{"title":"基于改进人工势场的固定翼无人机群避障算法研究","authors":"Qiping Zhou, Yong Wei, Wei He, Shu-min Shang, Haibo Fan, Weisong Yin","doi":"10.1109/ICARCE55724.2022.10046495","DOIUrl":null,"url":null,"abstract":"In the swarm formation control system by leader-follower strategy, obstacle avoidance is the key requirement of unmanned aerial vehicle (UAV) swarm to coordinate trajectory planning, while the traditional artificial potential field (APF) ignores the problem that the UAV swarm needs to return to the scheduled route immediately after obstacle avoidance. Based on the UAV obstacle avoidance rules, this paper proposes a trajectory planning scheme based on improved artificial potential field (IAPF). Though IAPF, the UAV swarm considers the minimum turning radius factor when avoiding obstacles, does not deviate from the route after obstacle avoidance, and returns to the scheduled route nearby, thus the unity of UAV swarm obstacle avoidance and trajectory planning is realized. The simulation results show that the proposed scheme can effectively solve the problem of UAV swarm returning to the scheduled route immediately after obstacle avoidance.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Research on Obstacle Avoidance Algorithm of Fixed-wing UAV Swarms Based on Improved Artificial Potential Field\",\"authors\":\"Qiping Zhou, Yong Wei, Wei He, Shu-min Shang, Haibo Fan, Weisong Yin\",\"doi\":\"10.1109/ICARCE55724.2022.10046495\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the swarm formation control system by leader-follower strategy, obstacle avoidance is the key requirement of unmanned aerial vehicle (UAV) swarm to coordinate trajectory planning, while the traditional artificial potential field (APF) ignores the problem that the UAV swarm needs to return to the scheduled route immediately after obstacle avoidance. Based on the UAV obstacle avoidance rules, this paper proposes a trajectory planning scheme based on improved artificial potential field (IAPF). Though IAPF, the UAV swarm considers the minimum turning radius factor when avoiding obstacles, does not deviate from the route after obstacle avoidance, and returns to the scheduled route nearby, thus the unity of UAV swarm obstacle avoidance and trajectory planning is realized. The simulation results show that the proposed scheme can effectively solve the problem of UAV swarm returning to the scheduled route immediately after obstacle avoidance.\",\"PeriodicalId\":416305,\"journal\":{\"name\":\"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)\",\"volume\":\"111 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICARCE55724.2022.10046495\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCE55724.2022.10046495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Obstacle Avoidance Algorithm of Fixed-wing UAV Swarms Based on Improved Artificial Potential Field
In the swarm formation control system by leader-follower strategy, obstacle avoidance is the key requirement of unmanned aerial vehicle (UAV) swarm to coordinate trajectory planning, while the traditional artificial potential field (APF) ignores the problem that the UAV swarm needs to return to the scheduled route immediately after obstacle avoidance. Based on the UAV obstacle avoidance rules, this paper proposes a trajectory planning scheme based on improved artificial potential field (IAPF). Though IAPF, the UAV swarm considers the minimum turning radius factor when avoiding obstacles, does not deviate from the route after obstacle avoidance, and returns to the scheduled route nearby, thus the unity of UAV swarm obstacle avoidance and trajectory planning is realized. The simulation results show that the proposed scheme can effectively solve the problem of UAV swarm returning to the scheduled route immediately after obstacle avoidance.