UAV 3D online track planning based on improved SAC algorithm
Online track planning technology is a key technology to improve the survivability and intelligence of UAVs. In this paper, a Soft Actor-Critic (SAC)-based online track planning algorithm is proposed by combining Artificial Potential Field (APF) algorithm and self-attentive mechanism. First, for the...
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| Veröffentlicht in: | Journal of the Brazilian Society of Mechanical Sciences and Engineering 2024, Vol.46 (1), Article 12 |
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| creator | Zhou, Yuxiang Shu, Jiansheng Hao, Hui Song, Huan Lai, Xiaochang |
| description | Online track planning technology is a key technology to improve the survivability and intelligence of UAVs. In this paper, a Soft Actor-Critic (SAC)-based online track planning algorithm is proposed by combining Artificial Potential Field (APF) algorithm and self-attentive mechanism. First, for the characteristics of high-dimensional complexity of environmental states presented in the 3D environmental space, the self-attention mechanism is introduced in the Actor network of SAC algorithm in order to analyze and process the state information. Secondly, the artificial potential field method is combined with the reward function of SAC algorithm to solve the problem of reward sparsity in online track planning. Finally, the observation, action, and reward functions of the algorithm are designed according to the characteristics of UAV online track planning. The experimental results show that the algorithm convergence speed and success rate of the improved SAC algorithm are significantly improved, which can realize the 3D online track planning of UAVs. |
| doi_str_mv | 10.1007/s40430-023-04570-7 |
| format | Article |
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Soc. Mech. Sci. Eng</addtitle><description>Online track planning technology is a key technology to improve the survivability and intelligence of UAVs. In this paper, a Soft Actor-Critic (SAC)-based online track planning algorithm is proposed by combining Artificial Potential Field (APF) algorithm and self-attentive mechanism. First, for the characteristics of high-dimensional complexity of environmental states presented in the 3D environmental space, the self-attention mechanism is introduced in the Actor network of SAC algorithm in order to analyze and process the state information. Secondly, the artificial potential field method is combined with the reward function of SAC algorithm to solve the problem of reward sparsity in online track planning. Finally, the observation, action, and reward functions of the algorithm are designed according to the characteristics of UAV online track planning. The experimental results show that the algorithm convergence speed and success rate of the improved SAC algorithm are significantly improved, which can realize the 3D online track planning of UAVs.</description><subject>Algorithms</subject><subject>Engineering</subject><subject>Mechanical Engineering</subject><subject>Potential fields</subject><subject>State (computer science)</subject><subject>Survivability</subject><subject>Technical Paper</subject><issn>1678-5878</issn><issn>1806-3691</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEUhYMoWKt_wFXAdfTmOQm4KfUJBRdatyGTJnXqdKYmo-C_NzqCO1f3wD3nPj6ETimcU4DqIgsQHAgwTkDICki1hyZUgyJcGbpftKo0kbrSh-go5w0AZ1LJCbpczp4xv8J91zZdwENy_hXvWtd1TbfGtcthVXq42e5S_1H042yOXbvuUzO8bI_RQXRtDie_dYqWN9dP8zuyeLi9n88WxHNqBhK18V6aWnBW0xCMWCkmtKSqXmkXhGeC8qIjDb42UfuoZeBUQXSc11oyPkVn49xyxNt7yIPd9O-pKystM1D-18JAcbHR5VOfcwrR7lKzdenTUrDflOxIyRZK9oeSrUqIj6FczN06pL_R_6S-AK7FaJw</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Zhou, Yuxiang</creator><creator>Shu, Jiansheng</creator><creator>Hao, Hui</creator><creator>Song, Huan</creator><creator>Lai, Xiaochang</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-1239-2526</orcidid></search><sort><creationdate>2024</creationdate><title>UAV 3D online track planning based on improved SAC algorithm</title><author>Zhou, Yuxiang ; Shu, Jiansheng ; Hao, Hui ; Song, Huan ; Lai, Xiaochang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-f89cc59b432b1ee94d6248516bd8ae4c24136bdf1ecb9f8cf85e3160fa33b8523</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Engineering</topic><topic>Mechanical Engineering</topic><topic>Potential fields</topic><topic>State (computer science)</topic><topic>Survivability</topic><topic>Technical Paper</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Yuxiang</creatorcontrib><creatorcontrib>Shu, Jiansheng</creatorcontrib><creatorcontrib>Hao, Hui</creatorcontrib><creatorcontrib>Song, Huan</creatorcontrib><creatorcontrib>Lai, Xiaochang</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of the Brazilian Society of Mechanical Sciences and Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Yuxiang</au><au>Shu, Jiansheng</au><au>Hao, Hui</au><au>Song, Huan</au><au>Lai, Xiaochang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>UAV 3D online track planning based on improved SAC algorithm</atitle><jtitle>Journal of the Brazilian Society of Mechanical Sciences and Engineering</jtitle><stitle>J Braz. Soc. Mech. Sci. Eng</stitle><date>2024</date><risdate>2024</risdate><volume>46</volume><issue>1</issue><artnum>12</artnum><issn>1678-5878</issn><eissn>1806-3691</eissn><abstract>Online track planning technology is a key technology to improve the survivability and intelligence of UAVs. In this paper, a Soft Actor-Critic (SAC)-based online track planning algorithm is proposed by combining Artificial Potential Field (APF) algorithm and self-attentive mechanism. First, for the characteristics of high-dimensional complexity of environmental states presented in the 3D environmental space, the self-attention mechanism is introduced in the Actor network of SAC algorithm in order to analyze and process the state information. Secondly, the artificial potential field method is combined with the reward function of SAC algorithm to solve the problem of reward sparsity in online track planning. Finally, the observation, action, and reward functions of the algorithm are designed according to the characteristics of UAV online track planning. The experimental results show that the algorithm convergence speed and success rate of the improved SAC algorithm are significantly improved, which can realize the 3D online track planning of UAVs.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s40430-023-04570-7</doi><orcidid>https://orcid.org/0000-0003-1239-2526</orcidid></addata></record> |
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| subjects | Algorithms Engineering Mechanical Engineering Potential fields State (computer science) Survivability Technical Paper |
| title | UAV 3D online track planning based on improved SAC algorithm |
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