To Örebro University

Humans and mobile robots while sharing the same work areas require a high level of safety especially at possible intersections of trajectories. An issue of the human-robot navigation is the computation of the intersection point in the presence of noisy measurements or fuzzy information. For Gaussian distributions of positions/orientations (inputs) of robot and human agent and their parameters the corresponding parameters at the intersections (outputs) are computed by analytical and fuzzy methods.This is done both for the static and the dynamic case using Kalman filters for robot/human positions and orientations and thus for the estimation of the intersection positions. For the overdetermined case (6 inputs, 2 outputs) a so-called ’energetic’ approach is used for the estimation of the point of intersection. The inverse task is discussed, specifying the parameters of the output distributions and looking for the parameters of the input distributions. For larger standard deviations (stds) mixed Gaussian models are suggested as approximation of non-Gaussian distributions.

Safety, legibility and efficiency are essential for autonomous mobile robots that interact with humans. A key factor in this respect is bi-directional communication of navigation intent, which we focus on in this article with a particular view on industrial logistic applications. In the direction robot-to-human, we study how a robot can communicate its navigation intent using Spatial Augmented Reality (SAR) such that humans can intuitively understand the robot's intention and feel safe in the vicinity of robots. We conducted experiments with an autonomous forklift that projects various patterns on the shared floor space to convey its navigation intentions. We analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift and carried out stimulated recall interviews (SRI) in order to identify desirable features for projection of robot intentions. In the direction human-to-robot, we argue that robots in human co-habited environments need human-aware task and motion planning to support safety and efficiency, ideally responding to people's motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond what can be inferred from the trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. In this work, we investigate the possibility of human-to-robot implicit intention transference solely from eye gaze data and evaluate how the observed eye gaze patterns of the participants relate to their navigation decisions. We again analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift for clues that could reveal direction intent. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human gaze for early obstacle avoidance.

This study introduces a fuzzy based optimal state estimation approach. The new method is based on two principles: Adaptive Unscented Kalman filter, and Fuzzy Adaptive Grasshopper Optimization Algorithm. The approach is designed for the optimization of an adaptive Unscented Kalman Filter. To find the optimal parameters for the filter, a fuzzy based evolutionary algorithm, named Fuzzy Adaptive Grasshopper Optimization Algorithm, is developed where its efficiency is verified by application to different benchmark functions. The proposed optimal adaptive unscented Kalman filter is applied to two nonlinear systems: a robotic manipulator, and a servo-hydraulic system. Different simulation tests are conducted to verify the performance of the filter. The results of simulations are presented and compared with a previous version of the unscented Kalman filter. For a realistic test, the proposed filter is applied on the practical servo-hydraulic system. Practical results are discussed, and presented results approve the capability of the presented method for practical applications.

A safe and synchronized interaction between human agents and robots in shared areas requires both long distance prediction of their motions and an appropriate control policy for short distance reaction. In this connection recognition of mutual intentions in the prediction phase is crucial to improve the performance of short distance control.We suggest an approach for short distance control inwhich the expected human movements relative to the robot are being summarized in a so-called “compass dial” from which fuzzy control rules for the robot’s reactions are derived. To predict possible collisions between robot and human at the earliest possible time, the travel times to predicted human-robot intersections are calculated and fed into a hybrid controller for collision avoidance. By applying the method of velocity obstacles, the relation between a change in robot’s motion direction and its velocity during an interaction is optimized and a combination with fuzzy expert rules is used for a safe obstacle avoidance. For a prediction of human intentions to move to certain goals pedestrian tracks are modeled by fuzzy clustering, and trajectories of human and robot agents are extrapolated to avoid collisions at intersections. Examples with both simulated and real data show the applicability of the presented methods and the high performance of the results.

In this paper the intersection problem in humanrobot systems with respect to noisy information is discussed. The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the intersections of trajectories. We discuss the intersection problem with respect to noisy information on the basis of an analytic geometrical model and its TS fuzzy version. The transmission of a 2-dimensional Gaussian noise signal, in particular information on human and robot orientations, through a non-linear static system and its fuzzy version, will be described. We discuss the problem: Given the parameters of the input distributions, find the parameters of the output distributions.

In this study, a new integral non-singular terminal sliding mode control method for nonlinear systems is introduced. The proposed controller is designed by defining a new sliding surface with an additional integral part. This new manifold is first introduced into the second-order system and then expanded to nth-order systems. The stability of the control system is demonstrated for both second-order and nth-order systems by using the Lyapunov stability theory. The proposed controller is applied to a robotic manipulator as a case study for second-order systems, and a servo-hydraulic system as a case study for third-order systems. The results are presented and discussed.

The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the crossings of the trajectories of humans and robots. We discuss the intersection calculation and its fuzzy version in the context of human-robot navigation with respect to noise information. Based on known parameters of the Gaussian input distributions at the orientations of human and robot the parameters of the output distributions at the intersection are to be found by analytical and fuzzy calculation. Furthermore the inverse task is discussed where the parameters of the output distributions are given and the parameters of the input distributions are searched. For larger standard deviations of the orientation signals we suggest mixed Gaussian models as approximation of nonlinear distributions.

Robots in human co-habited environments need human-aware task and motion planning, ideally responding to people’s motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. This paper investigates the possibility of human-to-robot implicit intention transference solely from eye gaze data.  We present experiments in which humans wearing eye-tracking glasses encountered a small forklift truck under various conditions. We evaluate how the observed eye gaze patterns of the participants related to their navigation decisions. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human eye gaze for early obstacle avoidance.

The interaction between humans and mobile robots in shared areas requires adequate control for both humans and robots.The online path planning of the robot depending on the estimated or intended movement of the person is crucial for the obstacle avoidance and close cooperation between them. The velocity obstacles method and its fuzzification optimizes the relationship between the velocities of a robot and a human agent during the interaction. In order to find the estimated intersection between robot and human in the case of positions/orientations disturbed by noise, analytical and fuzzified versions are presented. The orientation of a person is estimated by eye tracking, with the help of which the intersection area is calculated. Eye tracking leads to clusters of fixations that are condensed into cluster centers by fuzzy-time clustering to detect the intention and attention of humans.

The study of the interaction between autonomous robots and human agents in common working areas is an emerging field of research. Main points thereby are human safety, system stability, performance and optimality of the whole interaction process. Two approaches to deal with human-robot interaction can be distinguished: Long distance prediction which requires the recognition of intentions of other agents, and short distance control which deals with actions and reactions between agents and mutual reactive control of their motions and behaviors. In this context obstacle avoidance plays a prominent role. In this paper long distance prediction is represented by the identification of human intentions to use specific lanes by using fuzzy time clustering of pedestrian tracks. Another issue is the extrapolation of parts of both human and robot trajectories in the presence of scattered/uncertain measurements to guarantee a collision-free robot motion. Short distance control is represented by obstacle avoidance between agents using the method of velocity obstacles and both analytical and fuzzy control methods.