An intelligent UAV path planning optimization method for monitoring the risk of unattended offshore oil platforms

Wang, Yingying; Li, Yuqi; Yin, Feng; Wang, Wentao; Sun, Hao; Liu, Jianchang; Zhuang, Ke

doi:10.1016/j.psep.2022.02.011

Cited by 20 publications

(11 citation statements)

References 40 publications

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“…Therefore, this study draws lessons from the antibody concentration suppression mechanism in the immune algorithm and regularly filters these similar transitions based on the vector distance method to reduce memory occupation and improve the operation efficiency of the algorithm. Suppose {𝑆 𝑡 2 ] are vectors, their vector distances can be calculated as follows:…”

Section: ) Similar Transition Filtering Based On the Vector Distancementioning

confidence: 99%

“…Currently, unmanned aerial vehicle (UAV) are widely used in military applications. Many tasks that must be performed by traditional aircraft are increasingly being replaced by UAV, such as search and rescue, reconnaissance, surveillance, and ground attacks [1,2]. However, the autonomous decision ability of UAV in complex situations still needs to be significantly improved, such as the decision ability in unmanned autonomous air combat.…”

Section: Introductionmentioning

confidence: 99%

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Research on Maneuvering Decision Algorithm Based on Improved Deep Deterministic Policy Gradient

Xianyong

Hou

et al. 2022

IEEE Access

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Autonomous maneuvering decisions of unmanned aerial vehicle (UAV) in short-range air combat remain a challenging research topic, and a decision method based on an improved deep deterministic policy gradient (DDPG) is proposed. First, the problem model is improved from the perspective of energy-air combat, and a decision model with engine thrust, angle of attack, and roll angle as control variables is established. The normal and tangential overloads are determined by these control variables, and the decision is constrained by the flight stability and threshold range. Subsequently, the decision learning algorithm of the maneuver command is designed based on the DDPG framework. According to the energy air combat, speed is introduced into the return function in some states to make the return value more in line with reality. In view of the slow learning speed of the DDPG algorithm, the winning rate is introduced into the ε-greedy strategy to adjust the exploration and application probabilities in real time. In view of the decrease in computational efficiency caused by the large amount of empirical data, a similar empirical exclusion was carried out based on the vector distance. The simulation results show that the DDPG-based algorithm realizes autonomous decisions of engine thrust, roll angle, and attack angle under constraints, and the comparative simulation shows that the improvement measures are effective.INDEX TERMS Unmanned aerial vehicle (UAV), maneuvering decision, deep deterministic policy gradient (DDPG), short-range air combat, reinforcement Learning (RL)

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Section: ) Similar Transition Filtering Based On the Vector Distancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Maneuvering Decision Algorithm Based on Improved Deep Deterministic Policy Gradient

Xianyong

Hou

et al. 2022

IEEE Access

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“…Multirotor drones have seen continuous development over the past ten years as the need for this type of platform has grown continuously, and they are employed in a wide range of activities and fields such as inspection of large industrial installations [ 11 , 12 ], large buildings and constructions, oil and gas pipelines [ 13 , 14 ], inspection of continuous-flow machinery in quarries (to monitor temperatures in the area of high-friction pits using thermal imaging cameras), inspection of petrochemical installations (to detect cracks, fissures and leaks that may occur in pressure vessels using thermal imaging cameras [ 15 , 16 ]), etc. They can be equipped with a wide range of electromagnetic spectrum sensors [ 17 , 18 ], gamma ray sensors [ 19 ], biological sensors [ 20 , 21 , 22 ] and chemical sensors [ 23 , 24 ], which provide remote sensing functions.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems

Stamate

Pupăză

Nicolescu

et al. 2023

Sensors

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Today, there is a conspicuous upward trend for the development of unmanned aerial vehicles (UAVs), especially in the field of multirotor drones. Their advantages over fixed-wing aircrafts are that they can hover, which allows their usage in a wide range of remote surveillance applications: industrial, strategic, governmental, public and homeland security. Moreover, because the component market for this type of vehicles is in continuous growth, new concepts have emerged to improve the stability and reliability of the multicopters, but efficient solutions with reduced costs are still expected. This work is focused on hexacopter UAV tests carried out on an original platform both within laboratory and on unrestricted open areas during the start–stop manoeuvres of the motors to verify the operational parameters, hover flight, the drone stability and reliability, as well as the aerodynamics and robustness at different wind speeds. The flight parameters extracted from the sensor systems’ comprising accelerometers, gyroscopes, magnetometers, barometers, GPS antenna and EO/IR cameras were analysed, and adjustments were performed accordingly, when needed. An FEM simulation approach allowed an additional decision support platform that expanded the experiments in the virtual environment. Finally, practical conclusions were drawn to enhance the hexacopter UAV stability, reliability and manoeuvrability.

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“…Initially designed for military purposes, UAVs have seamlessly transitioned into civilian domains [2,3]. Their applications range from military surveillance, reconnaissance, and combat operations, significantly contributing to reducing human casualties [4], to civilian applications such as forest-fire monitoring [5], rescue missions [6], agriculture [7], offshore platform operations [8], traffic management [9], drone delivery [10], medical emergencies [11], tracking operations [12], and tasks relating to smart cities [13,14]. UAVs play a crucial role in accessing remote or inaccessible areas and serve as pivotal tools in emergency scenarios, such as disaster rescue operations [15,16].…”

Section: Introductionmentioning

confidence: 99%

Meta-heuristic Algorithms in UAV Path Planning Optimization: A Systematic Review (2018–2022)

Hooshyar,

Huang

2023

Drones

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Unmanned Aerial Vehicles (UAVs), a subset of aerial robots, play crucial roles in various domains, such as disaster management, agriculture, and healthcare. Their application proves invaluable in situations where human intervention poses risks or involves high costs. However, traditional approaches to UAV path planning struggle in efficiently navigating complex and dynamic environments, often resulting in suboptimal routes and extended mission durations. This study seeks to investigate and improve the utilization of meta-heuristic algorithms for optimizing UAV path planning. Toward this aim, we carried out a systematic review of five major databases focusing on the period from 2018 to 2022. Following a rigorous two-stage screening process and a thorough quality appraisal, we selected 68 papers out of the initial 1500 to answer our research questions. Our findings reveal that hybrid algorithms are the dominant choice, surpassing evolutionary, physics-based, and swarm-based algorithms, indicating their superior performance and adaptability. Notably, time optimization takes precedence in mathematical models, reflecting the emphasis on CPU time efficiency. The prevalence of dynamic environmental types underscores the importance of real-time considerations in UAV path planning, with three-dimensional (3D) models receiving the most attention for accuracy in complex trajectories. Additionally, we highlight the trends and focuses of the UAV path planning optimization research community and several challenges in using meta-heuristic algorithms for the optimization of UAV path planning. Finally, our analysis further highlights a dual focus in UAV research, with a significant interest in optimizing single-UAV operations and a growing recognition of the challenges and potential synergies in multi-UAV systems, alongside a prevalent emphasis on single-target mission scenarios, but with a notable subset exploring the complexities of multi-target missions.

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An intelligent UAV path planning optimization method for monitoring the risk of unattended offshore oil platforms

Cited by 20 publications

References 40 publications

Research on Maneuvering Decision Algorithm Based on Improved Deep Deterministic Policy Gradient

Research on Maneuvering Decision Algorithm Based on Improved Deep Deterministic Policy Gradient

Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems

Meta-heuristic Algorithms in UAV Path Planning Optimization: A Systematic Review (2018–2022)

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