首页|Studies from Fluminense Federal University Further Understanding of Robotics (He terogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Kn own Dynamic Environments)
Studies from Fluminense Federal University Further Understanding of Robotics (He terogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Kn own Dynamic Environments)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on robotics is now availab le. According to news reporting originating from Niteroi, Brazil, by NewsRx corr espondents, research stated, "This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAV s) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios ." Financial supporters for this research include Cefet/rj, The Federal Brazilian R esearch Agencies Capes; Cnpq; Rio De Janeiro Research Agency, Faperj. Our news reporters obtained a quote from the research from Fluminense Federal Un iversity: "This paper defines specific roles for the UAVs and UGV within the fra mework to address challenges like partially known terrains and dynamic obstacles . The UAVs are focused on aerial inspections and mapping, while UGV conducts gro und-level inspections. In addition, the UAVs can return and land at the UGV base , in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Cov erage Path Planning (CPP) algorithm that dynamically adapts paths to avoid colli sions and ensure efficient coverage. The Wavefront algorithm was selected for th e two-dimensional offline CPP. All robots must follow a predefined path generate d by the offline CPP." According to the news editors, the research concluded: "The study also integrate s advanced technologies like Neural Networks (NN) and Deep Reinforcement Learnin g (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS ) and Gazebo platforms were conducted to validate the approach considering speci fic real-world situations, that is, an electrical substation, in order to demons trate its functionality in addressing challenges in dynamic environments and adv ancing the field of autonomous robots."
Fluminense Federal UniversityNiteroiBrazilSouth AmericaEmerging TechnologiesMachine LearningNano-robotRobo tRobotics
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