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The purpose of this paper is to extend the usage of stroke gestures in manipulation tasks to make the interaction between human and robot more efficient.

In this paper, a set of stroke gestures is designed for typical manipulation tasks. A gesture recognition and parameter extraction system is proposed to exploit the information in stroke gestures drawn by the users.

The results show that the designed gesture recognition subsystem can reach a recognition accuracy of 99.00 per cent. The parameter extraction subsystem can successfully extract parameters needed for typical manipulation tasks with a success rate about 86.30 per cent. The system shows an acceptable performance in the experiments.

Using stroke gesture in manipulation tasks can make the transmission of human intentions to the robots more efficient. The proposed gesture recognition subsystem is based on convolutional neural network which is robust to different input. The parameter extraction subsystem can extract the spatial information encoded in stroke gestures.

The author designs stroke gestures for manipulation tasks which is an extension of the usage of stroke gestures. The proposed gesture recognition and parameter extraction system can make use of stroke gestures to get the type of the task and important parameters for the task simultaneously.

The purpose of this paper is to enable autonomous door-opening with unknown geometrical constraints. Door-opening is a common action needed for mobile manipulators to perform rescue operation. However, it remains difficult for them to handle it in real rescue environments. The major difficulties of rescue manipulation involve contradiction between unknown geometrical constraints and limited sensors because of extreme physical constraints.

A method for estimating the unknown door geometrical parameters using coordinate transformation of the end-effector with visual teleoperation assists is proposed. A trajectory planning algorithm is developed using geometrical parameters from the proposed method.

The relevant experiments are also conducted using a manipulator suited to extreme physical constraints to open a real door with a locked latch and unknown geometrical parameters, which demonstrates the validity and efficiency of the proposed approach.

This is a novel method for estimating the unknown door geometrical parameters with coordinate transformation of the end-effector through visual teleoperation assists.