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.