科研之友
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摘要
Image-based visual servoing (IBVS) can reach a desired position for a relatively stationary target using continuous visual feedback. Proper feature extraction and appropriate servoing control laws are essential to performance for IBVS. IBVS control can be interrupted or interfered abruptly if no features are extracted when the observed object is occluded. To address the problem of missing feature points in current images during a visual navigation task, a homography method that uses a priori visual information is proposed to predict all of the missing feature points and to ensure the execution of IBVS. The mixture parameter for the image Jacobian matrix can also affect the control of IBVS. The settings for the mixture parameter are heuristic so there is no a systematic approach for most IBVS applications. An adaptive control approach is proposed to determine the mixture parameter. The proposed method uses a reinforcement learning (RL) method to adaptively adjust the mixture parameter during the robot movement, which allows more efficient control than a constant parameter. A logarithmic interval state-space partition for RL is used to ensure efficient learning. The integrated visual servoing control system is validated by several experiments that involve wheeled mobile robots reaching a target with a desired configuration. The results for simulation and experiment demonstrate that the proposed method has a faster convergence rate than other methods.
