Adaptive Semantic Path Planning for Safe Human Robot Interaction (HRI)
Advancements in the field of Artificial Intelligence (AI) has enhanced the Human Robot Interaction (HRI) / Collaboration to another level beyond conventional into dynamic day to day conditions and environments. As robotics transformation evolves and integrated across various fields, the parallel need for adaptation to unpredictable environment is the demand of the hour. According to the latest research on human robot collaboration, the findings state that: 1) spatial comfort of this interaction is not fixed and predominantly depends on the chaotic nature of the environmental conditions, and 2) robot’s course of approach as well as proximity around humans explicitly affects the perceived safety and triggers psychological uncertainty in humans. The proposed system presented in this paper tackles this challenge by an adaptive semantic path planning for safe and anxiety less human robot collaboration. The proposed system executes following operations: 1) maps unstructured natural language to geometric baseline threshold, 2) evaluating cluster index value to analyze the chaotic conditions of the environmental surroundings and based on this index, A* with Manhattan algorithm calculates shortest destination route and 3) to comply with behavioral safety and anxiety less human robot interaction, the robot route points are regularized using cubic spline transformer to ensure it is avoid of orthogonal, jerky route and replace it by continuous, curvature trajectory in order to address latest research thereby signing safe movements near humans. The simulation results indicate that the proposed framework successfully converts natural human commands into flexible trajectory that effectively satisfies safe HRI. The performance metrics evaluated validates that the proposed system undertakes predictable, secure route with low computational power and latency in contrast to heavy computational architectures that bring forth behavioral uncertainty in HRI.