Optimizing Mathematical Morphology for Image Segmentation and Vision-based Path Planning in Robotic Environments
A. Francisco, Mar Pujol López, Ramón Rizo Aldeguer · 2007
In general terms, the path planning process for suitable morphological gradient threshold. To do this, global morphological operators have been used to compute the gradient and the Laplacian and, after a proper binarization, the distance between the ideal segmentation and the MGT segmentation has been computed. As a consequence, the gradient threshold with the lowest distance has been selected as the optimal threshold value. Experimental results show that our model is fast and robust and could be applied for real-time imaging. As a future work, to fully appreciate the implications of incorporating a path planner into a robot system it is necessary to consider a real robot system. The use of simulations can give a good idea of the ability to solve the basic problem but it is also necessary to consider how the planner will receive input data and how the output path will be used to generate a trajectory and be implemented by a physical robot. This will make possible a more accurate designing method so that the robot internal hardware and software could be efficiently implemented. Finally, the results of our research could be extended to object classification and recognition. It would be also an interesting task to consider new simulation experiments with different environments, such as image sequences obtained from a camera placed in a robot platform, where real-time constraints have a great influence a mobile robot is strongly influenced by the precision of the acquisition process. Thus, it can be modified both by the quality of the information obtained from the environment, and the attributes of the system and the environment in which it works. In this chapter, we have developed a proposal of a model for the generation of a map in unknown environments. To do this, we have described a path planning technique for autonomous robots that uses morphological filtering. In this method, some high security paths for a robot to follow are computed; the experimentation shows that the prototype is robust and can be applied in real time for many robotic applications, since it is a very quick algorithm to compute free paths with high probability of no collision.