General Hand Guidance Framework using Microsoft HoloLens
This work addresses the limitation of hand guidance being mostly restricted to collaborative robots, potentially making it accessible for industrial robots without sensor modifications.
The authors tackled the problem of enabling hand guidance for industrial robots without requiring built-in sensors, by using a Microsoft HoloLens for tracking and a registration algorithm to map hand movements to robot kinematics. They achieved a generalized hand guidance method that was successfully tested on a KUKA KR-5 industrial manipulator.
Hand guidance emerged from the safety requirements for collaborative robots, namely possessing joint-torque sensors. Since then it has proven to be a powerful tool for easy trajectory programming, allowing lay-users to reprogram robots intuitively. Going beyond, a robot can learn tasks by user demonstrations through kinesthetic teaching, enabling robots to generalise tasks and further reducing the need for reprogramming. However, hand guidance is still mostly relegated to collaborative robots. Here we propose a method that doesn't require any sensors on the robot or in the robot cell, by using a Microsoft HoloLens augmented reality head mounted display. We reference the robot using a registration algorithm to match the robot model to the spatial mesh. The in-built hand tracking and localisation capabilities are then used to calculate the position of the hands relative to the robot. By decomposing the hand movements into orthogonal rotations and propagating it down through the kinematic chain, we achieve a generalised hand guidance without the need to build a dynamic model of the robot itself. We tested our approach on a commonly used industrial manipulator, the KUKA KR-5.