Developing methods for autonomous landing of an unmanned aerial vehicle (UAV) on a mobile platform has been an active area of research over the past decade, as it offers an attractive solution for cases where rapid deployment and recovery of a fleet of UAVs, continuous flight tasks, extended operational ranges, and mobile recharging stations are desired. In this work, we present a new autonomous landing method that can be implemented on micro UAVs that require high-bandwidth feedback control loops for safe landing under various uncertainties and wind disturbances. We present our system architecture, including dynamic modeling of the UAV with a gimbaled camera, implementation of a Kalman filter for optimal localization of the mobile platform, and development of model predictive control (MPC), for guidance of UAVs. We demonstrate autonomous landing with an error of less than 37 cm from the center of a mobile platform traveling at a speed of up to 12 m/s under the condition of noisy measurements and wind disturbances.

Body-worn inertial sensors have enabled motion capture outside of the laboratory setting. In this work, an inertial measurement unit was attached to the upper arm to track and discriminate between shoulder motion gestures in order to help prevent shoulder over-use injuries in athletics through real-time preventative feedback. We present a detection and classification approach that can be used to count the number of times certain motion gestures occur. The application presented involves tracking baseball throws and volleyball serves, which are common overhead movements that can lead to shoulder and elbow overuse injuries. Eleven subjects are recruited to collect training, testing, and randomized validation data, which include throws, serves, and seven other exercises that serve as a large null class of similar movements, which is analogous to a realistic usage scenario and requires a robust estimator.

Smart glasses are a new family of technological devices that share several characteristics with conventional eyeglasses. Yet, little is known about how individuals process them. Drawing upon categorization theories and prior research on technology acceptance, the authors conduct two empirical studies to show that (a) smart glasses are perceived as technology but vary in their degree of fashion, (b) the perception of smart glasses determines the factors that explain adoption intention, and (c) a majority of consumers process smart glasses as a combination of fashion and technology (“fashnology”), whereas a smaller number of consumers perceive them exclusively as technology or fashion, respectively.

Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.

Convolutional neural networks (CNNs) have been used successfully in solving many challenging visual perception tasks facing mobile robots and self-driving cars. To facilitate deploying of such models on embedded hardware onboard mobile robots which has limited resources, multi-task learning approaches have become common. In typically used multi-task learning, a shared encoder network extracts features from inputs whereas multiple task-specific decoders transform these features into their target output. However, properly combining different tasks' losses into the final network loss such that each task is making progress learning is a major challenge in these approaches. In this paper, we present an innovative approach to extend a typical single-task network with the capability of performing two tasks without multiple decoders. That is, a single-stream two-task network. The two output tasks are semantic segmentation and monocular depth prediction which are essential tasks in visual perception for autonomous driving. The method is centered on solving semantic segmentation with a regression loss function rather than a classification one. With our approach, we seize multi-task learning benefits of reduced overhead and enhanced generalization while alleviating the need to balance different loss functions. Experimental evaluations with baseline single tasks and a multi-task network are presented.

Vision-based motion estimation is an effective means for mobile robot localization and is often used in conjunction with other sensors for navigation and path planning. This paper presents a low-overhead real-time ego-motion estimation (visual odometry) system based on either a stereo or RGB-D sensor. The algorithm’s accuracy outperforms typical frame-to-frame approaches by maintaining a limited local map, while requiring significantly less memory and computational power in contrast to using global maps common in full visual SLAM methods. The algorithm is evaluated on common publicly available datasets that span different use-cases and performance is compared to other comparable open-source systems in terms of accuracy, frame rate and memory requirements. This paper accompanies the release of the source code as a modular software package for the robotics community compatible with the Robot Operating System (ROS).