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Past projects<\/h2>\n
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Spring 2025<\/strong><\/p>\n Photo Album: link<\/a><\/p>\n Early Wildfire Detection Drone (8th Place out of 53 teams)<\/strong><\/p>\n Abstract<\/strong>: Wildfires pose escalating threats to human life, ecosystems, and infrastructure. This project presents a low-cost, autonomous drone system for early wildfire detection and rapid response. In contrast to conventional two-drone strategies, where one drone performs high-altitude surveillance and another conducts a low-altitude inspection, our solution combines both functions into a single platform. Equipped with a thermal camera and powered by onboard processing via the Jetson Orin Nano, the drone uses a YOLOv5-based machine-learning model trained specifically on low-resolution thermal imagery to detect fire hotspots in real-time. The proposed approach seeks to minimize response time, reduce system complexity, and support scalable wildfire monitoring in remote or resource-limited regions.<\/p>\n Team members<\/strong>: Zakria Abdelaziz, Amar Abualhassan, Ahmed Alnadi, Haseeb Elkhouga<\/p>\n Poster<\/strong>: SP25-10 Expo Poster_2025<\/a><\/p>\n Spring 2024<\/strong><\/p>\n Photo Album: link<\/a><\/p>\n RailVision: Overgrowth Detection Drone (3rd Place out of 52 teams)<\/strong><\/p>\n Abstract<\/strong>: Every year, billions of dollars are spent towards railroad maintenance, with vegetation overgrowth maintenance being a prominent issue. Vegetation overgrowth creates fire hazards, infrastructure damage, and lead to many negative economic and environmental impacts. To address this problem, we built an autonomous VTOL drone using an energy efficient 4+1 quadplane configuration. Utilizing our image classifying CNN (RailNet), our quadplane autonomously flies alongside rail lines, detecting vegetation overgrowth in real time. Through RailVision, we aim to contribute to the safety of maintenance workers, address the inefficiencies of current overgrowth inspections, increase the lifespan of railroad infrastructure, and make leaps toward preventing wildfires to better conserve our environment.<\/p>\n Team members<\/strong>: Osmin Nolasco, James Sullivan, Steeve Cantave, Dhruv Patel<\/p>\n Poster: RailVisionPoster<\/a><\/p>\n <\/p>\n Human Motion Estimation for Interactive Rehabilitation (8th Place out of 52 teams)<\/strong><\/p>\n Abstract<\/strong>: Sarcopenia is a musculoskeletal condition characterized by a loss of muscle and strength that affects a large portion of the elderly population globally. The condition can only be treated with physical therapy. However, not everyone who could benefit from PT is able to use it, either due to cost or location. The goal of our project is to provide a low-cost, at-home alternative to PT, using small integrated internal measurement units (IMUs) to track limb movements using inverse kinematics. This is paired with our software, which guides the user through different therapeutic exercises based on their goal, and gives them feedback on their performance. The results, although preliminary, indicate that this method can help patients reduce the effects of sarcopenia, and provide a way for progress to be tracked over time.<\/p>\n Team members<\/strong>:Ronan John, Daniel Gameiro, Marco Garcia-Palma<\/p>\n Poster<\/strong>: HumanMotionEstimationPoster<\/a><\/p>\n <\/p>\n Spring 2023: The LanternPredator<\/strong> (6th Place <\/strong>out of 71 teams<\/strong>)<\/p>\n Abstract<\/strong>: Lanternflies are an invasive pest species that can cause significant economic damage to agriculture by affecting plants and crops and disrupting the balance of natural ecosystems. These insects have a fast reproduction cycle, can withstand high-temperature variations, and have no natural predators in the US, making it very difficult to control their spread. Motivated by this environmental issue, we designed the LanternPredator, an autonomous pest control robot to help control the population growth of lanternflies. The proposed solution integrates machine learning algorithms for detecting the right insect species, an acoustic stimulus to attract the insects to a zap trap, and sonar for autonomous navigation.<\/p>\n Team members<\/strong>: David Banyamin, Wei Gou, Mark Rezk, Wictor Fedorowiat<\/p>\n Poster<\/strong>: LanternPredator<\/a><\/p>\n Pics<\/strong>: Photo Gallery<\/a><\/p>\n <\/p>\n Fall 2022: Navigation Implementation of Industrial Vehicles<\/strong><\/p>\n Abstract: Smart warehousing is the process of automating all the activities performed in a regular warehouse where raw materials and manufactured goods are stored. Smart warehousing has several advantages, from reducing operating costs and processing time to reducing human safety risks and optimizing physical storage space. The primary goal of our project is to develop an autonomous vehicle capable of navigating in a warehouse environment following a specific path. This research seeks to boost productivity in industrial settings by minimizing the wasteful movement of robotic vehicles, resulting in less time lost. This can open the door to a more general setting where a swarm of cooperative robots can fully automate the warehouse.\u00a0<\/span><\/p>\n Poster_Fall_2022<\/a><\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":"![\"\"](\"http:\/\/sites.rutgers.edu\/swarm-intelligence-lab\/wp-content\/uploads\/sites\/862\/2025\/06\/Screen-Shot-2025-06-26-at-8.31.25-AM.png\")
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