Guihua Xia

Modified Scale Invariant Feature Transform in omnidirectional images

The Scale Invariant Feature Transform, SIFT, is invariant to image translation, scaling, rotation, and is partially invariant to illumination changes. But, the time of features extraction and matching is huge, and the number of features is much larger then that is needed. To reduce the number of features generated by SIFT as well as their extraction and matching time, a modified approach based sampling is proposed. Mean-Shift algorithm is used in this modified SIFT to search local extrema points actively in scale space to improve the efficiency. It is demonstrated that the features extracted by modified SIFT are uniformly distributed in space, the time of feature extraction and matching is reduced obviously and the feature matching is accurate.

Monte Carlo Localization of Mobile Robot with Modified SIFT

The Scale Invariant Feature Transform, SIFT, is invariant to image translation, scaling, rotation, and is partially invariant to illumination changes. But, the time of features extraction and matching is huge, and the number of features is much larger then that is needed. To reduce the number of features generated by SIFT as well as their extraction and matching time¿a modified approach based sampling is proposed. Mean-Shift algorithm is used in this modified SIFT to search local extrema points actively in scale space to improve the efficiency. The modified SIFT is used in Monte Carlo localization of mobile robots with omnidirectional sensor, it is demonstrated that the features extracted by modified SIFT are uniformly distributed in space, the time of feature extraction and matching is reduced obviously, and the mobile robots can localize itself accurately with a lower number of features.

Hybrid force/position control of industrial robotic manipulator based on Kalman filter

This paper proposes a hybrid force/position controller for industrial robotic manipulator based on Kalman filter. Firstly, the mathematical model of real contact force is built to estimate the actual contact force by applying Kalman filter using a force derivative to achieve the system state description. The estimated actual contact force is used to control the end-effector force as well as estimating the stiffness of environment. To refine the environment stiffness estimation the Recursive Least Square (RLS) technique has been employed. Owing to the fact that the general industrial manipulator only provides the position control mode, the position-based hybrid force/position control architecture is designed and realized by using the position tracking mode of the motion control card. The main advantages of the implemented controller is simplicity, computational efficiency and robustness to unknown environment, it is convenience for the general industrial manipulators. Besides, it lends itself for industrial manipulators in order to achieve compliant behavior and perform complex tasks. The proposed control structure is successfully validated by practical experiments. The results show that the controller has a satisfactory performance in term of force control and trajectory tracking and robustness to force/torque sensor measurement interferences.

Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control

Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve the sampling efficiency and decrease the required interactions during the learning process, this paper develops a data-efficient learning variable impedance control method that enables the industrial robots automatically learn to control the contact force in the unstructured environment. To this end, a Gaussian process model is learned as a faithful proxy of the system, which is then used to predict long-term state evolution for internal simulation, allowing for efficient strategy updates. The effects of model bias are reduced effectively by incorporating model uncertainty into long-term planning. Then the impedance profiles are regulated online according to the learned humanlike impedance strategy. In this way, the flexibility and adaptivity of the system could be enhanced. Both simulated and experimental tests have been performed on an industrial manipulator to verify the performance of the proposed method.