Fundamentals of Wireless Communication Link Design for Networked Robotics

This chapter aims to present the fundamentals of the design of wireless communication links for networked robotics applications. First, we provide an overview of networked robotics applications, motivating the importance of the wireless communication link as an enabler of these applications. Next, we review the wireless communication technologies available today, discussing the existent tradeoffs between range, power, and data rate, and introducing the main concepts regarding the design of wireless communication links. Finally, we present a design example of a wireless communication link and the results obtained. We conclude the chapter with a discussion of the results and the challenges faced in the design of wireless communication links for networked robotics

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

A plug and play transparent communication layer for cloud robotics architectures

The cloud robotics paradigm aims at enhancing the abilities of robots by using cloud services, but it still poses several challenges in the research community. Most of the current literature focuses on how to enrich specific robotic capabilities, overlooking how to effectively establish communication between the two fields. Our work proposes a “plug-and-play” solution to bridge the communication gap between cloud and robotic applications. The proposed solution is designed based on the mature WebSocket technology and it can be extended to any ROS-based robotic platform. The main contributions of this work are the definition of a reliable autoconnection/autoconfiguration mechanism as well as to outline a scalable communication layer that allows the effective control of multiple robots from multiple users. The “plug-and-play” solution was evaluated in both simulated and real scenarios. In the first case, the presence of users and robots was simulated with Robot Operating System (ROS) nodes running on five machines. In the real scenario, three non-expert users teleoperated, simultaneously, three remote robots by using the proposed communication layer with different networking protocols. Results confirmed the reliability at different levels: at startup (success_rate = 100%); during high-rate communications (message_lost = 0%); in performing open-loop spiral trajectories with enhancement, with respect to similar works; and in the quality of simultaneous teleoperations

Detection, location and grasping objects using a stereo sensor on UAV in outdoor environments

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoor

A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

This paper treats the systematic injury analysis of lower arm robot–human impacts. For this purpose, a passive mechanical lower arm (PMLA) was developed that mimics the human impact response and is suitable for systematic impact testing and prediction of mild contusions and lacerations. A mathematical model of the passive human lower arm is adopted to the control of the PMLA. Its biofidelity is verified by a number of comparative impact experiments with the PMLA and a human volunteer. The respective dynamic impact responses show very good consistency and support the fact that the developed device may serve as a human substitute in safety analysis for the described conditions. The collision tests were performed with two different robots: the DLR Lightweight Robot III (LWR-III) and the EPSON PS3L industrial robot. The data acquired in the PMLA impact experiments were used to encapsulate the results in a robot independent safety curve, taking into account robot's reflected inertia, velocity and impact geometry. Safety curves define the velocity boundaries on robot motions based on the instantaneous manipulator dynamics and possible human injury due to unforeseen impacts. Copyright © Cambridge University Press 201

An Overview of Industrial Robots Control and Programming Approaches

Nowadays, manufacturing plants are required to be flexible to respond quickly to customer demands, adapting production and processes without affecting their efficiency. In this context, Industrial Robots (IRs) are a primary resource for modern factories due to their versatility which allows the execution of flexible, reconfigurable, and zero-defect manufacturing tasks. Even so, the control and programming of the commercially available IRs are limiting factors for their effective implementation, especially for dynamic production environments or when complex applications are required. These issues have stimulated the development of new technologies that support more efficient methods for robot control and programming. The goal of this research is to identify and evaluate the main approaches proposed in scientific papers and by the robotics industry in the last decades. After a critical review of the standard IR control schematic, the paper discusses the available control alternatives and summarizes their characteristics, range of applications, and remaining limitations

Gaussian mixture model-based contrast enhancement

In this study, a method for enhancing low-contrast images is proposed. This method, called Gaussian mixture model-based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low-contrast images into a set of scaled and shifted Gaussians. The individual histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimised to set up a Gaussian mixture modelling with lowest approximation error and highest similarity to the original histogram. Compared with the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis shows that GMMCE is a low-complexity method

Continuous fusion of motion data using an axis-angle rotation representation with uniform B-spline

The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept

Geoffrey: An Automated Schedule System on a Social Robot for the Intellectually Challenged

The accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion. The system is also able to detect if the steps are being properly carried out or not, issuing alerts in that case. To do so, an ensemble of deep learning techniques is used. The schedule is customizable by the carers and authorized relatives. Our system could enhance the quality of life of the patients and improve their self-autonomy. The experimentation, which was supervised by the ADACEA foundation, validates the achievement of these goalsThe accelerated growth of the percentage of elder people and persons with brain injury-related conditions and who are intellectually challenged are some of the main concerns of the developed countries. These persons often require special cares and even almost permanent overseers that help them to carry out diary tasks. With this issue in mind, we propose an automated schedule system which is deployed on a social robot. The robot keeps track of the tasks that the patient has to fulfill in a diary basis. When a task is triggered, the robot guides the patient through its completion. The system is also able to detect if the steps are being properly carried out or not, issuing alerts in that case. To do so, an ensemble of deep learning techniques is used. The schedule is customizable by the carers and authorized relatives. Our system could enhance the quality of life of the patients and improve their self-autonomy. The experimentation, which was supervised by the ADACEA foundation, validates the achievement of these goal

Extracting Semantic Information from Visual Data: A Survey

The traditional environment maps built by mobile robots include both metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots to interact with or serve human users who normally rely on the conceptual knowledge or semantic contents of the environment. Therefore, the construction of semantic maps becomes necessary for building an effective human-robot interface for service robots. This paper reviews recent research and development in the field of visual-based semantic mapping. The main focus is placed on how to extract semantic information from visual data in terms of feature extraction, object/place recognition and semantic representation methods