Differential Domestic Inherent Positioning System

Abstract

Path planning is critical in robotics as well as autonomous driving applications. This thesis provides a modified path planning planner algorithms by enhancing the performance of more than one algorithm. The proposed technique is based on dividing the domain of motion and solves the relative path in each division. Our results on synthetic maps show a dramatic reduction on processing time compared with the conventional algorithm.

 We explain and modified seven methods at absolute and relative mode

1. Path planning using probabilistic roadmap (PRM) method Explaining inputs parameters, outputs parameters and modified steps to execute method to get optimal results at both modes.

2. Path planning using bidirectional rapidly random trees (BI-RRT) method. Explaining inputs parameters, outputs parameters and modified steps to execute method to get optimal results at both modes

3. Path planning using rapidly random trees (RRT) method. Explaining inputs parameters, outputs parameters and modified steps to execute method to get optimal results at both modes.

4. Path planning using genetic method Explaining inputs parameters, outputs parameters and modified steps to execute method to get optimal results at both modes.

5. Path planning using artificial potential field method Explaining inputs parameters, outputs parameters and modified steps to execute method to get optimal results at both modes.

6. Path planning using fuzzy method Explaining inputs parameters, outputs parameters and modified steps to execute method to get optimal results at both modes.