A Dynamical System Approach for Resource-Constrained Mobile Robotics

The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system

Distributed approaches for coverage missions with multiple heterogeneous UAVs for coastal areas.

This Thesis focuses on a high-level framework proposal for heterogeneous aerial, fixed wing teams of robots, which operate in complex coastal areas. Recent advances in the computational capabilities of modern processors along with the decrement of small scale aerial platform manufacturing costs, have given researchers the opportunity to propose efficient and low-cost solutions to a wide variety of problems. Regarding marine sciences and more generally coastal or sea operations, the use of aerial robots brings forth a number of advantages, including information redundancy and operator safety. This Thesis initially deals with complex coastal decomposition in relation with a vehicles’ on-board sensor. This decomposition decreases the computational complexity of planning a flight path, while respecting various aerial or ground restrictions. The sensor-based area decomposition also facilitates a team-wide heterogeneous solution for any team of aerial vehicles. Then, it proposes a novel algorithmic approach of partitioning any given complex area, for an arbitrary number of Unmanned Aerial Vehicles (UAV). This partitioning schema, respects the relative flight autonomy capabilities of the robots, providing them a corresponding region of interest. In addition, a set of algorithms is proposed for obtaining coverage waypoint plans for those areas. These algorithms are designed to afford the non-holonomic nature of fixed-wing vehicles and the restrictions their dynamics impose. Moreover, this Thesis also proposes a variation of a well-known path tracking algorithm, in order to further reduce the flight error of waypoint following, by introducing intermediate waypoints and providing an autopilot parametrisation. Finally, a marine studies test case of buoy information extraction is presented, demonstrating in that manner the flexibility and modular nature of the proposed framework.Esta tesis se centra en la propuesta de un marco de alto nivel para equipos heterogéneos de robots de ala fija que operan en áreas costeras complejas. Los avances recientes en las capacidades computacionales de los procesadores modernos, junto con la disminución de los costes de fabricación de plataformas aéreas a pequeña escala, han brindado a los investigadores la oportunidad de proponer soluciones eficientes y de bajo coste para enfrentar un amplio abanico de cuestiones. Con respecto a las ciencias marinas y, en términos más generales, a las operaciones costeras o marítimas, el uso de robots aéreos conlleva una serie de ventajas, incluidas la redundancia de la información y la seguridad del operador. Esta tesis trata inicialmente con la descomposición de áreas costeras complejas en relación con el sensor a bordo de un vehículo. Esta descomposición disminuye la complejidad computacional de la planificación de una trayectoria de vuelo, al tiempo que respeta varias restricciones aéreas o terrestres. La descomposición del área basada en sensores también facilita una solución heterogénea para todo el equipo para cualquier equipo de vehículos aéreos. Luego, propone un novedoso enfoque algorítmico de partición de cualquier área compleja dada, para un número arbitrario de vehículos aéreos no tripulados (UAV). Este esquema de partición respeta las capacidades relativas de autonomía de vuelo de los robots, proporcionándoles una región de interés correspondiente. Además, se propone un conjunto de algoritmos para obtener planes de puntos de cobertura para esas áreas. Estos algoritmos están diseñados teniendo en cuenta la naturaleza no holonómica de los vehículos de ala fija y las restricciones que impone su dinámica. En ese sentido, esta Tesis también ofrece una variación de un algoritmo de seguimiento de rutas bien conocido, con el fin de reducir aún más el error de vuelo del siguiente punto de recorrido, introduciendo puntos intermedios y proporcionando una parametrización del piloto automático. Finalmente, se presenta un caso de prueba de estudios marinos de extracción de información de boyas, que demuestra de esa manera la flexibilidad y el carácter modular del marco propuesto

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

A survey on multi-robot coverage path planning for model reconstruction and mapping

There has been an increasing interest in researching, developing and deploying multi-robot systems. This has been driven mainly by: the maturity of the practical deployment of a single-robot system and its ability to solve some of the most challenging tasks. Coverage path planning (CPP) is one of the active research topics that could benefit greatly from multi-robot systems. In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions. We classified the topics into: viewpoints generation approaches; coverage planning strategies; coordination and decision-making processes; communication mechanism and mapping approaches. This paper provides a detailed analysis and comparison of the recent research work in this area, and concludes with a critical analysis of the field, and future research perspectives

Wide-Area Surveillance System using a UAV Helicopter Interceptor and Sensor Placement Planning Techniques

This project proposes and describes the implementation of a wide-area surveillance system comprised of a sensor/interceptor placement planning and an interceptor unmanned aerial vehicle (UAV) helicopter. Given the 2-D layout of an area, the planning system optimally places perimeter cameras based on maximum coverage and minimal cost. Part of this planning system includes the MATLAB implementation of Erdem and Sclaroff’s Radial Sweep algorithm for visibility polygon generation. Additionally, 2-D camera modeling is proposed for both fixed and PTZ cases. Finally, the interceptor is also placed to minimize shortest-path flight time to any point on the perimeter during a detection event. Secondly, a basic flight control system for the UAV helicopter is designed and implemented. The flight control system’s primary goal is to hover the helicopter in place when a human operator holds an automatic-flight switch. This system represents the first step in a complete waypoint-navigation flight control system. The flight control system is based on an inertial measurement unit (IMU) and a proportional-integral-derivative (PID) controller. This system is implemented using a general-purpose personal computer (GPPC) running Windows XP and other commercial off-the-shelf (COTS) hardware. This setup differs from other helicopter control systems which typically use custom embedded solutions or micro-controllers. Experiments demonstrate the sensor placement planning achieving \u3e90% coverage at optimized-cost for several typical areas given multiple camera types and parameters. Furthermore, the helicopter flight control system experiments achieve hovering success over short flight periods. However, the final conclusion is that the COTS IMU is insufficient for high-speed, high-frequency applications such as a helicopter control system

Design of a protocol for event-based network reconfiguration of active vision systems

Projecte final de carrera fet en col.laboració amb Leibniz Universtät HannoverCatalà: Avui en dia la vigilancia de grans àreas, com ara bancs, aeroports o ciutats es basa principalment en sistemas de video. Les Active Cameras (ACs) juguen un paper important per als sistemes de seguretat, ja que combinen video detecció, processament de video i comunicació en un sol dispositiu. Un punt feble és que les ACs son generalment fixes i poden apareixer oclusions que poden crear punts cecs al sistema. Aquests punts cecs poden ser superats mitjançant l?ús de ACs mòbils crean una xarxa mòbil, anomenada Active Camera Network (ACN), presentades en aquesta tesi. No obstant això, la mobilitat de les ACs ve juntament amb desafiaments en termes de coordinació i configuración. A més d?això, el cost de les ACs es més gran en comparació a les xarxes de cameres estatiques, però el nombre de guàrdies necessaris per inspeccionar una gran àrea com una ciutat per exemple o per controlar una gran quantitat de monitors es pot reduir considerablement amb les ACNs. El nostre objectiu és implementar l?arquitectura de un sistema auto-reconfigurable per una xarxa de ACs que poden anar muntades en robots mòbils pel terra o en microvehicles aeris (MAV). Així, les ACs decidirán per si mateixes on actualizar la seva posición per tal d?aconseguir un rendiment òptim del sistema. Per assolir aquest objectiu, les ACs aumentaran o disminuiran les regions espacials redundants amb el seus veïns fent focus en les regions mes sobrecarregades. El protocol presentat en aquesta tesi adapta la posición de les ACs per detectar les diferents trajectories que travessan la zona de vigilancia i que poden evolucionar amb el temps. Les simulacions han demostrat que el protocol presentat augmenta el rendiment general del sistema fins un 190% més gràcies a la reconfiguració i cooperación entre les ACs veïnes.Castellano: Hoy en día la vigilancia de grandes áreas, tales como bancos, aeropuertos o ciudades se basa principalmente en sistemas de video vigilancia. Las Active Cameras (ACs) juegan un papel importante para los sistemas de seguridad, ya que combinan video detección, procesamiento de video y comunicación en un solo dispositivo. Un punto débil es que las ACs son generalmente fijas y pueden aparecer oclusiones que creen puntos ciegos en el sistema. Estos puntos ciegos pueden ser superados mediante el uso de ACs móviles creando una red móvil, llamada Active Camera Network (ACN), presentadas en esta tesis. Sin embargo, la movilidad de las ACs viene junto con desafíos en términos de coordinación y configuración. Además de esto, el coste de las ACs es mayor en comparación a las redes de cámaras estáticas, pero el número de guardias necesarios para inspeccionar una gran área como una ciudad por ejemplo o para controlar una gran cantidad de monitores se puede reducir considerablemente con las ACNs. Nuestro objetivo es implementar la arquitectura de un sistema auto-reconfigurable para una red de ACs que pueden ir montadas en robots móviles por el suelo o en micro vehículos aéreos (MAV). Así, las ACs decidirán por sí mismas donde actualizar sus posiciones con el fin de conseguir un rendimiento óptimo del sistema. Para alcanzar este objetivo, las ACs aumentarán o disminuirán las regiones espaciales redundantes con sus vecinos haciendo foco en las regiones más sobrecargadas. El protocolo presentado en esta tesis adapta la posición de las ACs para detectar las diferentes trayectorias que atraviesan la zona de vigilancia y que pueden evolucionar con el tiempo. Las simulaciones han demostrado que el protocolo presentado aumenta el rendimiento general del sistema hasta un 190% más gracias a la reconfiguración y cooperación entre las ACs vecinas.English: Nowadays surveillance of large areas, such as banks, airports or cities is mostly based on vision systems. Smart Cameras (SCs) play an important role for security systems as they combine video sensing, video processing and communication within a single device. One weak point is that SCs are usually stationary and so occlusions may create blind spots in the system. These blind spots may be overcome by using mobile SCs, so called Active Camera Networks (ACNs), as introduced in this thesis. Nevertheless, mobility of SCs come along with challenges in terms of coordination and configuration. In addition to this, the cost of ACs is higher in comparison to static camera networks but the number of guards needed to survey a large area like a city or to control a lot of monitors can be reduced considerably with AC networks. Our goal is to implement a self-reconfiguration system architecture for networked smart cameras that could be mounted either on mobile robots on the ground or Micro Air Vehicles (MAVs). Thus, the ACs will decide by themselves where to update their position in order to achieve the optimal system's performance. To reach that goal, ACs will increase or decrease spatial redundancy regions with their neighbours to overcome overloaded regions. The protocol presented in this thesis adapts the position of the ACN to the different trajectories that traverse a surveillance area over time. The simulations have shown that the presented protocol increase the overall performance due to the node reconfiguration and cooperation between neighbouring ACs

Optimal steering for kinematic vehicles with applications to spatially distributed agents

The recent technological advances in the field of autonomous vehicles have resulted in a growing impetus for researchers to improve the current framework of mission planning and execution within both the military and civilian contexts. Many recent efforts towards this direction emphasize the importance of replacing the so-called monolithic paradigm, where a mission is planned, monitored, and controlled by a unique global decision maker, with a network centric paradigm, where the same mission related tasks are performed by networks of interacting decision makers (autonomous vehicles). The interest in applications involving teams of autonomous vehicles is expected to significantly grow in the near future as new paradigms for their use are constantly being proposed for a diverse spectrum of real world applications. One promising approach to extend available techniques for addressing problems involving a single autonomous vehicle to those involving teams of autonomous vehicles is to use the concept of Voronoi diagram as a means for reducing the complexity of the multi-vehicle problem. In particular, the Voronoi diagram provides a spatial partition of the environment the team of vehicles operate in, where each element of this partition is associated with a unique vehicle from the team. The partition induces, in turn, a graph abstraction of the operating space that is in a one-to-one correspondence with the network abstraction of the team of autonomous vehicles; a fact that can provide both conceptual and analytical advantages during mission planning and execution. In this dissertation, we propose the use of a new class of Voronoi-like partitioning schemes with respect to state-dependent proximity (pseudo-) metrics rather than the Euclidean distance or other generalized distance functions, which are typically used in the literature. An important nuance here is that, in contrast to the Euclidean distance, state-dependent metrics can succinctly capture system theoretic features of each vehicle from the team (e.g., vehicle kinematics), as well as the environment-vehicle interactions, which are induced, for example, by local winds/currents. We subsequently illustrate how the proposed concept of state-dependent Voronoi-like partition can induce local control schemes for problems involving networks of spatially distributed autonomous vehicles by examining different application scenarios.PhDCommittee Chair: Tsiotras Panagiotis; Committee Member: Egerstedt Magnus; Committee Member: Feron Eric; Committee Member: Haddad Wassim; Committee Member: Shamma Jef

Dynamic Coverage Control and Estimation in Collaborative Networks of Human-Aerial/Space Co-Robots

In this dissertation, the author presents a set of control, estimation, and decision making strategies to enable small unmanned aircraft systems and free-flying space robots to act as intelligent mobile wireless sensor networks. These agents are primarily tasked with gathering information from their environments in order to increase the situational awareness of both the network as well as human collaborators. This information is gathered through an abstract sensing model, a forward facing anisotropic spherical sector, which can be generalized to various sensing models through adjustment of its tuning parameters. First, a hybrid control strategy is derived whereby a team of unmanned aerial vehicles can dynamically cover (i.e., sweep their sensing footprints through all points of a domain over time) a designated airspace. These vehicles are assumed to have finite power resources; therefore, an agent deployment and scheduling protocol is proposed that allows for agents to return periodically to a charging station while covering the environment. Rules are also prescribed with respect to energy-aware domain partitioning and agent waypoint selection so as to distribute the coverage load across the network with increased priority on those agents whose remaining power supply is larger. This work is extended to consider the coverage of 2D manifolds embedded in 3D space that are subject to collision by stochastic intruders. Formal guarantees are provided with respect to collision avoidance, timely convergence upon charging stations, and timely interception of intruders by friendly agents. This chapter concludes with a case study in which a human acts as a dynamic coverage supervisor, i.e., they use hand gestures so as to direct the selection of regions which ought to be surveyed by the robot. Second, the concept of situational awareness is extended to networks consisting of humans working in close proximity with aerial or space robots. In this work, the robot acts as an assistant to a human attempting to complete a set of interdependent and spatially separated multitasking objectives. The human wears an augmented reality display and the robot must learn the human's task locations online and broadcast camera views of these tasks to the human. The locations of tasks are learned using a parallel implementation of expectation maximization of Gaussian mixture models. The selection of tasks from this learned set is executed by a Markov Decision Process which is trained using Q-learning by the human. This method for robot task selection is compared against a supervised method in IRB approved (HUM00145810) experimental trials with 24 human subjects. This dissertation concludes by discussing an additional case study, by the author, in Bayesian inferred path planning. In addition, open problems in dynamic coverage and human-robot interaction are discussed so as to present an avenue forward for future work.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155147/1/wbentz_1.pd