Real-Time Autonomous Robot Navigation by a Vector-Driven Methodology

Luo, Chaomin, Mohan Krishnan, and Mark Paulik

A novel Grossberg neural networks
approach associated with developed vector-driven autonomous
robot navigation is proposed in this research. The Grossberg
neural networks algorithm is employed to guide an autonomous robot to reach goal with obstacle avoidance motivated by Grossberg’s model for a biological neural system. As the robot plans its trajectory toward the goal, unreasonable path will be inevitably planned. A vector-based guidance paradigm is developed for guidance of the robot locally so as to plan more reasonable trajectories. The Grossberg’s neural network based scheme demonstrates that the algorithms avoid the issue of local minima in path planning. Both simulation and comparison
studies of an autonomous robot navigation demonstrate that the
proposed model is capable of planning more reasonable and
shorter collision-free paths in non-stationary and unstructured
environments compared with other approaches.