{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:00:34Z","timestamp":1772643634258,"version":"3.50.1"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. The existing UAV path planning method for fixed-point devices is usually acceptable for simple UAV flight scenes. However, it is unsuitable for forest patrol. Meanwhile, when collecting data, the UAV should consider the timeliness of the collected data. The paper proposes two-point path planning and multi-point path planning methods to maximize the amount of fresh information collected from ground-fixed devices in a complicated forest environment. Firstly, we adopt chaotic initialization and co-evolutionary algorithmto solve the two-point path planning issue considering all significant UAV performance and environmental factors. Then, a UAV path planning method based on simulated annealing is proposed for the multi-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the proposed approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning strategies. The results reveal that the proposed ways can effectively produce a UAV patrol path with higher information freshness in fewer iterations and at a lower computing cost, suggesting the practical value of the proposed approach.<\/jats:p>","DOI":"10.3389\/fnbot.2022.1105177","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T05:22:25Z","timestamp":1671686545000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["UAV path planning method for data collection of fixed-point equipment in complex forest environment"],"prefix":"10.3389","volume":"16","author":[{"given":"Xiaohui","family":"Cui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanzhang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Mou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"B1","first-page":"1","article-title":"Joint optimization of Age of information and energy efficiency in IoT networks","author":"Abbas","year":"2022","journal-title":"Proceedings of the 2020 IEEE 91st vehicular technology conference (VTC2020-Spring)"},{"key":"B2","first-page":"1","article-title":"Deep reinforcement learning for minimizing age-of-information in UAV-assisted networks","author":"Abd-Elmagid","year":"2019","journal-title":"Proceedings of the 2019 IEEE global communications conference"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2021.107287","article-title":"Multi-strategy fusion differential evolution algorithm for UAV path planning in complex environment.","volume":"121","author":"Chai","year":"2022","journal-title":"Aerosp. Sci. Technol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1080\/00207721.2014.929191","article-title":"UAV path planning using artificial potential field method updated by optimal control theory.","volume":"47","author":"Chen","year":"","journal-title":"Int. J. Syst. Sci."},{"key":"B5","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1016\/j.neucom.2015.07.044","article-title":"Modified central force optimization (MCFO) algorithm for 3D UAV path planning.","volume":"171","author":"Chen","year":"","journal-title":"Neurocomputing"},{"key":"B6","article-title":"An improved ABC algorithm for optimal path planning.","volume":"2","author":"Goel","year":"2013","journal-title":"Int. J. Sci. Res."},{"key":"B7","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.1109\/CCDC.2013.6561444","article-title":"Path planning for indoor UAV based on Ant Colony Optimization","author":"He","year":"2013","journal-title":"Proceedings of the 2013 25th chinese control and decision conference (CCDC)"},{"key":"B8","doi-asserted-by":"publisher","first-page":"150162","DOI":"10.1109\/ACCESS.2020.3016118","article-title":"Risk assessment model for UAV cost-effective path planning in urban environments.","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/lcomm.2018.2822700","article-title":"Power-efficient communication in UAV-aided wireless sensor networks.","volume":"22","author":"Hua","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCW.2019.8756751","article-title":"Age-based path planning and data acquisition in UAV-assisted iot networks","author":"Jia","year":"2019","journal-title":"Proceedings of the 2019 IEEE international conference on communications workshops (ICC Workshops"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCYB.2022.3159367","article-title":"Distributed and time-delayed k-winner-take-all network for competitive coordination of multiple robots.","volume":"2022","author":"Jin","year":"","journal-title":"IEEE Trans. Cybern."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tac.2022.3144135","article-title":"Gradient-based differential neural-solution to time-dependent nonlinear optimization.","author":"Jin","year":"2022","journal-title":"IEEE Trans. Autom. Contr."},{"key":"B13","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1109\/INFCOM.2012.6195689","article-title":"Real-time status: How often should one update?","author":"Kaul","year":"2012","journal-title":"2012 Proceedings IEEE INFOCOM"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3390\/app12030943","article-title":"Adaptive metaheuristic-based methods for autonomous robot path planning: Sustainable agricultural applications.","volume":"12","author":"Kiani","year":"2022","journal-title":"Appl. Sci."},{"key":"B15","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1109\/TWC.2020.3041750","article-title":"UAV-aided data collection for information freshness in wireless sensor networks.","volume":"20","author":"Liu","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"B16","doi-asserted-by":"publisher","first-page":"553","DOI":"10.3390\/s18051519","article-title":"Age-optimal trajectory planning for UAV-assisted data collection","author":"Liu","year":"2018","journal-title":"IEEE INFOCOM 2018 - IEEE conference on computer communications workshops"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/jas.2022.105731","article-title":"Gradient-based differential kWTA network with application to competitive coordination of multiple robots.","volume":"9","author":"Liu","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"key":"B18","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/J.AST.2016.08.017","article-title":"Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization.","volume":"58","author":"Liu","year":"2016","journal-title":"Aerosp. Sci. Technol."},{"key":"B19","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/ICUAS.2016.7502625","article-title":"Path planning for a UAV with kinematic constraints in the presence of polygonal obstacles","author":"Maini","year":"2016","journal-title":"Proceedings of the 2016 international conference on unmanned aircraft systems (ICUAS)"},{"key":"B20","doi-asserted-by":"publisher","first-page":"2836","DOI":"10.1109\/tvt.2021.3061243","article-title":"Space pruning based time minimization in delay constrained multi-task UAV-based sensing.","volume":"70","author":"Meng","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-85640-5_12","article-title":"Path planning for cooperating unmanned vehicles over 3-D terrain","author":"Nikolos","year":"2009","journal-title":"Informatics in control, automation and robotics. Lecture notes in electrical engineering"},{"key":"B22","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/J.AST.2011.02.006","article-title":"A new vibrational genetic algorithm enhanced with a voronoi diagram for path planning of autonomous UAV.","volume":"16","author":"Pehlivanoglu","year":"2012","journal-title":"Aerosp. Sci. Technol."},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1016\/J.ASOC.2021.107796","article-title":"An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problems.","volume":"112","author":"Pehlivanoglu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"B24","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1109\/tsmc.2017.2751259","article-title":"New discrete-time models of zeroing neural network solving systems of time-variant linear and nonlinear inequalities.","volume":"50","author":"Shi","year":"2020","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"B25","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1109\/TNNLS.2020.3028136","article-title":"Novel discrete-time recurrent neural networks handling discrete-form time-variant multi-augmented sylvester matrix problems and manipulator application.","volume":"33","author":"Shi","year":"2022","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst."},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCW.2019.8756665","article-title":"UAV-Enabled age-optimal data collection in wireless sensor networks","author":"Tong","year":"2019","journal-title":"Proceedings of the 2019 IEEE international conference on communications workshops (ICC Workshops)"},{"key":"B27","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/ICUAS.2015.7152275","article-title":"2D path planning for UAVs in radar threatening environment using simulated annealing algorithm","author":"Turker","year":"2015","journal-title":"Proceedings of the 2015 international conference on unmanned aircraft systems (ICUAS)"},{"key":"B28","first-page":"8612","article-title":"Path planning and obstacle avoidance of unmanned aerial vehicle based on improved genetic algorithms","author":"Wang","year":"2014","journal-title":"2014 33rd chinese control conference (CCC)"},{"key":"B29","doi-asserted-by":"publisher","first-page":"4461","DOI":"10.1109\/tcomm.2021.3065135","article-title":"UAV-to-device underlay communications: Age of information minimization by multi-agent deep reinforcement learning.","volume":"69","author":"Wu","year":"2021","journal-title":"IEEE Trans. Commun."},{"key":"B30","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/ISDEA.2012.184","article-title":"Flight path planning based on an improved genetic algorithm","author":"Xiao-Ting","year":"2013","journal-title":"Proceedings of the 2013 third international conference on intelligent system design and engineering applications (ISDEA)"},{"key":"B31","doi-asserted-by":"publisher","first-page":"5679","DOI":"10.1109\/tsmc.2021.3129794","article-title":"An Acceleration-level data-driven repetitive motion planning scheme for kinematic control of robots with unknown structure.","volume":"52","author":"Xie","year":"2022","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"B32","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s10846-019-01073-3","article-title":"Towards real-time path planning through deep reinforcement learning for a UAV in dynamic environments.","volume":"98","author":"Yan","year":"2020","journal-title":"J. Intell. Robot. Syst."},{"key":"B33","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1109\/TRO.2015.2459812","volume":"31","author":"Yang","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"B34","doi-asserted-by":"publisher","first-page":"13671","DOI":"10.1109\/ACCESS.2018.2812896","article-title":"Optimal UAV path planning: Sensing data acquisition over iot sensor networks using multi-objective bio-inspired algorithms.","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.3389\/fpls.2022.998962","article-title":"Flight path planning of agriculture UAV based on improved artificial potential field method","author":"Yingkun","year":"2018","journal-title":"Proceedings of the 2018 Chinese control and decision conference (CCDC)"},{"key":"B36","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1109\/twc.2019.2902559","article-title":"Energy minimization for wireless communication with rotary-wing UAV.","volume":"18","author":"Zeng","year":"2019","journal-title":"IEEE Trans. Wireless Commun."},{"key":"B37","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.neucom.2012.09.019","article-title":"Robot path planning in uncertain environment using multi-objective particle swarm optimization.","volume":"103","author":"Zhang","year":"2013","journal-title":"Neurocomputing"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2022.1105177\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T05:22:31Z","timestamp":1671686551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2022.1105177\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,22]]},"references-count":37,"alternative-id":["10.3389\/fnbot.2022.1105177"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2022.1105177","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,22]]},"article-number":"1105177"}}