Model-based real-time testing of drone autopilots

Key to the operation of robot drones is the autopilot software that realizes the low-level control. The correctness of autopilot implementations is currently mainly verified based on simulations. These may overlook the timing aspects of control loop executions, which are however fundamental to dependable operation. We report on our experience in applying model-based real-time testing to Ardupilot, a widely adopted autopilot. We describe our approach at deriving a model of Ardupilot's core functionality and at reducing the model to enable practical testing. Our work reveals that Ardupilot may fail in meeting the time constraints associated to critical functionality, such as enabling fail-safe operation. Through controlled experiments, we demonstrate the real-world occurrence of such erroneous executions

Platform Development for the Implementation and Testing of New Swarming Strategies

Gemstone Team SWARM-AISwarm robotics--the use of multiple autonomous robots in coordination to accomplish a task--is useful for mapping, light package transport, and search and rescue operations, among other applications. Researchers and industry professionals have developed robotic swarm mechanisms to accomplish these tasks. Some of those mechanisms or “strategies” have been tested on hardware; however, the technical requirements involved in fielding a drone swarm can be prohibitive to physical testing. Team SWARM-AI has developed a platform that provides a starting point for testing new swarming strategies. This platform allows the user to select vehicles of their choosing- either air, land, or water based, or some combination thereof- as well as define their own swarming method. Using a novel decentralized approach to ground control software, this platform provides a user interface and a system of computational “units” to coordinate drone swarms with a centralized, decentralized, or combination architecture. Additionally, the platform propagates user input from the master unit to the rest of the swarm and allows each unit to request sensor data from other units. The user is free to edit the processes by which each drone interacts with the environment and the rest of the swarm, giving them freedom to test their swarming strategy. The software system is then tested with a swarm of quadcopters using Software in the Loop (SITL) testing

Mobile Systems Research with Drones

Robot vehicle platforms, often called “drones”, offer exciting new opportuni- ties for mobile computing. While many systems respond to device mobility (such as smartphones), drones allow computer systems to actively control device location, allowing them to interact with the physical world in new ways and with new-found scale, efficiency, or precision

Exploring Multiway Dataflow Constraint Systems for programming Robotic Autonomous Systems

Denne avhandlingen utforsker programmering av robot-systemer ved hjelp av en programmeringsmodell som heter Multiway Dataflow Constraint Systems.Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO

An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to this day, most swarm development efforts have gone untested and thus unimplemented. Cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools are some of the major inhibitors to successful swarm implementation. This thesis examines how the OpenEaagles simulation framework can be extended to bridge this gap. This research aims to utilize Hardware-in-the-Loop (HIL) simulation to provide developers a functional capability to develop and test the behaviors of scalable and modular swarms of autonomous UAVs in simulation with high confidence that these behaviors will prop- agate to real/live ight tests. Demonstrations show the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, pro- vides realistic aircraft modeling, and is capable of simultaneously accommodating four hardware-piloted swarming UAVs during HIL simulation or 64 swarming UAVs during pure simulation

Unmanned aerial vehicle abstraction layer: An abstraction layer to operate unmanned aerial vehicles

This article presents a software layer to abstract users of unmanned aerial vehicles from the specific hardware of the platform and the autopilot interfaces. The main objective of our unmanned aerial vehicle abstraction layer (UAL) is to simplify the development and testing of higher-level algorithms in aerial robotics by trying to standardize and simplify the interfaces with the unmanned aerial vehicles. Unmanned aerial vehicle abstraction layer supports operation with PX4 and DJI autopilots (among others), which are current leading manufacturers. Besides, unmanned aerial vehicle abstraction layer can work seamlessly with simulated or real platforms and it provides calls to issue standard commands such as taking off, landing or pose, and velocity controls. Even though unmanned aerial vehicle abstraction layer is under continuous development, a stable version is available for public use. We showcase the use of unmanned aerial vehicle abstraction layer with a set of applications coming from several European research projects, where different academic and industrial entities have adopted unmanned aerial vehicle abstraction layer as a common development framework

Review of unmanned aircraft system technologies to enable beyond visual line of sight (BVLOS) operations

The need to develop and deploy Beyond Visual Line of Sight (BVLOS) aerial vehicles has intensified over the last decade. As the demand for Unmanned Aircraft Systems (UAS) has increased, so too has the regulations that surrounds the industry. Strict regulations are currently in place but differ from country to country. Due to these regulations BVLOS innovators have been posed the task of exploring the means of operating flight missions with the UAV out of the sight of the pilot. Autonomous flight capability is not only fundamental to BVLOS operations for UAS but also likely to have a significant impact on the future development of passenger carrying autonomous aircraft. This review explores the technologies that have been developed to date that enable BVLOS applications. BVLOS flight operations have the potential to open a huge area of commercial opportunity however, there remain many concerns about the current capabilities of UAS to detect and avoid manned and unmanned airborne hazards that may pose a significant safety risk