge facing the emergency vehicular network. To address this challenge, this study proposes a UAV-assisted multi-objective and multi-hop ad hoc network (UMMVN) that can be used as an emergency vehicular network. Firstly, it presents an integrated design of a search system to find a trapped vehicle, the communication relay, and the networking, which significantly decreases the UAV’s networking time cost. Secondly, it presents a multi-objective search for a trapped vehicle and navigates UAVs along multiple paths to different objectives. Thirdly, it presents an optimal branching node strategy that allows the adequate use of the overlapping paths to multiple targets, which decreases the networking cost within the limited communication and searching range. The numerical experiments illustrate that the UMMVN performs better than other state-of-the-art networking methods.

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Coverage Path Planning for SAR-UAV in Search Area Coverage Tasks Based on Deep Reinforcement Learning

Cited by 7 publications

References 8 publications

A survey on autonomous environmental monitoring approaches: towards unifying active sensing and reinforcement learning

A survey on autonomous environmental monitoring approaches: towards unifying active sensing and reinforcement learning

A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

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