{"id":247280,"date":"2026-06-23T07:29:40","date_gmt":"2026-06-23T11:29:40","guid":{"rendered":"https:\/\/today.uconn.edu\/?p=247280"},"modified":"2026-06-18T13:04:45","modified_gmt":"2026-06-18T17:04:45","slug":"advancing-drone-and-robotics-research","status":"publish","type":"post","link":"https:\/\/today.uconn.edu\/2026\/06\/advancing-drone-and-robotics-research\/","title":{"rendered":"Advancing Drone and Robotics Research"},"content":{"rendered":"

Under the leadership of\u00a0George Bollas,\u00a0Associate\u00a0Dean for Research\u00a0at UConn\u2019s College of Engineering\u00a0and Director of\u00a0the\u00a0Pratt & Whitney Institute for Advanced Systems Engineering, UConn researchers are uniting\u00a0to\u00a0shape the future of drone technology.<\/span>\u00a0<\/span><\/p>\n

Bollas, Jonathan Shihao Ji,\u00a0and Shalabh\u00a0Gupta\u00a0are\u00a0working together\u00a0on two major initiatives. One is a proposed collaboration\u00a0between UConn, the United States Coast Guard Academy, and the United States Coast Guard Research & Development Center, designed to bolster the Coast Guard\u2019s mission capabilities with UConn engineering acumen.\u00a0<\/span>This partnership will pilot a research and development program focused on AI-enabled autonomous and swarm-based maritime drone operations, involving\u00a0engineering staff and students at both UConn and the USCGA, to\u00a0support\u00a0USCG missions such as search and rescue, maritime domain awareness, environmental monitoring and pollution response, intelligence gathering, and port and event security.<\/span>\u00a0<\/span><\/p>\n

The other initiative, a collaboration between UConn, RTX, Sikorsky, and the Connecticut Center for Advanced Technology (CCAT), seeks to boost the capabilities of multi-drone systems, allowing these vehicles to better learn from one another and their environment.<\/span><\/p>\n

Using AI algorithms, edge computing, and network and system integration, this project will apply to all applications of multi-drone systems \u2013 including\u00a0traffic management in major cities, large-scale facility\u00a0inspection, aerial photography, precision agriculture, and search & rescue.<\/span>\u00a0<\/span><\/p>\n

These UConn engineers are leading innovative work at the intersection of engineering, computer science, and systems design. Together, their research is helping redefine drones as intelligent collaborators capable of making decisions, adapting to uncertainty, and\u00a0operating\u00a0safely in dynamic settings.<\/span>\u00a0<\/span><\/p>\n

At the core of this\u00a0work\u00a0is a focus on autonomy. Traditional drones rely heavily on human operators, but these researchers are developing algorithms that allow drones to interpret their surroundings and act independently.\u00a0Their scholarly collaboration\u00a0leverages\u00a0their unique individual\u00a0expertise\u00a0in diverse subfields of engineering, including chemical, mechanical, and computing.<\/span>\u00a0<\/span><\/p>\n

George Bollas<\/span><\/b><\/a>\u00a0brings\u00a0expertise\u00a0in complex systems and energy processes, examining how drone technologies can integrate with broader infrastructure systems. His work considers how aerial platforms can support industrial operations, environmental sensing, and energy efficiency, opening new possibilities for drones in sectors like manufacturing and sustainability.<\/span>\u00a0<\/span><\/p>\n

Associate professor\u00a0<\/span>Jonathan Shihao Ji<\/span><\/b><\/a>\u00a0<\/span><\/i>directs the Intelligent Systems Lab,\u00a0which combines AI and robotics.\u00a0With his work exploring\u00a0how AI can be integrated into various robotic systems (including drones, robotic dogs, and robotic arms),\u00a0Ji\u2019s efforts are key for the multi-drone sensing initiative, which\u00a0could significantly improve\u00a0communication and sensing abilities in a drone swarm.<\/span>\u00a0<\/span><\/p>\n

An\u00a0AI-powered approach can help drones \u201ccollaborate with each other to detect objects, like vehicles, on the ground,\u201d Ji explains.\u00a0Drones in the field can have difficulty sensing objects when they are obscured by other objects or by weather conditions, but this\u00a0new approach\u00a0strengthens their ability to accurately \u201csee\u201d their environment and respond to challenging situations. This is\u00a0critical for applications like search-and-rescue missions or environmental monitoring across large areas.<\/span>\u00a0<\/span><\/p>\n

Associate professor\u00a0<\/span>Shalabh Gupta<\/a>\u00a0<\/span><\/b>is the director of UConn robotics engineering program and runs\u00a0the Laboratory of Intelligent Networked Systems (LINKS).\u00a0He\u00a0is focused on\u00a0combining AI and robotic\u00a0motion planning\u00a0to improve\u00a0the functioning of\u00a0drones\u00a0in real-life environments.\u00a0Gupta\u00a0contributes to the\u00a0perception,\u00a0diagnostics,\u00a0and autonomy\u00a0aspects\u00a0of the equation. His research focuses on\u00a0integrating\u00a0smart\u00a0perception\u00a0and planning\u00a0technologies\u00a0using\u00a0data-driven\u00a0learning approaches\u00a0that allow\u00a0multi-drone\u00a0systems\u00a0to detect anomalies,\u00a0gather situational\u00a0awareness\u00a0and plan time-risk\u00a0optimal\u00a0flight paths in real time\u00a0in\u00a0constricted environments. These capabilities are essential for applications such as\u00a0surveillance,\u00a0bridge\u00a0and\u00a0pipeline\u00a0inspections,\u00a0agriculture, manufacturing,\u00a0and other critical\u00a0infrastructure\u00a0systems\u00a0where safety and accuracy are paramount.<\/span>\u00a0<\/span><\/p>\n

He points out that\u00a0multi-robot collaborative teams, including\u00a0multi-drone teams, face unique engineering challenges.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cThings change very fast, and they need to be able to make real-time decisions to adapt,\u201d says Gupta. \u201cHow do those robots\u00a0perceive and\u00a0adapt in real-life situations?\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

Improving the answer to this question is the cornerstone of his research.\u00a0He focuses on resilience in multi-robot systems \u2013 for example, if the battery fails in one machine, “the others should take over and finish the job.\u201d<\/span>\u00a0<\/span><\/p>\n

\u201cThey can make a\u00a0team\u00a0optimal decision about who to help and what they should do,\u201d says Gupta.<\/span>\u00a0<\/span><\/p>\n

Another\u00a0project kicking off soon\u00a0at UConn,\u00a0\u201cTechnology-Assisted Culvert Inspections in Confined or Hazardous Areas<\/span>,\u201d is being\u00a0finalized\u00a0for award by the Connecticut Department of Transportation\u2019s (CTDOT) Research Unit<\/a>. This project is\u00a0led\u00a0by\u00a0engineering faculty\u00a0<\/span>Shinae Jang<\/span><\/b><\/a>,\u00a0<\/span>Kai Wang<\/span><\/b><\/a>,<\/span>\u00a0Alexandra Hain<\/a><\/span><\/b>,\u00a0<\/span>Yuan Hong<\/span><\/b><\/a>, and\u00a0<\/span>Shan Zuo<\/span><\/b><\/a>. This\u00a0effort\u00a0will\u00a0advance the use of drone and AI-assisted technologies for culvert inspection and infrastructure management.\u00a0<\/span>\u00a0<\/span><\/p>\n

The proposed work focuses on using drones and other robotic technologies to improve culvert inspections in confined, hazardous, and difficult-to-access environments. The research will evaluate unmanned aerial systems (UAS), LiDAR, sonar, AI-driven defect detection, and GPS-denied navigation technologies to create safer, faster, and more reliable inspection workflows for CTDOT. Pilot field inspections will be conducted to validate the effectiveness, accuracy, and operational feasibility of drone-assisted inspections under real-world conditions. The project will ultimately develop CTDOT-ready guidance, safety protocols, and integration plans that support long-term adoption of AI-integrated drone technologies for infrastructure inspection and asset management.<\/span>\u00a0<\/span><\/p>\n

What makes UConn\u2019s approach\u00a0to\u00a0drone research\u00a0especially powerful is\u00a0the collaboration\u00a0across disciplines. By combining\u00a0expertise\u00a0in sensing, control, artificial intelligence, and systems engineering, the team is building integrated drone systems that are greater than the sum of their parts. These efforts are supported by UConn\u2019s commitment to interdisciplinary research and innovation, including initiatives within robotics, intelligent systems, and advanced manufacturing.<\/span>\u00a0<\/span><\/p>\n

The potential impact of this work is far-reaching. Smarter drone systems could help first responders navigate disaster zones more effectively, enable more sustainable monitoring of natural resources, and improve the safety and efficiency of infrastructure maintenance. As these technologies continue to evolve, UConn researchers are ensuring they do so with reliability, scalability, and real-world impact in mind.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

UConn College of Engineering boasts state-of-the-art research capacity and top-tier talent<\/p>\n","protected":false},"author":175,"featured_media":247582,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"wds_primary_category":0,"wds_primary_series":0,"wds_primary_attribution":0,"footnotes":""},"categories":[1866,2711,2460,2076,2709,2235,2227],"tags":[],"magazine-issues":[],"coauthors":[2413],"class_list":["post-247280","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-engr","category-emerging-technology","category-faculty","category-research","category-security","category-today-homepage","category-uconn-edu-homepage"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-06-30 12:23:58","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/247280","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/users\/175"}],"replies":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/comments?post=247280"}],"version-history":[{"count":3,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/247280\/revisions"}],"predecessor-version":[{"id":247590,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/247280\/revisions\/247590"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/media\/247582"}],"wp:attachment":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/media?parent=247280"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/categories?post=247280"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/tags?post=247280"},{"taxonomy":"magazine-issue","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/magazine-issues?post=247280"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/coauthors?post=247280"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}