Swarm Technologies For Future Space Exploration Missions

Modern robotic platforms for in-situ space exploration are single-robots equipped with a number of specialized sensors providing scientists with unique information about a planet's surface. However, there is a number of exploration problems where large spatial apertures of the exploration system are necessary, requiring a completely new perspective on in-situ space exploration and it's required technologies. Large networks of robots, called swarm, pave the way: agents in a swarm span ad-hoc communication networks, localize themselves based on radio signals, share resources, process data and make inference over the network in a decentralized fashion. By cooperation, local information collected by agents becomes globally available. In this work we present our recent results in development of swarm technologies for future in-situ space exploration missions: a wireless system jointly used for communication and localization, and swarm navigation and exploration strategies to sample and reconstruct static spatial fields

Autonomous Swarm Navigation

Robotic swarm systems attract increasing attention in a wide variety of applications, where a multitude of self-organized robotic entities collectively accomplish sensing or exploration tasks. Compared to a single robot, a swarm system offers advantages in terms of exploration speed, robustness against single point of failures, and collective observations of spatio-temporal processes. Autonomous swarm navigation, including swarm self-localization, the localization of external sources, and swarm control, is essential for the success of an autonomous swarm application. However, as a newly emerging technology, a thorough study of autonomous swarm navigation is still missing. In this thesis, we systematically study swarm navigation systems, particularly emphasizing on their collective performance. The general theory of swarm navigation as well as an in-depth study on a specific swarm navigation system proposed for future Mars exploration missions are covered. Concerning swarm localization, a decentralized algorithm is proposed, which achieves a near-optimal performance with low complexity for a dense swarm network. Regarding swarm control, a position-aware swarm control concept is proposed. The swarm is aware of not only the position estimates and the estimation uncertainties of itself and the sources, but also the potential motions to enrich position information. As a result, the swarm actively adapts its formation to improve localization performance, without losing track of other objectives, such as goal approaching and collision avoidance. The autonomous swarm navigation concept described in this thesis is verified for a specific Mars swarm exploration system. More importantly, this concept is generally adaptable to an extensive range of swarm applications

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

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

Aerial base station placement in temporary-event scenarios

Die Anforderungen an den Netzdatenverkehr sind in den letzten Jahren dramatisch gestiegen, was ein großes Interesse an der Entwicklung neuartiger Lösungen zur Erhöhung der Netzkapazität in Mobilfunknetzen erzeugt hat. Besonderes Augenmerk wurde auf das Problem der Kapazitätsverbesserung bei temporären Veranstaltungen gelegt, bei denen das Umfeld im Wesentlichen dynamisch ist. Um der Dynamik der sich verändernden Umgebung gerecht zu werden und die Bodeninfrastruktur durch zusätzliche Kapazität zu unterstützen, wurde der Einsatz von Luftbasisstationen vorgeschlagen. Die Luftbasisstationen können in der Nähe des Nutzers platziert werden und aufgrund der im Vergleich zur Bodeninfrastruktur höheren Lage die Vorteile der Sichtlinienkommunikation nutzen. Dies reduziert den Pfadverlust und ermöglicht eine höhere Kanalkapazität. Das Optimierungsproblem der Maximierung der Netzkapazität durch die richtige Platzierung von Luftbasisstationen bildet einen Schwerpunkt der Arbeit. Es ist notwendig, das Optimierungsproblem rechtzeitig zu lösen, um auf Veränderungen in der dynamischen Funkumgebung zu reagieren. Die optimale Platzierung von Luftbasisstationen stellt jedoch ein NP-schweres Problem dar, wodurch die Lösung nicht trivial ist. Daher besteht ein Bedarf an schnellen und skalierbaren Optimierungsalgorithmen. Als Erstes wird ein neuartiger Hybrid-Algorithmus (Projected Clustering) vorgeschlagen, der mehrere Lösungen auf der Grundlage der schnellen entfernungsbasierten Kapazitätsapproximierung berechnet und sie auf dem genauen SINR-basierten Kapazitätsmodell bewertet. Dabei werden suboptimale Lösungen vermieden. Als Zweites wird ein neuartiges verteiltes, selbstorganisiertes Framework (AIDA) vorgeschlagen, welches nur lokales Wissen verwendet, den Netzwerkmehraufwand verringert und die Anforderungen an die Kommunikation zwischen Luftbasisstationen lockert. Bei der Formulierung des Platzierungsproblems konnte festgestellt werden, dass Unsicherheiten in Bezug auf die Modellierung der Luft-Bodensignalausbreitung bestehen. Da dieser Aspekt im Rahmen der Analyse eine wichtige Rolle spielt, erfolgte eine Validierung moderner Luft-Bodensignalausbreitungsmodelle, indem reale Messungen gesammelt und das genaueste Modell für die Simulationen ausgewählt wurden.As the traffic demands have grown dramatically in recent years, so has the interest in developing novel solutions that increase the network capacity in cellular networks. The problem of capacity improvement is even more complex when applied to a dynamic environment during a disaster or temporary event. The use of aerial base stations has received much attention in the last ten years as the solution to cope with the dynamics of the changing environment and to supplement the ground infrastructure with extra capacity. Due to higher elevations and possibility to place aerial base stations in close proximity to the user, path loss is significantly smaller in comparison to the ground infrastructure, which in turn enables high data capacity. We are studying the optimization problem of maximizing network capacity by proper placement of aerial base stations. To handle the changes in the dynamic radio environment, it is necessary to promptly solve the optimization problem. However, we show that the optimal placement of aerial base stations is the NP-hard problem and its solution is non-trivial, and thus, there is a need for fast and scalable optimization algorithms. This dissertation investigates how to solve the placement problem efficiently and to support the dynamics of temporary events. First, we propose a novel hybrid algorithm (Projected Clustering), which calculates multiple solutions based on the fast distance-based capacity approximation and evaluates them on the accurate SINR-based capacity model, avoiding sub-optimal solutions. Second, we propose a novel distributed, self-organized framework (AIDA), which conducts a decision-making process using only local knowledge, decreasing the network overhead and relaxing the requirements for communication between aerial base stations. During the formulation of the placement problem, we found that there is still considerable uncertainty with regard to air-to-ground propagation modeling. Since this aspect plays an important role in our analysis, we validated state-of-the-art air-to-ground propagation models by collecting real measurements and chose the most accurate model for the simulations

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available