Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

Cognitive wireless sensor network platform for cooperative communications

Nowadays, Wireless Ad Hoc Sensor Networks (WAHSNs), specially limited in energy and resources, are subject to development constraints and difficulties such as the increasing RF spectrum saturation at the unlicensed bands. Cognitive Wireless Sensor Networks (CWSNs), leaning on a cooperative communication model, develop new strategies to mitigate the inefficient use of the spectrum that WAHSNs face. However, few and poorly featured platforms allow their study due to their early research stage. This paper presents a versatile platform that brings together cognitive properties into WAHSNs. It combines hardware and software modules as an entire instrument to investigate CWSNs. The hardware fits WAHSN requirements in terms of size, cost, features, and energy. It allows communication over three different RF bands, becoming the only cognitive platform for WAHSNs with this capability. In addition, its modular and scalable design is widely adaptable to almost any WAHSN application. Significant features such as radio interface (RI) agility or energy consumption have been proven throughout different performance tests

Cooperative Carrying Control for Mobile Robots in Indoor Scenario

openIn recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions. In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints. Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage.In recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions. In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints. Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage

Armstrong Flight Research Center Research Technology and Engineering 2017

I am delighted to present this report of accomplishments at NASA's Armstrong Flight Research Center. Our dedicated innovators possess a wealth of performance, safety, and technical capabilities spanning a wide variety of research areas involving aircraft, electronic sensors, instrumentation, environmental and earth science, celestial observations, and much more. They not only perform tasks necessary to safely and successfully accomplish Armstrong's flight research and test missions but also support NASA missions across the entire Agency. Armstrong's project teams have successfully accomplished many of the nation's most complex flight research projects by crafting creative solutions that advance emerging technologies from concept development and experimental formulation to final testing. We are developing and refining technologies for ultra-efficient aircraft, electric propulsion vehicles, a low boom flight demonstrator, air launch systems, and experimental x-planes, to name a few. Additionally, with our unique location and airborne research laboratories, we are testing and validating new research concepts. Summaries of each project highlighting key results and benefits of the effort are provided in the following pages. Technology areas for the projects include electric propulsion, vehicle efficiency, supersonics, space and hypersonics, autonomous systems, flight and ground experimental test technologies, and much more. Additional technical information is available in the appendix, as well as contact information for the Principal Investigator of each project. I am proud of the work we do here at Armstrong and am pleased to share these details with you. We welcome opportunities for partnership and collaboration, so please contact us to learn more about these cutting-edge innovations and how they might align with your needs

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Towards a swarm robotic approach for cooperative object recognition

Social insects have inspired the behaviours of swarm robotic systems for the last 20 years. Interactions of the simple individuals in these swarms form solutions to relatively complex problems. A novel swarm robotic method is investigated for future robotic cooperative object recognition tasks. Previous multi-agent systems involve cameras and image analyses to identify objects. They cooperate only to improve their hypotheses of the shape's identity. The system proposed uses agents whose interactions with each other around the physical boundaries of the object's shape allow the distinguishing features found. The agents are a physical embodiment of the vision system, making them suitable for environments where it would not be possible to use a camera. A Simplified Hexagonal Model was developed to simulate and examine the strategies. The hexagonal cells of which can be empty, contain an agent (hBot) or part of an object shape. Initially the hBots are required to identify the valid object shapes from a set of two types of known shapes. To do this the hBots change state when in contact with an object and when touching other hBots of the same state level, where some states are only achieved when neighbouring certain object shapes. The agents are oblivious, anonymous and homogeneous. They also do not know their position or orientation and cannot distinguish between object shapes alone due to their limited sensor range. Further work increased the number of object shapes to provide a range of scenarios