Hand GestureBased OnLine Programming of Industrial Robot Manipulators Robots are widely used in industrial manufacturing processes and play an important role in the enhancement of industrial organizations productivity. One of the major issues that engineers are facing is that, current programming methods are too timeconsuming and they lack of intuitiveness use by human users. However, the latest advances in the field of sensors, let manufacturers to develop and produce devices that allow humans to interact with machines in a more intuitive way, reducing the need of additional complex software components, and hence, the required time to establish the aforementioned humanmachine interactions. This research work presents an approach for gesturebased online programming of industrial robot manipulators. This is achieved by utilizing a combination of devices with a set of integrated, costeffective visual and bending sensors, in order to precisely track the useru0027s hand position and gestures at system runtime. This continuous tracking allows the robot manipulator to mimic the operatorxe2x80x99s hand motion. In addition, desired paths performed by a human with expertise on task execution, are translated into robot targets, composing a new robot path, and are stored for later use. Such path can be modified to fit into different robot manufacturers, programming language. Further steps of the presented approach will include the possibility of path optimization by the industrial manipulator itself.

General Hand Guidance Framework using Microsoft HoloLens Hand guidance emerged from the safety requirements for collaborative robots, namely possessing jointtorque sensors. Since then it has proven to be a powerful tool for easy trajectory programming, allowing layusers 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 does not 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 inbuilt 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 KR5.