On the origin of satellite swarms

For a species to develop in nature, two basically two things are needed: an enabling technology and a "niche". In spacecraft design the story is basically the same. Both a suitable technology and a niche application need to be there before a new generation of spacecraft can be developed. Last century two technologies have emerged that had and still have a huge impact on the development of technical systems: Micro-Electronics (ME) and Micro-Systems Technology (MST). Both are ruled by Moore's Law that indicates that considerable technology updates appear at the pace of years or even months instead of decades. Systems that need a development time of more than a few years will inevitably be based on "out-dated" and thereby difficult to maintain and repair technology unless during the development constant redesigns are made. This makes the development of the system at least very expensive. Although expenses do not seem to be a frequent show stopper in the design of spacecraft, it is still very interesting to investigate what system architectures might evolve when the specific properties of the new technologies ME and MST are fully exploited. ME presently offers more than 2 billion transistors on a chip and MST offers mechanical systems like resonators, mechanical switches, propulsions units, gyroscopes and many other sensors that _t in a volume of a few square millimeters to a few centimeters. So it is possible to fit a lot of signal processing power together with the necessary sensors and actuators in a volume that is really very small compared to any know space system. Of course state-of-the art spacecraft will immediately outperform these units in all aspects apart from cost and quantity. For the _rst time it makes sense to envisage the operation of formations of tens to hundreds of satellites that are cheap because they are based on standard commercial COTS technology and system designs. These satellite swarms will not be the systems that replace all other space systems. But, like in nature, there is a niche where swarms are the optimal solution. It's time to start occupying this niche. Typical properties of a swarm in nature are robustness, redundancy, large area coverage, the lack a hierarchical command structure, limited processing power per unit and self-organization ("swarm-intelligence"). This paper discusses the technological trends that lead to satellite swarms, where they can go and what new science they can create

OLFAR a radio telescope based on nano satellites in moon orbit

It seems very likely that missions with nano-satellites in professional scientific or commercial applications will not be single-satellite missions. Well structured formations or less structured swarms of nano-satellites will be able to perform tasks that cannot be done in the “traditional” way. The Dutch space-born radio telescope project OLFAR, the Orbiting Low Frequency Array, is a good example of a typical “swarm task”. The OLFAR radio telescope will be composed of an antenna array based on nano-satellites orbiting the moon to shield the receiving nodes from terrestrial interference. The array will receive frequencies in a band from around 30 kHz to 30 MHz. This frequency band is scientifically very interesting, since it will be able to detect signals originating from the yet unseen “Dark Ages” ranging from the Big Bang until around 400 million year after. Another science driver is the LF activity from (exo) planets. In this paper the design parameters for the satellites and the swarm will be given and status of the OLFAR project will be reported. Details will be given about the antenna system, the LF-receiver and the signals that are expecte

Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper

Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

sUAS Swarm Navigation using Inertial, Range Radios and Partial GNSS

Small Unmanned Aerial Systems (sUAS) operations are increasing in demand and complexity. Using multiple cooperative sUAS (i.e. a swarm) can be beneficial and is sometimes necessary to perform certain tasks (e.g., precision agriculture, mapping, surveillance) either independent or collaboratively. However, controlling the flight of multiple sUAS autonomously and in real-time in a challenging environment in terms of obstacles and navigation requires highly accurate absolute and relative position and velocity information for all platforms in the swarm. This information is also necessary to effectively and efficiently resolve possible collision encounters between the sUAS. In our swarm, each platform is equipped with a Global Navigation Satellite System (GNSS) sensor, an inertial measurement unit (IMU), a baro-altimeter and a relative range sensor (range radio). When GNSS is available, its measurements are tightly integrated with IMU, baro-altimeter and range-radio measurements to obtain the platform’s absolute and relative position. When GNSS is not available due to external factors (e.g., obstructions, interference), the position and velocity estimators switch to an integrated solution based on IMU, baro and relative range meas-urements. This solution enables the system to maintain an accurate relative position estimate, and reduce the drift in the swarm’s absolute position estimate as is typical of an IMU-based system. Multiple multi-copter data collection platforms have been developed and equipped with GNSS, inertial sensors and range radios, which were developed at Ohio University. This paper outlines the underlying methodology, the platform hardware components (three multi-copters and one ground station) and analyzes and discusses the performance using both simulation and sUAS flight test data