{"id":565253,"date":"2023-03-14T20:48:09","date_gmt":"2023-03-15T00:48:09","guid":{"rendered":"https:\/\/www.therobotreport.com\/?p=565253"},"modified":"2023-03-14T20:48:43","modified_gmt":"2023-03-15T00:48:43","slug":"mit-traffic-cop-algorithm-helps-drone-swarm-stay-on-task","status":"publish","type":"post","link":"https:\/\/www.therobotreport.com\/mit-traffic-cop-algorithm-helps-drone-swarm-stay-on-task\/","title":{"rendered":"MIT &#8216;traffic cop&#8217; algorithm helps drones stay on task"},"content":{"rendered":"<div id=\"attachment_565254\" style=\"width: 910px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-565254\" src=\"https:\/\/www.therobotreport.com\/wp-content\/uploads\/2023\/03\/MIT_WiSwarm-01-PRESS_0.jpg\" alt=\"\" width=\"900\" height=\"600\" class=\"size-full wp-image-565254\" srcset=\"https:\/\/www.therobotreport.com\/wp-content\/uploads\/2023\/03\/MIT_WiSwarm-01-PRESS_0.jpg 900w, https:\/\/www.therobotreport.com\/wp-content\/uploads\/2023\/03\/MIT_WiSwarm-01-PRESS_0-300x200.jpg 300w, https:\/\/www.therobotreport.com\/wp-content\/uploads\/2023\/03\/MIT_WiSwarm-01-PRESS_0-150x100.jpg 150w, https:\/\/www.therobotreport.com\/wp-content\/uploads\/2023\/03\/MIT_WiSwarm-01-PRESS_0-768x512.jpg 768w, https:\/\/www.therobotreport.com\/wp-content\/uploads\/2023\/03\/MIT_WiSwarm-01-PRESS_0-357x238.jpg 357w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/><p id=\"caption-attachment-565254\" class=\"wp-caption-text\">MIT engineers developed a method to tailor any wireless network to handle a high load of time-sensitive data coming from multiple sources. | Credit: Christine Daniloff, MIT<\/p><\/div>\n<p>How fresh are your data? For <a href=\"https:\/\/www.therobotreport.com\/category\/robots-platforms\/uav-drones\/\">drones<\/a> searching a disaster zone or robots inspecting a building, working with the freshest data is key to locating a survivor or reporting a potential hazard. But when multiple robots simultaneously relay time-sensitive information over a wireless network, a traffic jam of data can ensue. Any information that gets through is too stale to consider as a useful, real-time report.<\/p>\n<p>Now, MIT engineers may have a solution. They\u2019ve developed a method to tailor any wireless network to handle a high load of time-sensitive data coming from multiple sources. Their new approach, <a href=\"http:\/\/MIT 'traffic cop' algorithm helps drone swarm stay on task\" rel=\"noopener\" target=\"_blank\">called WiSwarm<\/a>, configures a wireless network to control the flow of information from multiple sources while ensuring the network is relaying the freshest data.<\/p>\n<p>The team used their method to tweak a conventional Wi-Fi router, and showed that the tailored network could act like an efficient traffic cop, able to prioritize and relay the freshest data to keep multiple vehicle-tracking drones on task.<\/p>\n<p>The team\u2019s method, which they will present in May at IEEE\u2019s International Conference on Computer Communications (INFOCOM), offers a practical way for multiple robots to communicate over available Wi-Fi networks so they don\u2019t have to carry bulky and expensive communications and processing hardware onboard.<\/p>\n<h2>Last in line<\/h2>\n<p>The team\u2019s approach departs from the typical way in which robots are designed to communicate data.<\/p>\n<p>\u201cWhat happens in most standard networking protocols is an approach of first come, first served,\u201d said MIT author Vishrant Tripathi. &#8220;A video frame comes in, you process it. Another comes in, you process it. But if your task is time-sensitive, such as trying to detect where a moving object is, then all the old video frames are useless. What you want is the newest video frame.&#8221;<\/p>\n<p>In theory, an alternative approach of \u201clast in, first out\u201d could help keep data fresh. The concept is similar to a chef putting out entre\u00e9s one by one as they are hot off the line. If you want the freshest plate, you\u2019d want the last one that joined the queue. The same goes for data, if what you care about is the \u201cage of information,\u201d or the most up-to-date data.<\/p>\n<p>\u201cAge-of-information is a new metric for information freshness that considers latency from the perspective of the application,\u201d said Eytan Modiano of the Laboratory for Information and Decision Systems (LIDS). \u201cFor example, the freshness of information is important for an autonomous vehicle that relies on various sensor inputs. A sensor that measures the proximity to obstacles in order to avoid collision requires fresher information than a sensor measuring fuel levels.\u201d<\/p>\n<p>The team looked to prioritize age-of information, by incorporating a \u201clast in, first out\u201d protocol for multiple robots working together on time-sensitive tasks. They aimed to do so over conventional wireless networks, as Wi-Fi is pervasive and doesn\u2019t require bulky onboard communication hardware to access.<\/p>\n<p>However, wireless networks come with a big drawback: They are distributed in nature and do not prioritize receiving data from any one source. A wireless channel can then quickly clog up when multiple sources simultaneously send data. Even with a \u201clast in, first out\u201d protocol, data collisions would occur. In a time-sensitive exercise, the system would break down.<\/p>\n<h2>Data priority<\/h2>\n<p>As a solution, the team developed WiSwarm \u2014 a scheduling algorithm that can be run on a centralized computer and paired with any wireless network to manage multiple data streams and prioritize the freshest data.<\/p>\n<p>Rather than attempting to take in every data packet from every source at every moment in time, the algorithm determines which source in a network should send data next. That source (a drone or robot) would then observe a \u201clast in, first out\u201d protocol to send their freshest piece of data through the wireless network to a central processor.<\/p>\n<p>The algorithm determines which source should relay data next by assessing three parameters: a drone\u2019s general weight, or priority (for instance, a drone that is tracking a fast vehicle might have to update more frequently, and therefore would have higher priority over a drone tracking a slower vehicle); a drone\u2019s age of information, or how long it\u2019s been since a drone has sent an update; and a drone\u2019s channel reliability, or likelihood of successfully transmitting data.<\/p>\n<p>By multiplying these three parameters for each drone at any given time, the algorithm can schedule drones to report updates through a wireless network one at a time, without clogging the system, and in a way that provides the freshest data for successfully carrying out a time-sensitive task. <\/p>\n<p>The team tested out their algorithm with multiple mobility-tracking drones. They outfitted flying drones with a small camera and a basic Wi-Fi-enabled computer chip, which it used to continuously relay images to a central computer rather than using a bulky, onboard computing system. They programmed the drones to fly over and follow small vehicles moving randomly on the ground.<\/p>\n<p>When the team paired the network with its algorithm, the computer was able to receive the freshest images from the most relevant drones, which it used to then send commands back to the drones to keep them on the vehicle\u2019s track.<\/p>\n<p>When the researchers ran experiments with two drones, the method was able to relay data that was two times fresher, which resulted in six times better tracking, compared to when the two drones carried out the same experiment with Wi-Fi alone. When they expanded the system to five drones and five ground vehicles, Wi-Fi alone could not accommodate the heavier data traffic, and the drones quickly lost track of the ground vehicles. With WiSwarm, the network was better equipped and enabled all drones to keep tracking their respective vehicles.<\/p>\n<p>\u201cOurs is the first work to show that age-of-information can work for real robotics applications,\u201d said MIT author Ezra Tal.<\/p>\n<p>In the near future, cheap and nimble drones could work together and communicate over wireless networks to accomplish tasks such as inspecting buildings, agricultural fields, and wind and solar farms. Farther in the future, he sees the approach being essential for managing data streaming throughout smart cities.<\/p>\n<p>\u201cImagine self-driving cars come to an intersection that has a sensor that sees something around the corner,\u201d said MIT&#8217;s Sertac Karaman. \u201cWhich car should get that data first? It\u2019s a problem where timing and freshness of data matters.&#8221;<\/p>\n<p><em><strong>Editor&#8217;s Note:<\/strong> This article was republished from <a href=\"http:\/\/news.mit.edu\" rel=\"noopener\" target=\"_blank\">MIT News<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>MIT developed a method for tailoring any wireless network to handle a large load of time-sensitive data from various sources.<\/p>\n","protected":false},"author":90,"featured_media":565254,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rbr50_analysis":"","rbr50_state":"","rbr50_country":"","rbr50_description":"","rbr50_numemps":"","rbr50_text_taxonomy_radio":"","rbr50_text_taxonomy_select":"","rbr50_url":"","rbr50_yearfounded":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","ngg_post_thumbnail":0,"footnotes":""},"categories":[1753,2009,1383,1288,1401,1384],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>MIT &#039;traffic cop&#039; 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