This paper proposes a method to robustify model predictive path integral (MPPI) control by directly taking into account the effects of parameter uncertainty into the controller formulation. Leveraging the recent notion of closed-loop state sensitivity, the proposed MPPI can consider the state sensitivity against parameter mismatch as a part of the system state, and consequently exploit this additional information to address the challenge of model mismatch in sampling-based model predictive control. Using an obstacle avoidance scenario, we demonstrate the use of our approach to control an aerial robot. We present an embedded implementation of our method, utilizing parallelization of computations on a GPU. Finally, we show the increased robustness of our approach over a standard MPPI controller through hardware-in-the-loop simulations and validate its embedded real-time properties.

We present a fully autonomous self-recharging drone system capable of long-duration sustained operations near powerlines. The drone is equipped with a robust onboard perception and navigation system that enables it to locate powerlines and approach them for landing. A passively actuated gripping mechanism grasps the powerline cable during landing after which a control circuit regulates the magnetic field inside a split-core current transformer to provide sufficient holding force as well as battery recharging.The system is evaluated in an active outdoor three-phase powerline environment. We demonstrate multiple contiguous hours of fully autonomous uninterrupted drone operations composed of several cycles of flying, landing, recharging, and takeoff, validating the capability of extended, essentially unlimited, operational endurance.

This work proposes a novel drone system designed to autonomously track and follow power lines and reconstruct them in 3D with a point cloud representation based on mmWave radar measurements. The system is composed of a GNSS-enabled quadrotor UAV equipped with a combined mmWave radar sensor and onboard compute module payload and has been designed to be small, lightweight, and low-cost. MmWave radar sensors offer great range and sensitivity in the task of power line detection with a high level of sparsity in the produced data when compared to traditional sensors such as LiDARs. The proposed system overcomes the radar sensor’s shortcomings by building up a point cloud representing the power line environment as the drone moves around in it. The built-up point cloud is analyzed using the onboard computer to detect the cables in the power line environment and to produce pose-estimates of each line. The system has been tested in a variety of scenarios and has been shown to be able to accurately detect and track power lines in varying weather conditions.

Non-functional properties, such as energy, time, and security (ETS) are becoming increasingly important in Cyber-Physical Systems (CPS) programming. This article describes TeamPlay, a research project funded under the EU Horizon 2020 programme between January 2018 and June 2021. TeamPlay aimed to provide the system designer with a toolchain for developing embedded applications where ETS properties are first-class citizens, allowing the developer to reflect directly on energy, time and security properties at the source code level. In this paper we give an overview of the TeamPlay methodology, introduce the challenges and solutions of our approach and summarise the results achieved. Overall, applying our TeamPlay methodology led to an improvement of up to 18% performance and 52% energy usage over traditional approaches.

Autonomously recharging UAVs from existing infrastructure has enormous potential for various applications, such as infrastructure inspection, surveillance, and search and rescue. While it is an active area of research, most related work focuses on alternating current (AC) infrastructure while very little work has been done on investigating the potential of recharging UAVs from direct current (DC) infrastructure. This work proposes a UAV system designed to autonomously recharge from existing DC infrastructure. Two onboard powerline grippers and a motorized cable drum enable the UAV to perform a two-stage landing on railway DC lines where a wire is connected between them through the UAV for recharging. Light-weight electronics designed to be carried by the UAV are developed to harvest energy from up to 3kV DC railway lines. The recharge mission is autonomously executed using fully onboard and real-time perception and trajectory planning and tracking algorithms. The potential of the system is shown in lab setting validation, with hardware-in-the-loop simulation, and partly in a real overhead powerline environment, verifying the functionality of the sub-components.

Drone grasping on power lines for recharging is challenging since it requires the gripper to be lightweight, carried by a drone, and efficient for a firm grasp. A deep understanding of the power line nature and its magnetic characteristic helps ease such challenges and bring new knowledge to gripper design. In this work, a novel adaptive, lightweight, and fail-safe magnetic gripper with a recharging feature is presented. The gripper exploits the radiated magnetic field of the lines for charging and holding the drone and can easily detach from the line. The gripper design has been validated in the lab and on a quadcopter with a real power line.

Unmanned Aerial Vehicles (UAVs) have introduced benefits in many areas of the energy sector. Today, power line sensor deployment is manually executed on passive power lines using helicopters, introducing great risks, costs and difficulties for the power distribution companies and the human operators.In this paper, we present a novel modular mechanical system utilizing pneumatic as the actuation source to deploy a sensor unit to a power line using a UAV, with aid of an autonomous alignment algorithm. The results show that the UAV can facilitate the sensor unit, and is capable of deploying it to the power line in an autonomous manner. The works leave opportunity for expanding to further applications in the future.

Today, many inspection domains utilize the benefits of drones to monitor and inspect infrastructure in an efficient manner. The energy grid is challenged by frequent and thorough inspection to stay operational. So far, drones have already been introduced to solve this challenge. However, the inspection drone still requires manual control and subsequent human examination of the captured photos and videos. This inspection process comes with a high inspection cost and is susceptible to human errors in fault finding. The proposed system builds on top of the authors’ previous research to develop and verify an integrated drone system for autonomous power line inspection, enabling functioning mechanics through pneumatics, extension of operation time through energy harvesting, Artificial Intelligent (AI) fault detection, and system autonomy using navigational algorithms. An advanced drone system has been designed and manufactured for the mission, with the results demonstrating the capability to perform an autonomous inspection mission in conditions up to 30 kts wind speeds, being additionally able to detect faults in real-time at a high rate during drone motion. Furthermore, we demonstrate the ability to recharge the drone battery within 2.4 hours.

In recent years drones have proven their ability to solve complex tasks autonomously and effectively. Drones have not been present as a ubiquitous technology for solving these tasks yet. The reason is mainly due to their limited flying time. The technology has great market potential in many areas if flight time could be increased. However, only a few solutions for recharging are available, but none for recharging on power lines. This remains a research topic for now. Through this paper a novel energy harvesting design is presented based on inductive coupling, allowing a drone to grasp and perch onto an overhead power line to recharge its batteries. The overall electronic design was based on analyzing and simulating different current transformers in simulation before testing in the laboratory. The split-core including windings was integrated into a gripping mechanism, verified by analytical and numerical analysis as well as non-destructive and destructive testing. The results indicate that enough power can be harvested from the powerline to recharge the drone batteries. The theoretically achievable power was also found to be within the expected range. With this design, drones can solve time-consuming and complex tasks in the future.

A large percentage of current overhead power transmission infrastructure is becoming aged, raising the demand for frequent grid inspections in an efficient, and cost-effect manner in order to keep it functional. Autonomous drones are a promising solution for infrastructure inspection, but require further investigation and development to be a viable alternative. This paper proposes a drone framework based on open-source platforms for autonomous inspection of electrical grid infrastructure and for cable grasping. The framework incorporates a cloud service functionality to provide geo-location data of nearby power pylons; a localisation system for navigation near power pylons and grasping cables; and algorithms for finding and planning paths for navigation and grasping over cables. The proposed framework has been tested and validated in simulation, with the development of a UAV platform for future integration.

Unmanned Aerial Vehicles (UAV) are increasingly gaining interest in Internet of Drones (IoD) applications for automatizing the labor-intensive tasks. They are used in various areas such as infrastructure inspection. However, UAVs have limited energy resources and computational processing capabilities, which prevents them from running applications onboard and accessing the internet for gaining knowledge about their mission. In order to address these challenges imposed by limited resource on the drone, we propose a new cloud system infrastructure for building open-source IoD applications. We have designed a client-server architecture which hosts the drone as a client and the cloud as a scalable server. For validating the developed IoD application, an open-source drone simulator and flight controller are adopted to perform the tests. The overall architecture of a drone-cloud framework is presented along with a use case to show the applicability of the proposed framework.

Recently, drones have been involved in several critical tasks such as infrastructure inspection, crisis response, and search and rescue operations. Such drones mostly use sophisticated computer vision techniques to effectively avoid obstacles and, thereby, require high computational power. Therefore, this work tuned and tested a computationally inexpensive algorithm, previously developed by the authors, for adaptive obstacle avoidance control of a drone. The algorithm aims at protecting the drone from entering in complex situations such as deadlocks and corners. The algorithm has been validated through simulation and implemented on a newly developed drone platform for infrastructure inspection. The design of the drone platform and the experimental results are presented in this study.

Recently, Unmanned Aerial Vehicles (UAVs), so-called drones, have gotten a lot of attention in academic research and commercial applications due to their simple structure, ease of operations and low-cost hardware components. Flight controller, embedded electronic component, represents the core part of the drone. It aims at performing the main operations of the drone (e.g., autonomous control and navigation). There are various types of flight controllers and each of them has its own characteristics and features. This paper presents an extensive survey on the publicly available open-source flight controllers that can be used for academic research. The paper introduces the basics of UAV system with its components. The survey fully covers both hardware and software open-source flight controller platforms and compares their main features.

This paper investigates the possibility of elevating SMS (Short Message Service) to support a cloud-based vehicle-Tracking platform. A secure application protocol over SMS is introduced to circumvent the security issues that may succumb the SMS. We propose a cost-effective Internet of Things (IoT) solution to countries dominated with GSM mobile infrastructure taking into consideration technology usage and the mobile network infrastructure available. A commercial off-The-shelf (COTS) IoT device such as the Raspberry Pi single board computer is depicted relying on GPS-GSM technologies envisioned as the in-vehicle device. Furthermore, a cloud web-platform is built involving the de-facto modern web-Applications standards and carefully tailored concerning the security aspects. The cost-effectiveness of our solution results from the use of COTS components, open source software, and cheap SMS subscription packages.

Smart grid applications belong to a diverse set of technologies, which combine behaviors and actions of all grid actors. Building such application is a challenging task requiring modeling, integrating, and validating different grid aspects efficiently. The design flow should adapt to the grid's requirements ranging from basic appliance functions to complex load scheduling algorithms and their integration's consequences. This paper tackles such challenges by proposing a framework to describe smart grid elements formally along with defining their interactions. The framework provides a smooth way for upgrading grid components independently and evaluate their performance. A case study utilizing an infrastructure of electric vehicles is demonstrated to validate the framework and prove its applicability in modeling smart grid applications. The case study enables a demand response service that provides a solution to the coordinated charging scheduling problem of electric vehicles.

Software applications are built to run on different devices with dissimilar platforms. The traditional software development process allows to build applications that run on a specific platform in which the development cycle becomes time-consuming and costly due to rebuilding the application for individual platform. To solve this problem, we propose a model-driven methodology for the development of a data visualization application targeting multiple platforms such as desktop computers, tablets and smart phones. The methodology flows from a Platform-Independent Model (PIM) to a Platform-Specific Model (PSM) and ends by executable models for multiple platform. The methodology paves the way to build an automatic synthesis tool that relieves the software developers from the repetitive work. The methodology is validated through a case study that visualizes data from smart grid domain. The case study is built in collaboration with the European research project SmartHG.

Event-driven demand response programs reward consumers for shifting their electricity loads upon requests from an aggregator, a balance responsible party, or a distribution system operator. In order to have any significant impact on the smart grid, the flexibility exhibited on the demand-side needs to be aggregated from a large number of consumers. This poses significant scalability challenges for the ICT infrastructure that controls flexible electricity loads. This paper reports from the European research project SEMIAH. The project aims to design a scalable infrastructure for residential demand response. The study presents progress towards the system design of a centralized load scheduling algorithm for controlling home appliances over time. The demand response system takes the power grid constraints and satisfaction of consumers into account. Simulation results from a case study quantify the potential impact of a residential demand response program.

In the above paper, the location for the Arab Academy for Science and Technology was incorrect in the biography of author, Emad Ebied. The correct location is Cairo, Egypt. The correct biography is provided.

The SmartHG project goal is to develop a suite of integrated software services (the SmartHG Platform) aiming at steering residential users energy demand in order to: keep operating conditions of the electrical grid within given healthy bounds, minimize energy costs, and minimize CO2 emissions. This is achieved by exploiting knowledge (demand awareness) of electrical energy prosumption of residential users as gained from SmartHG sensing and communication infrastructure. This paper describes such an infrastructure along with user demand patterns emerging from the data gathered from ~600 sensors installed in ~40 homes participating in SmartHG test-beds.

Smart grids offer an indisputable business opportunity for system operators and energy traders to engage in demand response programs. Hereby these actors may profit from trading flexibility provided at the prosumer side on the energy markets. This paper discusses the system design challenges for an information and communication technology system infrastructure facilitating DR for residential prosumers. It presents the SEMIAH framework for providing a scalable infrastructure for residential DR built on a component-based architecture with the virtual power plant at the hearth of the system. The paper examines the possible impact of deploying an automated residential DR program on the quality and stability of a low voltage grid.

The design of distributed embedded systems is a challenging task that requires raising the level of abstraction to handle the different involved concerns. In particular, standard modeling languages and precise semantics specification are necessary to address the networking-related aspects at a high level of abstraction. The Unified Modeling Language (UML) and its MARTE profile are valid formalisms to model real-time embedded systems but they lack precise modeling elements when addressing applications and platforms forming distributed embedded systems. In this work, we formalize a coherent set of modeling elements for the design and deployment of distributed embedded systems. A novel UML profile for networking is proposed as a semantic and syntactic extension to the UML Profile for MARTE: The Network Profile.

Design of distributed embedded systems is a challenging task and it requires raising the level of abstraction to overcome the complexity of the design. In particular, modeling languages and semantic specification are necessary to address network description at high level of abstraction. Starting from this abstraction view, model manipulation is needed to explore various design alternatives and code generation is required for their simulation. In this paper, we propose the use of unified modeling language diagrams combined with a formal computational model as a key solution to specify requirements, generate design alternatives, and code for simulation. This paper proposes a formal framework and supporting tools to represent the application requirements, the library of network components, the environment description, and the rules to compose them. The framework allows to generate code for design validation by simulation and provides back annotation mechanism of the simulation results to refine the original model.

Networked embedded systems are essential building blocks of a broad variety of distributed applications ranging from agriculture to industrial automation to healthcare and more. These often require specific energy optimizations to increase the battery lifetime or to operate using energy harvested from the environment. Since a dominant portion of power consumption is determined and managed by software, the software development process must have access to the sophisticated power management mechanisms provided by state-of-the-art hardware platforms to achieve the best tradeoff between system availability and reactivity. Furthermore, internode communications must be considered to properly assess the energy consumption. This article describes a design flow based on a SystemC virtual platform including both accurate power models of the hardware components and a fast abstract model of the wireless network. The platform allows both model-driven design of the application and the exploration of power and network management alternatives. These can be evaluated in different network scenarios, allowing one to exploit power optimization strategies without requiring expensive field trials. The effectiveness of the approach is demonstrated via experiments on a wireless body area network application.

The behavior of Networked Embedded Systems (NES) is not only driven by network components but also by the surrounding environment. To verify the correct behavior of such systems, different tests should be performed under different environmental conditions. The complexity and expense of testing such systems in real environments leads us to propose a simulation-based approach to tackle this problem. In this work, we propose UML-based methodology and a framework for modeling NES applications together with the environment and a mechanism to automatically generate simulation code for design verification. The approach is supported by a novel UML profile and a set of tools for simulation code generation.

Network design in distributed embedded applications is a novel challenging task which requires 1) the extraction of communication requirements from application specification and 2) the choice of channels and protocols connecting physical nodes. These issues are faced in the paper by adopting UML/MARTE as specification front-end and repository of refined versions of the model obtained by both simulation and analytical exploration of the design space. The emphasis is on using standard UML/MARTE elements for the description of networked embedded systems to allow re-use, tool interoperability and documentation generation. The approach is explained on a case study related to building automation.

Verification of real time embedded systems at high level of abstraction is a challenging task that requires the simulation of the system and the checking of its timing and functional properties as well as constraints. The paper presents a methodology which starts from UML sequence diagrams with MARTE timing constraints and generates a SystemC/TLM model with checkers. The execution of the model allows to verify the specified sequence of exchanged information between components while checkers allow to verify that properties and timing constraints are met. The application of the methodology to the design of a wireless sensor node shows the validity of the approach and its simulation overhead.

Verification of real time embedded systems is becoming more and more complex in terms of maintaining the code size and keeping equivalence between the specification. It requires the simulation of the system and the checking of its timing and functional properties as well as constraints. The paper presents a methodology which starts from UML sequence diagrams with MARTE timing constraints and generates a VHDL model with checkers. The simulation of the model allows to verify the specified sequence of exchanged information between components while checkers allow to verify that properties and timing constraints are met. The application of the methodology to the design of a wireless sensor node shows the validity of the approach and its simulation overhead.