Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test

Data processing and wearable systems for effective human monitoring

The last few decades have seen an unrestrained diffusion of smart-integrated technologies that are extremely pervasive and customized based on humans’ environments and habits. Wearable and mobile technologies such as smartphones, smartwatches, lightweight sensors, textile-based support systems, flexible displays, and micro-cameras are now supplied with a significant amount of computational power, low-energy wireless communication, long-life battery, and large-memory storage that make them a valid platform for monitoring the everyday life of humans [1]. In this context, a large variety of new sensors are being developed to equip such well-established wearable and mobile technologies with the aim of continuous monitoring of physical behavior, emotional state, well-being, and health condition. Interestingly, the recently improved computational resources of mobile systems allow us to acquire, process, and communicate a large set of different information. Nevertheless, this confronts us with the chance and challenge of managing an impressive amount of heterogeneous data, including physiological signals, through new ad-hoc processing, synthesis methods, and big data analysis as well as ad-hoc experimental paradigms, system designs, and models

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human–robot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle’s driving cab. The proposed system obtains human’s skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds

Context-aware collaborative storage and programming for mobile users

Since people generate and access most digital content from mobile devices, novel innovative mobile apps and services are possible. Most people are interested in sharing this content with communities defined by friendship, similar interests, or geography in exchange for valuable services from these innovative apps. At the same time, they want to own and control their content. Collaborative mobile computing is an ideal choice for this situation. However, due to the distributed nature of this computing environment and the limited resources on mobile devices, maintaining content availability and storage fairness as well as providing efficient programming frameworks are challenging. This dissertation explores several techniques to improve these shortcomings of collaborative mobile computing platforms. First, it proposes a medley of three techniques into one system, MobiStore, that offers content availability in mobile peer-to-peer networks: topology maintenance with robust connectivity, structural reorientation based on the current state of the network, and gossip-based hierarchical updates. Experimental results showed that MobiStore outperforms a state-of-the-art comparison system in terms of content availability and resource usage fairness. Next, the dissertation explores the usage of social relationship properties (i.e., network centrality) to improve the fairness of resource allocation for collaborative computing in peer-to-peer online social networks. The challenge is how to provide fairness in content replication for P2P-OSN, given that the peers in these networks exchange information only with one-hop neighbors. The proposed solution provides fairness by selecting the peers to replicate content based on their potential to introduce the storage skewness, which is determined from their structural properties in the network. The proposed solution, Philia, achieves higher content availability and storage fairness than several comparison systems. The dissertation concludes with a high-level distributed programming model, which efficiently uses computing resources on a cloud-assisted, collaborative mobile computing platform. This platform pairs mobile devices with virtual machines (VMs) in the cloud for increased execution performance and availability. On such a platform, two important challenges arise: first, pairing the two computing entities into a seamless computation, communication, and storage unit; and second, using the computing resources in a cost-effective way. This dissertation proposes Moitree, a distributed programming model and middleware that translates high-level programming constructs into events and provides the illusion of a single computing entity over the mobile-VM pairs. From programmers’ viewpoint, the Moitree API models user collaborations into dynamic groups formed over location, time, or social hierarchies. Experimental results from a prototype implementation show that Moitree is scalable, suitable for real-time apps, and can improve the performance of collaborating apps regarding latency and energy consumption

An Experimental Platform for Investigating Decision and Collaboration Technologies in Time-Sensitive Mission Control Operations

This report describes the conceptual design and detailed architecture of an experimental platform developed to support investigations of novel decision and collaboration technologies for complex, time-critical mission control operations, such as military command and control and emergency response. In particular, the experimental platform is designed to enable exploration of novel interface and interaction mechanisms to support both human-human collaboration and human-machine collaboration for mission control operations involving teams of human operators engaged in supervisory control of intelligent systems, such as unmanned aerial vehicles (UAVs). Further, the experimental platform is designed to enable both co-located and distributed collaboration among operations team members, as well as between team members and relevant mission stakeholders. To enable initial investigations of new information visualization, data fusion, and data sharing methods, the experimental platform provides a synthetic task environment for a representative collaborative time-critical mission control task scenario. This task scenario involves a UAV operations team engaged in intelligence, surveillance, and reconnaissance (ISR) activities. In the experimental task scenario, the UAV team consists of one mission commander and three operators controlling multiple, homogeneous, semi-autonomous UAVs. In order to complete its assigned missions, the UAV team must coordinate with a ground convoy, an external strike team, and a local joint surveillance and target attack radar system (JSTARS). This report details this task scenario, including the possible simulation events that can occur and the logic governing the simulation dynamics. In order to perform human-in-the-loop experimentation within the synthetic task environment, the experimental platform also consists of a physical laboratory designed to emulate a miniature command center. The Command Center Laboratory comprises a number of large-screen displays, multi-screen operator stations, and mobile, tablet-style devices. This report details the physical configuration and hardware components of this Command Center Laboratory. Details are also provided of the software architecture used to implement the synthetic task environment and experimental interface technologies to facilitate user experiments in this laboratory. The report also summarizes the process of conducting an experiment in the experimental platform, including details of scenario design, hardware and software instrumentation, and participant training. Finally, the report suggests several improvements that could be made to the experimental platform based on insights gained from initial user experiments that have been conducted in this environment.Prepared For Boeing, Phantom Work

Development and Performance Evaluation of a Connected Vehicle Application Development Platform (CVDeP)

Connected vehicle (CV) application developers need a development platform to build, test and debug real-world CV applications, such as safety, mobility, and environmental applications, in edge-centric cyber-physical systems. Our study objective is to develop and evaluate a scalable and secure CV application development platform (CVDeP) that enables application developers to build, test and debug CV applications in realtime. CVDeP ensures that the functional requirements of the CV applications meet the corresponding requirements imposed by the specific applications. We evaluated the efficacy of CVDeP using two CV applications (one safety and one mobility application) and validated them through a field experiment at the Clemson University Connected Vehicle Testbed (CU-CVT). Analyses prove the efficacy of CVDeP, which satisfies the functional requirements (i.e., latency and throughput) of a CV application while maintaining scalability and security of the platform and applications

JXTA-Overlay: a P2P platform for distributed, collaborative, and ubiquitous computing

With the fast growth of the Internet infrastructure and the use of large-scale complex applications in industries, transport, logistics, government, health, and businesses, there is an increasing need to design and deploy multifeatured networking applications. Important features of such applications include the capability to be self-organized, be decentralized, integrate different types of resources (personal computers, laptops, and mobile and sensor devices), and provide global, transparent, and secure access to resources. Moreover, such applications should support not only traditional forms of reliable distributing computing and optimization of resources but also various forms of collaborative activities, such as business, online learning, and social networks in an intelligent and secure environment. In this paper, we present the Juxtapose (JXTA)-Overlay, which is a JXTA-based peer-to-peer (P2P) platform designed with the aim to leverage capabilities of Java, JXTA, and P2P technologies to support distributed and collaborative systems. The platform can be used not only for efficient and reliable distributed computing but also for collaborative activities and ubiquitous computing by integrating in the platform end devices. The design of a user interface as well as security issues are also tackled. We evaluate the proposed system by experimental study and show its usefulness for massive processing computations and e-learning applications.Peer ReviewedPostprint (author's final draft