首页|Studies from University of Djelfa Describe New Findings in Robotics (Robotic Vis ual-based Navigation Structures Using Lucas-kanade and Horn-schunck Algorithms o f Optical Flow)

Studies from University of Djelfa Describe New Findings in Robotics (Robotic Vis ual-based Navigation Structures Using Lucas-kanade and Horn-schunck Algorithms o f Optical Flow)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Robotics. Acc ording to news reporting out of Djelfa, Algeria, by NewsRx editors, research sta ted, "This paper aims to present vision-based navigation structures for a wheele d mobile robot using optical flow techniques. The two algorithms of the differen tial approach are examined and investigated for visual motion in unknown static and dynamic indoor environments." Our news journalists obtained a quote from the research from the University of D jelfa, "Horn-Schunck (HS) and Lucas-Kanade (LK) algorithms of the optical flow ( OF) technique are employed to extract information about the environment surround ing the controlled robot by an installed color camera on the robot platform. Obs tacles and objects are identified and detected based on image processing and vid eo acquisition steps for the different tasks of mobile robots: navigation of one robot with static obstacle avoidance, navigation with dynamic obstacle avoidanc e, and multi-robot navigation with a static obstacle. The proposed control struc tures are based on motion estimation and decision mechanisms that use the necess ary measured variables calculated by optical flow algorithms to carry out the ap propriate steering actions to guide autonomously the robot in its workspace. The efficiency of the proposed control structures is tested in 2D and 3D environmen ts using the Virtual Reality Modeling Language (VRML) Toolbox of Matlab."

DjelfaAlgeriaAlgorithmsEmerging Te chnologiesMachine LearningNano-robotRobotRoboticsRobotsUniversity of Djelfa

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.19)