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In the second method, the robot is equipped with sensors that measure the working environment.
To test the hand gesture recognition method, the robot movement control system was built.
Using our method, the robot can not reach an edge of the ZMP's support polygon with a fast whole-body movement.
Experimental results showed that using our control method, the robot is able to adapt its spine motions in real-time to allow stable walking.
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Our method allows the robot to precisely navigate on the path by using DCS.
The method allows the robot to adjust the welding path designed from the CAD model to the actual workpiece.
An estimation method of the robot's heading direction using ambient magnetic field has been proposed [19].
In contrast, our proposed method allows the robot to stably navigate in dynamic outdoor environments even if LIDAR readings are used.
Results from several simulations show that the proposed method enables the robot to find an optimal trajectory from any given state towards a predefined target.
As shown in simulation and experimental results, the proposed method allows the robot to smoothly and effectively navigate through cluttered environments such as dense forests.
Experimental results show that the proposed method enables the robot to catch its target and to perform locomotion from any initial state.
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