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To demonstrate the performance of the proposed framework, mobile robot visual tracking experiments are carried out.
For non-roboticists, the standard paradigm for robot visual programming is based on the selection of behavioral blocks, followed by their connection using communication links.
In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems.
In this paper we aim at improving the robot visual perception capabilities in a natural, human-like fashion, with a very limited amount of constraints to the acquisition scenario.
This article presents an algorithm for moving object detection (MOD) in robot visual simultaneous localization and mapping (SLAM).
In this research, we developed an algorithm for detection and tracking of moving objects to improve the robustness of robot visual SLAM system.
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As the video notes, the resolution (both temporal and spatial) of current robots' visual systems is very limited due to data bandwidth and CPU limitations.
I start by discussing an algorithm for fast visual-inertial navigation, which estimates the motion of the robot from visual and inertial cues, and demonstrate its use for agile flight of micro aerial vehicles.
In the experiments, the operator directly controls the (simulated) robot using visual feedback to achieve robust grasping with the robot.
Mr Holland will rely on clues from the robot's visual systems to search for signs of consciousness - in much the same way that the human consciousness is dependent on our visual systems.
Finally, the experiment results demonstrate the effectiveness of the fuzzy-model-based control design for the mobile robot with visual odometry.
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