Human Aware Robot Motion Planning using RRT Algorithm in Industry4.0 Environment

Abstract

This work has been carried out to implement the Rapidly exploring Random Tree (RRT) Algorithm for designing a path in a 2D workspace cluttered with different human activities perceived as obstacles of different densities in a collaborative human-robot search operation-based Industry 4.0 environment. RRT based learning is an autonomous path planning algorithm suitable for perceiving environments with differential and non-holonomic constraints. Paths have been generated between an initial point, START to destination point, GOAL amidst uncertain temporal objects met when they are near and far. The algorithm starts to expand the search tree from the START node by randomly exploring the workspace until the GOAL is reached in the presence of these obstacles. The solution is a locus of points in the workspace between source and destination. This has been done for sparsely distributed human activity as obstacles encapsulating waypoint and lower and higher obstacle densities as observed in a real collaborative search environment. The generated paths were evaluated for closeness and impact of different kinds of dynamic obstacle configurations in a real experimental test bed.