In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

The advent of Industry 4.0 (I4.0) has pushed technology beyond its physical limits, making the process prone to errors and poorer performance. Whether it is about smart manufacturing where mass customization is envisaged, or collaborative human–robot engineering systems, the pyramid of process operation has changed to a matrix form and control is the backbone of all process elements. The paper gives a concise guideline as to how, when, where, and what to apply when it comes to choosing the most suitable control strategy as a function of multi-parameter objective optimization. Both proportional-integral-derivative (PID) and model predictive control (MPC) control are addressed in this context.

The visual servoing architectures with an eye-in-hand configuration, composed of a 6-d.o.f. manipulator robot and a video camera, are categorised as high-coupled and nonlinear systems. For this reason, in order to have a stable and convergent behaviour, advanced techniques like predictive control are needed. These types of techniques can solve the potential problems that can appear in classical (image-based visual servoing) while different type of constraints for visual servoing system can be fulfilled easier. Three different visual predictive control architectures based on image moments are proposed. The model for the image moments predictor used in the first architecture is an extension of a local model point features predictor. The second predictive approach uses an image moment-based predictor to describe the future behaviour of the system over a certain camera velocity scenario directly in the image moments space. In order to have a faster convergence and in the same time a desired behaviour of the servoing system, a reference trajectory is employed. This represents the main contribution of the last predictive control technique proposed in this study. A Matlab simulator was developed and all the visual servoing architectures described in the study were implemented. The simulation results showed that all the proposed visual predictive control approaches are stable and convergent when dealing with visual servoing tasks, but the best performances were assigned to the reference trajectory-based image moments architecture.

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology.

This paper describes the process of identification and closed-loop control of an Parrot AR.Drone Unmanned Aerial Vehicle (UAV) as well as a path following application based on IMC position controllers. The research issue is to achieve position control of the AR.Drone quadrotor movement via its on-board sensory equipment and external webcam video stream. Firstly, transfer functions are detailed for pitch and altitude movements and a comparison is made between implemented PID and IMC controller performance for both simulation and practice. Furthermore, using IMC controllers, a path following application exhibits controller behavior from a practical point of view. It is concluded that the dynamic model and the controllers implemented on the quadrotor can serve as a reliable basis for more advanced applications.