“Flight Time” and the “Scope of the mission” play major roles in using UAVs as they affect most industrial activities. Once the battery has depleted, the UAV has to land on the ground and human interaction is needed to change the battery with a fully charged one. Nowadays, several automatic battery swapping systems are catching interest in research. This research presents the novel concept of an Inverted Docking Station that allows a quadrotor UAV to attach to the ceiling during the automatic battery-swapping process. The proposed design consist of a docking station, a positioning system and gripper mechanisms. The proposed design allows the quadrotor to carry the load under the quadrotor and remain attached throughout the servicing period. A mathematical model and design guideline have been proposed, and a Finite Element Analysis (FEA) was performed to check that the developed platform is strong enough to withstand the above task. A ‘DJI TELLO’ small-scale quadrotor was chosen as a case study to demonstrate the proposed research. Finally the advantages and the limitations of the system are discussed.

In virtual reality, VR sickness resulting from continuous locomotion via controllers or joysticks is still a significant problem. In this article, we present a set of algorithms to mitigate VR sickness that dynamically modulate the user's field of view by modifying the contrast of the periphery based on movement, color, and depth. In contrast with previous work, this vision modulator is a shader that is triggered by specific motions known to cause VR sickness, such as acceleration, strafing, and linear velocity. Moreover, the algorithm is governed by delta velocity, delta angle, and average color of the view. We ran two experiments with different washout periods to investigate the effectiveness of dynamic modulation on the symptoms of VR sickness, in which we compared this approach against a baseline and pitch-black field-of-view restrictors. Our first experiment made use of a just-noticeable-sickness design, which can be useful for building experiments with a short washout period.

Locomotion is an essential factor for interaction in virtual environments. Virtual reality allows users to move freely from the constraints of gravity or the environment, which is impossible in the real world. These experiences, in which users can move at all 6 degrees of freedom, can enable new navigation paradigms that exceed 2D ground-based movement in terms of enjoyment and interactivity. However, most existing VR locomotion interfaces have limitations because they are developed for ground-based locomotion, constraining the degrees of freedom (DoF) during movement as in the real world. This exploratory study was designed to seek the features required for three-dimensional (3D) locomotion by evaluating three types of interfaces: Slider, Teleport, and Point-Tug, which were implemented based on existing ground-based locomotion techniques. We then conducted a user study with 3D navigation tasks, using both objective and subjective measures to evaluate efficiency, overall usability, motion sickness, perceived workload, and presence. The results suggest that Slider has an advantage compared to the others in terms of usability and perceived workload since it can freely and intuitively designate the direction of movement.

Image-to-image translation methods have progressed from only considering the image-level information to integrating the global- and instance-level information. However, only the foreground instances are refined, and the background semantics are taken as an entire feature, which causes a substantial loss of the semantic information in the translation. Additionally, the insufficient quality of the translated semantic regions also leads to an unsatisfactory performance of the object recognition or visual odometry tasks in which the translated images/videos are further used. In this paper, we propose a novel generative adversarial network for panoptic-level image-to-image translation (PanopticGAN). The proposed method has three advantages: (1) the extracted panoptic perception (i.e., the foreground instances and background semantic regions) as content codes are aligned with the sampled panoptic style codes, which considers the panoptic-level information to avoid the semantic information loss, and the latent space of each object has a rich fusion of content and style codes to generate the higher-fidelity results; (2) a feature masking module is proposed to extract the representations within each object contour by masks for sharpening the object boundaries; (3) the improved fidelity of the translated semantic regions further contributes to enhancing the performance of the object recognition or visual odometry tasks that the translated images/videos are used in. In this paper, we also annotate a compact panoptic segmentation dataset for the thermal-to-color translation task. Extensive experiments are conducted to demonstrate the effectiveness of our PanopticGAN over the latest methods.

Prediction and observation of human motion are essential functions for robots co-existing with humans in everyday environments. We propose a people motion tracking and prediction approach by using the advantage of detailed 3D information about the positions of body joints. Using the shoulder position displayed in a geometrical skeleton diagram of a human's upper body part, the body pose from the proposed human kinematic model is estimated. Human motion tracking and path prediction are achieved via the extended Kalman Filter. The proposed method is verified in an indoor environment where humans pass by each other. Experiment results demonstrate that walking people and their body pose are robustly tracked and predicted accurately, with less occlusions compared to traditional human tracking.

Water is an essential substance for humans in their daily lives. There are many opportunities for us to come in contact with water, such as cooking, bathing, and swimming. However, few studies have reproduced the sensation of water touching the skin. This study aims to propose a novel midair haptic device, named FlowHaptics, that reproduces the feeling of the force of flowing water over human fingers using multiple air jets. We first estimated the temporal pressure distribution change of water in two-dimensional space using machine-learning-accelerated fluid simulation. We controlled the airflow based on the pressure distribution change obtained from the fluid simulation to reproduce the feeling of flowing water over the fingers using our proposed device, which can control multiple air jets in real time. We performed a psycho-physical evaluation of different flow velocities and a subjective evaluation of different velocity profiles. We found that FlowHaptics reliably created the illusion of the pressure distribution of flowing water on the fingers where the flow velocity could be distinguished within the range of 8.42% to 13.05%, and our estimated flow velocity profile with the configuration of three air jets felt more similar to flowing water when compared to a constant velocity profile according to the users.