This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios."> MathJax.Hub.Config({ "HTML-CSS": { availableFonts: ["TeX"], preferredFonts: "TeX", webFont:"TeX", imageFont:"TeX", undefinedFamily:'Arial Unicode MS',serif", scale: 80, linebreaks: { automatic: true, width: "container" } }, "TeX": { extensions: ["noErrors.js"], noErrors: { inlineDelimiters: ["",""], multiLine: true, style: { "font-size": "90%", "text-align": "left", "color": "black", "padding": "1px 3px", "border": "1px solid" } } } }); MathJax.Hub.Register.StartupHook("End",function () { $(".art-abstract").css("display", "block"); }); (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','https://www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-5824718-2', 'auto'); ga('require', 'displayfeatures'); ga('send', 'pageview'); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-WPK7SW5'); _linkedin_partner_id = "2846186"; window._linkedin_data_partner_ids = window._linkedin_data_partner_ids || []; window._linkedin_data_partner_ids.push(_linkedin_partner_id); (function(){var s = document.getElementsByTagName("script")[0]; var b = document.createElement("script"); b.type = "text/javascript";b.async = true; b.src = "https://snap.licdn.com/li.lms-analytics/insight.min.js"; s.parentNode.insertBefore(b, s);})(); Next Article in Journal
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Open AccessArticleTwo-Stage Pursuit Strategy for Incomplete-Information Impulsive Space Pursuit-Evasion Mission Using Reinforcement Learning byBin Yang 1, Pengxuan Liu 1, Jinglang Feng 2 and Shuang Li 1,* 1College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China2Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UK*Author to whom correspondence should be addressed. Academic Editor: Fanghua JiangAerospace 2021, 8(10), 299; https://doi.org/10.3390/aerospace8100299Received: 29 August 2021 / Revised: 7 October 2021 / Accepted: 7 October 2021 / Published: 14 October 2021(This article belongs to the Special Issue Spacecraft Dynamics and Control)
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Cite This PaperAbstractThis paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios.Keywords: space pursuit-evasion mission; incomplete-information game; reinforcement learning; impulsive propulsion; J2 perturbation space pursuit-evasion mission; incomplete-information game; reinforcement learning; impulsive propulsion; J2 perturbationThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedShare and CiteMDPI and ACS Style

Yang, B.; Liu, P.; Feng, J.; Li, S. Two-Stage Pursuit Strategy for Incomplete-Information Impulsive Space Pursuit-Evasion Mission Using Reinforcement Learning. Aerospace 2021, 8, 299.https://doi.org/10.3390/aerospace8100299

AMA Style

Yang B, Liu P, Feng J, Li S. Two-Stage Pursuit Strategy for Incomplete-Information Impulsive Space Pursuit-Evasion Mission Using Reinforcement Learning. Aerospace. 2021; 8(10):299.https://doi.org/10.3390/aerospace8100299

Chicago/Turabian Style

Yang, Bin, Pengxuan Liu, Jinglang Feng, and Shuang Li. 2021. "Two-Stage Pursuit Strategy for Incomplete-Information Impulsive Space Pursuit-Evasion Mission Using Reinforcement Learning" Aerospace 8, no. 10: 299.https://doi.org/10.3390/aerospace8100299

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Not used anymore (this code can be moved to somewhere for storage in case we need similar logic with other iterations) // function checkDisplaySurvey() { var surveyDisabled = localStorage.getItem("mdpi_disable_survey"); var viewCount = localStorage.getItem("mdpi_new_layout_views"); var viewLimit = localStorage.getItem("mdpi_new_layout_limit"); if (null !== surveyDisabled) { return; } return; // todo: the below part can be removed later on... if (null === viewLimit) { localStorage.setItem("mdpi_new_layout_limit", Math.floor(Math.random() * 50)); localStorage.setItem("mdpi_new_layout_views", 0); } else { viewCount++; localStorage.setItem("mdpi_new_layout_views", viewCount); if (viewCount >= viewLimit) { $.ajax({ url: "/ajax_cookie_value/mdpi_disable_survey", success: function(data) { if (data.value) { localStorage.setItem("mdpi_disable_survey", true); } else { $(".js-survey").show(); } } }); } } } var banners_url = 'https://serve.mdpi.com'; $(document).ready(function() { var helpFunctions = $(".middle-column__help__fixed"); var middleColumn = $("#middle-column"); helpFunctions.affix({ offset: { top: function() { return middleColumn.offset().top - 8 - (Foundation.utils.is_medium_only() ? 30 : 0); }, bottom: function() { return $("#footer").innerHeight() + 56 + (Foundation.utils.is_medium_only() ? 0 : 0); } } }); new ClipboardJS('.js-clipboard-copy'); }); $(document).ready(function() { $(".link-article-menu").click(function(e) { e.preventDefault(); $(this).find('span').toggle(); $(this).next("div").toggleClass("active"); }); }); $(document).ready(function() { $(document).on('keyup', function (e) { if (e.keyCode == 27) { var hElem = $(this).find(".annotator-adder"); if (hElem.length){ hElem.css({'visibility':'hidden'}); } else { document.querySelector("hypothesis-adder").shadowRoot.querySelector(".annotator-adder").style.visibility = "hidden"; } } }); }); window.hypothesisConfig = function () { return { sidebarAppUrl: 'https://commenting.mdpi.com/app.html', showHighlights: 'whenSidebarOpen' , openSidebar: false , assetRoot: 'https://commentingres.mdpi.com/hypothesis', services: [{ apiUrl: 'https://commenting.mdpi.com/api/', authority: 'mdpi', grantToken: ', doi: '10.3390/aerospace8100299' }], }; }; MathJax . Hub . Startup . signal . Interest ( function ( message ) { if ( message == 'End' ){ var hypoLink = document . getElementById ( "hypothesis_frame" ) hypoLink . setAttribute ( "src" , "https://commenting.mdpi.com/embed.js" ) }}); $(document).ready(function() { var scifeedCounter = 0; var search = window.location.search; if (0 === search.indexOf('?type=check_update&version=')) { $.ajax({ url: "/2226-4310/8/10/299" + "/versioncheck" + search, success: function(result) { $(".js-check-update-container").html(result); } }); } $('#feed_option').click(function() { // tracker if ($('#scifeed_clicked').length0) { event.preventDefault(); // alert('Here there would be a captcha because user is not logged in'); var captchaSection = $("#captchaSection"); if (captchaSection.hasClass('ui-helper-hidden')) { captchaSection.removeClass('ui-helper-hidden').find('input').prop('disabled', false); var img = captchaSection.find('img'); img.attr('src', img.data('url') + "?" + (new Date()).getTime()); $("#reloadCaptcha").trigger("click"); } } }); $(document).on('click', '.scifeed_msg', function(){ $(this).hide(); }); $(document).on('click', '.article-scilit-search', function(e) { e.preventDefault(); var data = $(".article-scilit-search-data").val(); var dataArray = data.split(';').map(function(keyword) { return "("" + keyword.trim() + "")"; }); var searchQuery = dataArray.join(" OR "); var searchUrl = encodeURI("https://www.scilit.net/articles/search?q="+ searchQuery + "&advanced=1&highlight=1"); var win = window.open(searchUrl, '_blank'); if (win) { win.focus(); } else { window.location(searchUrl); } }); display_stats(); citedCount(); follow_goto(); // Check if one of the related widget is loaded... $(document).on('DOMNodeInserted', function(e) { // THE IF STATEMENT TO INSERT RELATED "TAB" if ($(e.target).hasClass('trendmd-widget-container') || $(e.target).hasClass('related_body') ) { // Insert the tab... $('

» Author: Bin Yang

» Reference: Aerospace 2021, Vol. 8, Page 299

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