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In the field of robotics, Markov decision process (MDP) is a popular approach for robot motion planning.
This paper introduces a novel neural network-based reinforcement learning approach for robot gaze control.
Visual Simultaneous Localization and Mapping (SLAM) based on RGB-D data has developed as a fundamental approach for robot perception over the past decades.
Involvement of gradually refined dynamic model into control design, as well as manipulator design, has become a dominating approach for robot manufacturer to pursue competitive performance and keep technological leadership.
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In this study, an evolutionary path-planning approach for robot-assisted handling of sheet metal parts in bending is firstly proposed and implemented.
Here we have presented an accommodated approach for multi robot exploration, mapping and searching suitable for large teams of robot, function in unknown environment which lacking from an existing supporting communications framework.
In this paper, we propose a novel approach for mobile robot localization from images.
This paper presents an approach for safe robot controlling using the Intelligent Space.
In this paper we present a Guided Autowave Pulse Coupled Neural Network (GAPCNN) approach for mobile robot path planning.
This paper proposes a spatial attention approach for a robot manipulator with an eye-in-hand Kinect range sensor.
Gohring et al. [5] present a novel approach for multi robot object tracking and self localization using spatial relations of the target with respect to stationary objects.
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