Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection
Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm inte...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1104 |
| _version_ | 1827342850453405696 |
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| author | Jinwei Zhang Xijing Zhu Jing Li |
| author_facet | Jinwei Zhang Xijing Zhu Jing Li |
| author_sort | Jinwei Zhang |
| collection | DOAJ |
| description | Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA’s search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping–firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms. |
| first_indexed | 2024-03-07T22:15:29Z |
| format | Article |
| id | doaj.art-f3e064bf780d455b82540f247c58ab6a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:15:29Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f3e064bf780d455b82540f247c58ab6a2024-02-23T15:33:35ZengMDPI AGSensors1424-82202024-02-01244110410.3390/s24041104Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV InspectionJinwei Zhang0Xijing Zhu1Jing Li2School of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaIntelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA’s search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping–firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms.https://www.mdpi.com/1424-8220/24/4/1104UAVsparrow search algorithmfirefly algorithmchaotic sequencepath planning |
| spellingShingle | Jinwei Zhang Xijing Zhu Jing Li Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection Sensors UAV sparrow search algorithm firefly algorithm chaotic sequence path planning |
| title | Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection |
| title_full | Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection |
| title_fullStr | Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection |
| title_full_unstemmed | Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection |
| title_short | Intelligent Path Planning with an Improved Sparrow Search Algorithm for Workshop UAV Inspection |
| title_sort | intelligent path planning with an improved sparrow search algorithm for workshop uav inspection |
| topic | UAV sparrow search algorithm firefly algorithm chaotic sequence path planning |
| url | https://www.mdpi.com/1424-8220/24/4/1104 |
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