{"id":220,"date":"2012-01-10T10:16:54","date_gmt":"2012-01-10T15:16:54","guid":{"rendered":"https:\/\/www.bu.edu\/codes\/?page_id=220"},"modified":"2019-05-21T14:28:44","modified_gmt":"2019-05-21T18:28:44","slug":"cooperative-control","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/codes\/research\/1246-2\/cooperative-control\/","title":{"rendered":""},"content":{"rendered":"<h3>Cooperative Control<\/h3>\n<h4>Introduction<\/h4>\n<p>Cooperative control deals with the problem of controlling a multi-agent robotic system to fulfill a common goal. The tasks associated with these robotic systems include search, exploration, surveillance, rescue operations and mapping unknown or partially known environments. In real-world applications, the control of multiple robots is often complicated by the following factors<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Resource constraints on sensing, motion and communication capabilities, onboard computation capacities, and power supplies<\/li>\n<li>Unknown, uncertain nature of the environments requires robotic systems to be adaptive to environmental changes, accurate in information acquisition, prompt and smart in decision making<\/li>\n<li>Distributed, asynchronous information and computation structures are inherent due to the geographical separation and communication constraints<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4>Goals<\/h4>\n<p>We are interested in the decision-making process in cooperative control applications. We currently concentrate on the cooperative mission control of multiple Uninhabitated Autonomous Vehicles (UAVs) in a battlefield environment. The main objectives include developing the methodology and software that enable<\/p>\n<ul>\n<li>Dynamic task assignment<\/li>\n<li>Vehicle routing and obstacle-free path planning<\/li>\n<li>Distributed and real-time decision making<\/li>\n<li>Optimal trajectory generation under nonholonomic constraints<\/li>\n<\/ul>\n<p><a href=\"\/codes\/files\/2012\/01\/DRH-Addin-target.jpg\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" class=\"aligncenter wp-image-227\" title=\"DRH-Addin target\" src=\"\/codes\/files\/2012\/01\/DRH-Addin-target-636x477.jpg\" alt=\"DRH-Addin target\" width=\"433\" height=\"325\" srcset=\"https:\/\/www.bu.edu\/codes\/files\/2012\/01\/DRH-Addin-target-636x477.jpg 636w, https:\/\/www.bu.edu\/codes\/files\/2012\/01\/DRH-Addin-target.jpg 640w\" sizes=\"(max-width: 433px) 100vw, 433px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<div class=\"bu_collapsible_container\" aria-live=\"polite\" data-customize-animation=\"false\" style=\"overflow: hidden;\">\n<h4 aria-expanded=\"false\" role=\"0\" tabindex=\"0\"><\/h4>\n<h4 aria-expanded=\"false\" role=\"0\" tabindex=\"0\"><\/h4>\n<h4 class=\"bu_collapsible\" aria-expanded=\"false\" role=\"0\" style=\"cursor: pointer;\" tabindex=\"0\">Centralized\u00a0Implementations<\/h4>\n<div class=\"bu_collapsible_section\" style=\"display: none;\">\n<ul>\nThe following videos are made by <em><strong>Wei Li<\/strong><\/em> and<em><strong> Xu Ning<\/strong><\/em>. They implemented it using Khepera II robots. The control strategy is implemented in a central computer and sent to robots via RF wireless communication.<\/p>\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: center;\"><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"http:\/\/www.youtube.com\/embed\/QkbfcuZ6f3w\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<td style=\"text-align: center;\"><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"http:\/\/www.youtube.com\/embed\/FOIRGDmcfI0\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\"><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"http:\/\/www.youtube.com\/embed\/OzUcMqs6VdI\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<td style=\"text-align: center;\"><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"http:\/\/www.youtube.com\/embed\/RK6XCcVupRw\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"bu_collapsible_container\" aria-live=\"polite\" data-customize-animation=\"false\" style=\"overflow: hidden;\">\n<h4 aria-expanded=\"false\" role=\"0\" tabindex=\"0\"><\/h4>\n<h4 aria-expanded=\"false\" role=\"0\" tabindex=\"0\"><\/h4>\n<h4 class=\"bu_collapsible\" aria-expanded=\"false\" role=\"0\" style=\"cursor: pointer;\" tabindex=\"0\">Distributed Implementations<\/h4>\n<div class=\"bu_collapsible_section\" style=\"display: none;\">\n<ul>\nThe following videos are made by <strong><em>Yanfeng Geng<\/em><\/strong>. He implemented it using Khepera III robots. Each robot carries a camera to obtain target information and makes a decision (movement heading and speed) by itself. No central computer and no overhead camera is needed.<\/p>\n<p><strong>Case I:<\/strong> Environment fixed<\/p>\n<table class=\" aligncenter\">\n<tbody>\n<tr>\n<td><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"http:\/\/www.youtube.com\/embed\/YbGx3FzvPcE\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<td><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"http:\/\/www.youtube.com\/embed\/NuWy3nOOjzA\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Case II:<\/strong> One target is added into the mission space<\/p>\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: center;\"><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"http:\/\/www.youtube.com\/embed\/I62NW7qkCUQ\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Case III:<\/strong> There is an obstacle in the mission space and also another target is added in during the mission. Before going back to the homebase, each robot searches around to see whether there is any target left without visiting.<\/p>\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: center;\"><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"http:\/\/www.youtube.com\/embed\/uKd5CeCyc2s\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"bu_collapsible_container\" aria-live=\"polite\" data-customize-animation=\"false\" style=\"overflow: hidden;\">\n<h4 aria-expanded=\"false\" role=\"0\" tabindex=\"0\"><\/h4>\n<h4 aria-expanded=\"false\" role=\"0\" tabindex=\"0\"><\/h4>\n<h4 class=\"bu_collapsible\" aria-expanded=\"false\" role=\"0\" style=\"cursor: pointer;\" tabindex=\"0\">Maximum Reward Collection Problem<\/h4>\n<div class=\"bu_collapsible_section\" style=\"display: none;\">\n<ul>\nThe following is a video from simulation results for MRCP, The mission space has 25 targets distributed uniformly with 2 agents originally located at the base. Target&#8217;s reward value is decreasing by time and as can be seen, some targets have vanished before agents get to reach them in this scenario. The results are:<\/p>\n<table>\n<tbody>\n<tr>\n<td>3L- controller<\/td>\n<td>5-L controller<\/td>\n<\/tr>\n<tr>\n<td><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"https:\/\/www.youtube.com\/embed\/FXBLUnZPkWM\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<td><iframe loading=\"lazy\" width=\"230\" height=\"150\" src=\"https:\/\/www.youtube.com\/embed\/m4PaINeFkZc\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" data-mce-fragment=\"1\"><\/iframe><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/ul>\n<\/div>\n<\/div>\n<p><script type=\"text\/javascript\" src=\"http:\/\/www.bu.edu\/eng\/wp-content\/themes\/r-eng\/js\/script.min.js?ver=1.1.1\"><\/script><\/p>\n<p><script type=\"text\/javascript\" src=\"http:\/\/www.bu.edu\/eng\/wp-includes\/js\/wp-embed.min.js?ver=4.9.10\"><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><\/script><\/p>\n<p><script type=\"text\/javascript\" src=\"http:\/\/www.bu.edu\/eng\/wp-content\/plugins\/bu-front-end-library\/js\/collapsible.js?ver=4.9.10\"><\/script><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cooperative Control Introduction Cooperative control deals with the problem of controlling a multi-agent robotic system to fulfill a common goal. The tasks associated with these robotic systems include search, exploration, surveillance, rescue operations and mapping unknown or partially known environments. In real-world applications, the control of multiple robots is often complicated by the following factors [&hellip;]<\/p>\n","protected":false},"author":4463,"featured_media":0,"parent":1246,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/pages\/220"}],"collection":[{"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/users\/4463"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/comments?post=220"}],"version-history":[{"count":28,"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/pages\/220\/revisions"}],"predecessor-version":[{"id":1513,"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/pages\/220\/revisions\/1513"}],"up":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/pages\/1246"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/codes\/wp-json\/wp\/v2\/media?parent=220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}