An incremental unsupervised learning based trajectory controller for a 4 wheeled skid steer mobile robot

This paper proposes a trajectory controller for a 4-wheel skid steering mobile robot designed for use in an oil palm plantation. The nature of the working environment requires adaptive control to eliminate noise and to learn necessary variations on-the-go. The proposed control system is based on the Enhanced Self Organizing Incremental Neural Network (ESOINN), and is able to produce exceptional trajectory control without the use of a kinematic / dynamic model of the mobile robot by training the network with measured trajectory data as well as simulated data by incremental learning. Our simulation results show that the ESOINN is able to adapt to new training samples and errors have been reduced after only a few iterations of incremental learning. The RMSE error of the output of the initial network was reduced by almost 50% after 3 stages of incremental learning. When comparing training times, ESOINN had a much faster computation time with each consecutive incremental learning instance as compared to other non-incremental methods such as self-organizing maps (SOM), K-means clustering and an adaptive Neural Network. In addition, ESOINN produced improved performance after each consecutive stage of learning, proving its reliability, unlike the other mentioned methods, which gave varied performance during each stage.