Hand-worn Haptic Interface for Drone Teleoperation

Drone teleoperation is usually accomplished using remote radio controllers, devices that can be hard to master for inexperienced users. Moreover, the limited amount of information fed back to the user about the robot's state, often limited to vision, can represent a bottleneck for operation in several conditions. In this work, we present a wearable interface for drone teleoperation and its evaluation through a user study. The two main features of the proposed system are a data glove to allow the user to control the drone trajectory by hand motion and a haptic system used to augment their awareness of the environment surrounding the robot. This interface can be employed for the operation of robotic systems in line of sight (LoS) by inexperienced operators and allows them to safely perform tasks common in inspection and search-and-rescue missions such as approaching walls and crossing narrow passages with limited visibility conditions. In addition to the design and implementation of the wearable interface, we performed a systematic study to assess the effectiveness of the system through three user studies (n = 36) to evaluate the users' learning path and their ability to perform tasks with limited visibility. We validated our ideas in both a simulated and a real-world environment. Our results demonstrate that the proposed system can improve teleoperation performance in different cases compared to standard remote controllers, making it a viable alternative to standard Human-Robot Interfaces.Comment: Accepted at the IEEE International Conference on Robotics and Automation (ICRA) 202

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

A Mathematical Framework for Unmanned Aerial Vehicle Obstacle Avoidance

The obstacle avoidance navigation problem for Unmanned Aerial Vehicles (UAVs) is a very challenging problem. It lies at the intersection of many fields such as probability, differential geometry, optimal control, and robotics. We build a mathematical framework to solve this problem for quadrotors using both a theoretical approach through a Hamiltonian system and a machine learning approach that learns from human sub-experts\u27 multiple demonstrations in obstacle avoidance. Prior research on the machine learning approach uses an algorithm that does not incorporate geometry. We have developed tools to solve and test the obstacle avoidance problem through mathematics

Exploring Alternative Control Modalities for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs), commonly known as drones, are defined by the International Civil Aviation Organization (ICAO) as an aircraft without a human pilot on board. They are currently utilized primarily in the defense and security sectors but are moving towards the general market in surprisingly powerful and inexpensive forms. While drones are presently restricted to non-commercial recreational use in the USA, it is expected that they will soon be widely adopted for both commercial and consumer use. Potentially, UAVs can revolutionize various business sectors including private security, agricultural practices, product transport and maybe even aerial advertising. Business Insider foresees that 12% of the expected $98 billion cumulative global spending on aerial drones through the following decade will be for business purposes.[28] At the moment, most drones are controlled by some sort of classic joystick or multitouch remote controller. While drone manufactures have improved the overall controllability of their products, most drones shipped today are still quite challenging for inexperienced users to pilot. In order to help mitigate the controllability challenges and flatten the learning curve, gesture controls can be utilized to improve piloting UAVs. The purpose of this study was to develop and evaluate an improved and more intuitive method of flying UAVs by supporting the use of hand gestures, and other non-traditional control modalities. The goal was to employ and test an end-to-end UAV system that provides an easy-to-use control interface for novice drone users. The expectation was that by implementing gesture-based navigation, the novice user will have an overall enjoyable and safe experience quickly learning how to navigate a drone with ease, and avoid losing or damaging the vehicle while they are on the initial learning curve. During the course of this study we have learned that while this approach does offer lots of promise, there are a number of technical challenges that make this problem much more challenging than anticipated. This thesis details our approach to the problem, analyzes the user data we collected, and summarizes the lessons learned

Design and Implementation of the Kinect Controlled Electro-Mechanical Skeleton (K.C.E.M.S)

Mimicking real-time human motion with a low cost solution has been an extremely difficult task in the past but with the release of the Microsoft Kinect motion capture system, this problem has been simplified. This thesis discusses the feasibility and design behind a simple robotic skeleton that utilizes the Kinect to mimic human movements in near real-time. The goal of this project is to construct a 1/3-scale model of a robotically enhanced skeleton and demonstrate the abilities of the Kinect as a tool for human movement mimicry. The resulting robot was able to mimic many human movements but was mechanically limited in the shoulders. Its movements were slower then real-time due to the inability for the controller to handle real-time motions. This research was presented and published at the 2012 SouthEastCon. Along with this, research papers about the formula hybrid accumulator design and the 2010 autonomous surface vehicle were presented and published

Natural User Interfaces for Human-Drone Multi-Modal Interaction

Personal drones are becoming part of every day life. To fully integrate them into society, it is crucial to design safe and intuitive ways to interact with these aerial systems. The recent advances on User-Centered Design (UCD) applied to Natural User Interfaces (NUIs) intend to make use of human innate features, such as speech, gestures and vision to interact with technology in the way humans would with one another. In this paper, a Graphical User Interface (GUI) and several NUI methods are studied and implemented, along with computer vision techniques, in a single software framework for aerial robotics called Aerostack which allows for intuitive and natural human-quadrotor interaction in indoor GPS-denied environments. These strategies include speech, body position, hand gesture and visual marker interactions used to directly command tasks to the drone. The NUIs presented are based on devices like the Leap Motion Controller, microphones and small size monocular on-board cameras which are unnoticeable to the user. Thanks to this UCD perspective, the users can choose the most intuitive and effective type of interaction for their application. Additionally, the strategies proposed allow for multi-modal interaction between multiple users and the drone by being able to integrate several of these interfaces in one single application as is shown in various real flight experiments performed with non-expert users