VIRTUAL INSTRUMENTATION BASED SYSTEMS FOR REAL-TIME PATH PLANNING OF MOBILE ROBOTS USING BIO-INSPIRED NEURAL NETWORKS

Abstract

In this paper, novel virtual instrumentation based systems for real-time collision-free path planning and tracking control of mobile robots are proposed. The developed virtual instruments are computationally simple and efficient in comparison to other approaches, which act as a new soft-computing platform to implement a biologically-inspired neural network. This neural network is topologically arranged with only local lateral connections among neurons. The dynamics of each neuron is described by a shunting equation with both excitatory and inhibitory connections. The neural network requires no off-line training or on-line learning, which is capable of planning a comfortable trajectory to the target without suffering from neither the too close nor the too far problems. LabVIEW is chosen as the software platform to build the proposed virtual instrumentation systems, as it is one of the most important industrial platforms. We take the initiative to develop the first neuro-dynamic application in LabVIEW. The developed virtual instruments could be easily used as educational and research tools for studying various robot path planning and tracking situations that could be easily understood and analyzed step by step. The effectiveness and efficiency of the developed virtual instruments are demonstrated through simulation and comparison studies.