Reducing the Dimension of the Configuration Space with Self Organizing Neural Networks

For robotics, especially industrial applications, it is crucial to reactively plan safe motions through efficient algorithms. Planning is more powerful in the configuration space than the task space. However, for robots with many degrees of freedom, this is challenging and computationally expensive. Sophisticated techniques for motion planning such as the Wavefront algorithm are limited by the high dimensionality of the configuration space, especially for robots with many degrees of freedom. For a neural implementation of the Wavefront algorithm in the configuration space, neurons represent discrete configurations and synapses are used for path planning. In order to decrease the complexity, we reduce the search space by pruning superfluous neurons and synapses. We present different models of self-organizing neural networks for this reduction. The approach takes real-life human motion data as input and creates a representation with reduced dimension. We compare six different neural network models and adapt the Wavefront algorithm to the different structures of the reduced output spaces. The method is backed up by an extensive evaluation of the reduced spaces, including their suitability for path planning by the Wavefront algorithm.