Comparative study of neural networks in path planning for catering robots

Abstract

Neural Networks (NN) have been the forefront of growth in recent years due to their variety, the opportunities they provide and most importantly their dynamic nature. A control system for catering robots for path planning is proposed with the help of neural networks as a comparative study. Various parameters such as training time, performance of the network, forecasted distance are considered after iterating to obtain the optimal dataset using Probabilistic Roadmap (PRM) algorithm. Approximately 36% improvement in forecasted distance was obtained using neural networks when compared to the traditional PRM algorithm.