Multi-UAV trajectory planning for 3D visual inspection of complex structures

This paper presents a new trajectory planning algorithm for 3D autonomous UAV volume coverage and visual inspection. The algorithm is an extension of a state-of-the-art Heat Equation Driven Area Coverage (HEDAC) multi-agent area coverage algorithm for 3D domains. With a given target exploration density field, the algorithm designs a potential field and directs UAVs to the regions of higher potential, i.e., higher values of remaining density. Collisions between the agents and agents with domain boundaries are prevented by implementing the distance field and correcting the agent's directional vector when the distance threshold is reached. A unit cube test case is considered to evaluate this trajectory planning strategy for volume coverage. For visual inspection applications, the algorithm is supplemented with camera direction control. A field containing the nearest distance from any point in the domain to the structure surface is designed. The gradient of this field is calculated to obtain the camera orientation throughout the trajectory. Three different test cases of varying complexities are considered to validate the proposed method for visual inspection. The simplest scenario is a synthetic portal-like structure inspected using three UAVs. The other two inspection scenarios are based on realistic structures where UAVs are commonly utilized: a wind turbine and a bridge. When deployed to a wind turbine inspection, two simulated UAVs traversing smooth spiral trajectories have successfully explored the entire turbine structure while cameras are directed to the curved surfaces of the turbine's blades. In the bridge test case an efficacious visual inspection of a complex structure is demonstrated by employing a single UAV and five UAVs. The proposed methodology is successful, flexible and applicable in real-world UAV inspection tasks.Comment: 14 page

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

System Architectures for Cooperative Teams of Unmanned Aerial Vehicles Interacting Physically with the Environment

Unmanned Aerial Vehicles (UAVs) have become quite a useful tool for a wide range of applications, from inspection & maintenance to search & rescue, among others. The capabilities of a single UAV can be extended or complemented by the deployment of more UAVs, so multi-UAV cooperative teams are becoming a trend. In that case, as di erent autopilots, heterogeneous platforms, and application-dependent software components have to be integrated, multi-UAV system architectures that are fexible and can adapt to the team's needs are required. In this thesis, we develop system architectures for cooperative teams of UAVs, paying special attention to applications that require physical interaction with the environment, which is typically unstructured. First, we implement some layers to abstract the high-level components from the hardware speci cs. Then we propose increasingly advanced architectures, from a single-UAV hierarchical navigation architecture to an architecture for a cooperative team of heterogeneous UAVs. All this work has been thoroughly tested in both simulation and eld experiments in di erent challenging scenarios through research projects and robotics competitions. Most of the applications required physical interaction with the environment, mainly in unstructured outdoors scenarios. All the know-how and lessons learned throughout the process are shared in this thesis, and all relevant code is publicly available.Los vehículos aéreos no tripulados (UAVs, del inglés Unmanned Aerial Vehicles) se han convertido en herramientas muy valiosas para un amplio espectro de aplicaciones, como inspección y mantenimiento, u operaciones de rescate, entre otras. Las capacidades de un único UAV pueden verse extendidas o complementadas al utilizar varios de estos vehículos simultáneamente, por lo que la tendencia actual es el uso de equipos cooperativos con múltiples UAVs. Para ello, es fundamental la integración de diferentes autopilotos, plataformas heterogéneas, y componentes software -que dependen de la aplicación-, por lo que se requieren arquitecturas multi-UAV que sean flexibles y adaptables a las necesidades del equipo. En esta tesis, se desarrollan arquitecturas para equipos cooperativos de UAVs, prestando una especial atención a aplicaciones que requieran de interacción física con el entorno, cuya naturaleza es típicamente no estructurada. Primero se proponen capas para abstraer a los componentes de alto nivel de las particularidades del hardware. Luego se desarrollan arquitecturas cada vez más avanzadas, desde una arquitectura de navegación para un único UAV, hasta una para un equipo cooperativo de UAVs heterogéneos. Todo el trabajo ha sido minuciosamente probado, tanto en simulación como en experimentos reales, en diferentes y complejos escenarios motivados por proyectos de investigación y competiciones de robótica. En la mayoría de las aplicaciones se requería de interacción física con el entorno, que es normalmente un escenario en exteriores no estructurado. A lo largo de la tesis, se comparten todo el conocimiento adquirido y las lecciones aprendidas en el proceso, y el código relevante está publicado como open-source

Safe navigation and motion coordination control strategies for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications