Minimum dose path planning for facility inspection based on the discrete Rao-combined ABC algorithm in radioactive environments with obstacles

Hong, Kwon Ryong; O, Su Il; Kim, Ryon Hui; Kim, Tae Song; Kim, Jang Su

doi:10.1007/s41365-023-01203-5

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…To optimize the UAV flight route, heuristic algorithms, which are widely used in UAV path planning [10][11][12][13], are considered. Among those heuristic algorithms [14][15][16][17][18], the artificial bee colony (ABC) algorithm is characterized by simple parameter settings and a strong global search ability, making it particularly suitable for UAV path planning [19][20][21][22][23]. However, the ABC algorithm has certain drawbacks, such as susceptibility to local optima, and extremely simple scout bee activation mechanism.…”

Section: Introductionmentioning

confidence: 99%

A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

Chen,

Liang,

Meng

2023

Remote Sensing

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To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles is proposed, considering the observation angle constraints under two conditions: whether there is an obstacle around the target or not. To efficiently find the optimal observation angles, half of the population that is lower-quality generates bit points through opposition-based learning. The algorithm searches for better individuals near the bit points when generating new solutions. Furthermore, to prevent individuals from observing targets repeatedly from similar angles, the concept of individual abandonment probability is proposed. The algorithm can adaptively abandon similar solutions based on the relative position between the individual and the population. To verify the effectiveness of the proposed method, information acquisition experiments were conducted on real residential buildings, and the results of 3D reconstruction were analyzed. The experiment results show that while model accuracy is comparable to that of the comparison method, the number of images obtained is reduced to one-fourth of the comparison method. The operation time is significantly reduced, and 3D reconstruction efficiency is remarkably improved.

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Section: Introductionmentioning

confidence: 99%

A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

Chen,

Liang,

Meng

2023

Remote Sensing

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Chaotic Motion Planning for Mobile Robots: Progress, Challenges, and Opportunities

Ahuraka,

Mcnamee,

Wang

et al. 2023

IEEE Access

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Optimizing radiation exposure in dynamic radioactive environments: Dynamic ACO-RRT* path planning algorithm

Hong,

Kim,

Kim

et al. 2024

Radiation Protection and Environment

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Radioactive environments are often dynamic for various reasons, such as the dismantling of nuclear facilities, leakage of radiation sources, and nuclear accidents. Path planning in a dynamic radioactive environment is one of the important issues in radiation protection for nuclear facility workers. In traditional dynamic optimal path planning, there are phenomena, in which the optimal path is not found within the allowable time or the quality of the path searched within the allowable time is low. To solve this problem, we propose a hybrid algorithm that combines meta-heuristic and sample-based algorithms, called dynamic ant colony optimization-rapidly exploring random tree star (ACO-RRT*). A low-level grid optimal path consisting of grid vertices is obtained by the ACO algorithm, and then it is further refined by the RRT* algorithm. At every instant of environmental change, the vertices of the grid optimal path are divided into vertices with and without samples, and new samples are generated only around vertices without samples. Sampling optimization is performed only around the grid optimal path using new samples and already existing samples. Simulations in a virtual radioactive environment showed that the proposed algorithm reduces computational costs and improves the quality of the optimal path compared to traditional algorithms.

show abstract

Minimum dose path planning for facility inspection based on the discrete Rao-combined ABC algorithm in radioactive environments with obstacles

Cited by 3 publications

References 26 publications

A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

Chaotic Motion Planning for Mobile Robots: Progress, Challenges, and Opportunities

Optimizing radiation exposure in dynamic radioactive environments: Dynamic ACO-RRT* path planning algorithm

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