In this paper, we present an information entropybased viewpoint-planning approach for reconstruction of freeform surfaces of three-dimensional objects. To achieve the reconstruction, the object is first sliced into a series of cross section curves, with each curve to be reconstructed by a closed B-spline curve. In the framework of Bayesian statistics, we propose an improved Bayesian information criterion (BIC) for determining the B-spline model complexity. Then, we analyze the uncertainty of the model using entropy as the measurement. Based on this analysis, we predict the information gain for each cross section curve for the next measurement. After predicting the information gain of each curve, we obtain the information change for all the B-spline models. This information gain is then mapped into the view space. The viewpoint that contains maximal information gain about the object is selected as the next best view. Experimental results show successful implementation of our view planning method for digitization and reconstruction of freeform objects.

In this paper we present an uncertainty-driven viewpoint planning approach for measwement and digitalization of free form 3D objects. The object surface is first decomposed into a number of cross section curves, and each curve is reconstructed by a closed B-spline curve. Then, we analyze the uncertainty of the B-spline model using entropy as the measurement of uncertainty of the Bspline model. Based on this, we predict the information gain for each cross section curve for candidate views. After predicting the information gain of all the B-spline models, we can map the information gain of these Bspline models into the view space. The viewpoint that contains maximal information gain about the object is selected as the Next Best View. Experimental results are given in the digitization and reconstruction of freeform objects using our view planning method.

Since the attitude angle between the gripper and the roller of the robot arm is not adjusted during the autonomous obstacle surmounting process of the substation inspection robot, and the obstacle surmounting route found is not optimal, the path of the substation inspection robot for autonomous obstacle surmounting is not the optimal result. Therefore, the autonomous obstacle surmounting method of the substation inspection robot based on locust optimization algorithm is proposed. After calculating the vertical distance between the overhead line and the working ground and adjusting the attitude angle between the gripper and the roller of the manipulator, a two-dimensional Gaussian scale space is constructed to extract the key feature points of the obstacle image in the space. According to the feature extraction results, combined with the locust optimization algorithm, the autonomous obstacle path of the substation inspection robot is planned to obtain the most superior obstacle path. In the experiment, the obstacle surmounting effect of the proposed method is verified. The experimental results show that the error rate of the optimal solution is low, and the path planning performance is good when the proposed method is used for robot autonomous obstacle crossing control.

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