Robot Path Planning System in Populated Dynamic Environment using Dynamic Wave Expansion Neural Network Techniques

Abstract

In heavily crowded dynamic situations robot navigation faces numerous problems. The path planning approaches for navigation robots in dense situations are presented in this work. In terms of planning and temperament, the route planning of independent mobile robots in the navigation architecture is separated into local path planning and global path planning. About the scope, preparation, and capacity for the implementation, we have presented in this article a neural network model that implements a dynamic version of the route length transformation process (used a stationary domain for path planning). With this new version of path generation extremely dynamic environment is possible. The neural network operates in discrete time, is locally linked, and hence is extremely fast. The planning procedure does not need any early findings of the state of the world. The neural-activity landscape, which creates a dynamically updated topographic map across a distribution representing the robot's arrangement space, is used to generate paths. The network dynamics ensure local adaptations and give stringent criteria for selecting a robot's future route step. With a standards, planned pathways are likely to be optimum, because of these principles. The efficiency of the suggested model is demonstrated by simulating the results inside a set of tests for diverse dynamical conditions.