Immersive Robot Programming Interface for Human-Guided Automation and Randomized Path Planning

TL;DR

This paper presents an augmented reality interface for online robot programming that incorporates human guidance to improve randomized path planning, demonstrating enhanced accuracy and user interaction in robotic manipulation tasks.

Contribution

The study introduces a novel AR interface integrating human input with randomized path planning algorithms like RRT* and SLERP, enhancing robot programming and environmental adaptability.

Findings

Human-in-the-loop improves path selection accuracy

AR interface enables intuitive robot programming

Validated on a 7-DOF manipulator with positive results

Abstract

Researchers are exploring Augmented Reality (AR) interfaces for online robot programming to streamline automation and user interaction in variable manufacturing environments. This study introduces an AR interface for online programming and data visualization that integrates the human in the randomized robot path planning, reducing the inherent randomness of the methods with human intervention. The interface uses holographic items which correspond to physical elements to interact with a redundant manipulator. Utilizing Rapidly Random Tree Star (RRT*) and Spherical Linear Interpolation (SLERP) algorithms, the interface achieves end-effector s progression through collision-free path with smooth rotation. Next, Sequential Quadratic Programming (SQP) achieve robot s configurations for this progression. The platform executes the RRT* algorithm in a loop, with each iteration independently exploring the shortest path through random sampling, leading to variations in the optimized paths produced. These paths are then demonstrated to AR users, who select the most appropriate path based on the environmental context and their intuition. The accuracy and effectiveness of the interface are validated through its implementation and testing with a seven Degree-OF-Freedom (DOF) manipulator, indicating its potential to advance current practices in robot programming. The validation of this paper include two implementations demonstrating the value of human-in-the-loop and context awareness in robotics.