Decision Making for Teams of Mobile Robots

In the past years, we could observe a significant amount of new robotic systems in science, industry, and everyday life. To reduce the complexity of these systems, the industry constructs robots that are designated for the execution of a specific task such as vacuum cleaning, autonomous driving, observation, or transportation operations. As a result, such robotic systems need to combine their capabilities to accomplish complex tasks that exceed the abilities of individual robots. However, to achieve emergent cooperative behavior, multi-robot systems require a decision process that copes with the communication challenges of the application domain. This work investigates a distributed multi-robot decision process, which addresses unreliable and transient communication. This process composed by five steps, which we embedded into the ALICA multi-agent coordination language guided by the PROViDE negotiation middleware. The first step encompasses the specification of the decision problem, which is an integral part of the ALICA implementation. In our decision process, we describe multi-robot problems by continuous nonlinear constraint satisfaction problems. The second step addresses the calculation of solution proposals for this problem specification. Here, we propose an efficient solution algorithm that integrates incomplete local search and interval propagation techniques into a satisfiability solver, which forms a satisfiability modulo theories (SMT) solver. In the third decision step, the PROViDE middleware replicates the solution proposals among the robots. This replication process is parameterized with a distribution method, which determines the consistency properties of the proposals. In a fourth step, we investigate the conflict resolution. Therefore, an acceptance method ensures that each robot supports one of the replicated proposals. As we integrated the conflict resolution into the replication process, a sound selection of the distribution and acceptance methods leads to an eventual convergence of the robot proposals. In order to avoid the execution of conflicting proposals, the last step comprises a decision method, which selects a proposal for implementation in case the conflict resolution fails. The evaluation of our work shows that the usage of incomplete solution techniques of the constraint satisfaction solver outperforms the runtime of other state-of-the-art approaches for many typical robotic problems. We further show by experimental setups and practical application in the RoboCup environment that our decision process is suitable for making quick decisions in the presence of packet loss and delay. Moreover, PROViDE requires less memory and bandwidth compared to other state-of-the-art middleware approaches.

Hierarchical concept of optimization based path planning for autonomous driving

Otto-von Guericke-Universität Magdeburg, Fakultät für Maschinenbau, Dissertation, 2016

Autonomous Vehicles and Commercial Real Estate

Hardly a day goes by without the release of a handful of news stories about autonomous vehicles (or AVs for short). The proverbial “tipping point” of awareness has been reached in the public consciousness as AV technology is quickly becoming the new focus of firms from Silicon Valley to Detroit and beyond. Automation has, and will continue to have far-reaching implications for many human activities, but for driving, the technology is here. Google has been in talks with automaker Ford (1), Elon Musk has declared that Tesla will have the appropriate technology in two years (2), GM is paired-up with Lyft (3), Uber is in development-mode (4), Microsoft and Volvo have announced a partnership (5), Apple has been piloting its top-secret project “Titan” (6), Toyota is working on its own technology (7), as is BMW (8). Audi (9) made a splash by sending a driverless A7 concept car 550 miles from San Francisco to Las Vegas just in time to roll-into the 2016 Consumer Electronics Show. Clearly, the race is on.

A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

This paper outlines the development of a crosscorrelation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation.

Detección y evitación de objetos flotantes en el contexto de un vehículo autónomo de superficie

[ES]El Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería y en especial la División de Robótica y Oceanografía Computacional está desarrollando un velero autónomo de superficie que requiere de un sistema para la detección y evasión de obstáculos. Dicho sistema se ha desarrollado sobre una Raspberry Pi con un servicio para la captura de imágenes, así como un servidor web que permita la modificación de la configuración de la cámara. Una vez completada dicha infraestructura se tomaron las fotografías que conformarán el conjunto de entrenamiento para el sistema de visión por computador y se desarrollará este último. Los resultados se han integrado con el sistema del control modificando el rumbo cuando se detecte un obstáculo.

Law, poetry and medicine: the literate professionals in autonomous Gaelic Ireland, c.1250 - c.1630

This thesis is about a class of literate professionals that served as hereditary brehons, poets and doctors to the Gaelic aristocracy over a period from c.1250-c.1630. My investigation into these families brings together evidence from Gaelic and English sources to highlight the work these families did for their patrons, their status in society and their subsequent fall in the seventeenth century. Such a broad canvas allows us to observe the vibrancy of Gaelic literary culture as these families adapted to the changing political landscape to absorb new Anglo-Norman patrons and assimilated English and Continental ideas while maintaining their distinctive identity. I want to look beyond the ideology espoused by these families to look at the practical choices members of these families made to maintain their status and relevance in a changing social context. To do this I have chosen to focus on each of the three professions in individual chapters to highlight the continuities and changes within the professions and ultimately by comparing the three groups to gauge the success or failure of these professional families to adapt to the encroachment of the New English and the ultimate collapse of the Gaelic world. This thesis takes a holistic approach to these families by including branches of these families not engaged in the hereditary profession. It seeks to provide a broader picture of Gaelic society below the level of the aristocracy by looking at the geographic distribution of these families, their proximity to centres of power, and to land and sea routes that can indicate their involvement in alternative economic activities.

Navegação de robô móvel em ambiente com humanos

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

Using augmented state Kalman filter to localize multi autonomous underwater vehicles

The present paper describes a system for the construction of visual maps ("mosaics") and motion estimation for a set of AUVs (Autonomous Underwater Vehicles). Robots are equipped with down-looking camera which is used to estimate their motion with respect to the seafloor and built an online mosaic. As the mosaic increases in size, a systematic bias is introduced in its alignment, resulting in an erroneous output. The theoretical concepts associated with the use of an Augmented State Kalman Filter (ASKF) were applied to optimally estimate both visual map and the fleet position.

A survey of control architectures for autonomous mobile robots

MEDEIROS, Adelardo A. D.A survey of control architectures for autonomous mobile robots. J. Braz. Comp. Soc., Campinas, v. 4, n. 3, abr. 1998 .Disponível em: Acesso: 27 set. 2010.

Multirobot Tethering for Localization and Control

Particle filtering has proven to be an effective localization method for wheeled autonomous vehicles. For a given map, a sensor model, and observations, occasions arise where the vehicle could equally likely be in many locations of the map. Because particle filtering algorithms may generate low confidence pose estimates under these conditions, more robust localization strategies are required to produce reliable pose estimates. This becomes more critical if the state estimate is an integral part of system control. We investigate the use of particle filter estimation techniques on a hovercraft vehicle. The marginally stable dynamics of a hovercraft require reliable state estimates for proper stability and control. We use the Monte Carlo localization method, which implements a particle filter in a recursive state estimate algorithm. An H-infinity controller, designed to accommodate the latency inherent in our state estimation, provides stability and controllability to the hovercraft. In order to eliminate the low confidence estimates produced in certain environments, a multirobot system is designed to introduce mobile environment features. By tracking and controlling the secondary robot, we can position the mobile feature throughout the environment to ensure a high confidence estimate, thus maintaining stability in the system. A laser rangefinder is the sensor the hovercraft uses to track the secondary robot, observe the environment, and facilitate successful localization and stability in motion.

Predicting the metabolic energy costs of bipedalism using evolutionary robotics

To understand the evolution of bipedalism among the homnoids in an ecological context we need to be able to estimate theenerrgetic cost of locomotion in fossil forms. Ideally such an estimate would be based entirely on morphology since, except for the rare instances where footprints are preserved, this is hte only primary source of evidence available. In this paper we use evolutionary robotics techniques (genetic algoritms, pattern generators and mechanical modeling) to produce a biomimentic simulation of bipedalism based on human body dimensions. The mechnaical simulation is a seven-segment, two-dimensional model with motive force provided by tension generators representing the major muscle groups acting around the lower-limb joints. Metabolic energy costs are calculated from the muscel model, and bipedal gait is generated using a finite-state pattern generator whose parameters are produced using a genetic algorithm with locomotor economy (maximum distance for a fixed energy cost) as the fitness criterion. The model is validated by comparing the values it generates with those for modern humans. The result (maximum efficiency of 200 J m-1) is within 15% of the experimentally derived value, which is very encouraging and suggests that this is a useful analytic technique for investigating the locomotor behaviour of fossil forms. Initial work suggests that in the future this technique could be used to estimate other locomotor parameters such as top speed. In addition, the animations produced by this technique are qualitatively very convincing, which suggests that this may also be a useful technique for visualizing bipedal locomotion.

CONCURRENT LOCALIZATION AND MAPPING WITH SONAR SENSORS AND CONSIDERATION OF VEHICLE MOTION

Simultaneous Localization and Mapping (SLAM) is a procedure used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. A typical SLAM consists of four main components, namely, experimental setup (data gathering), vehicle pose estimation, feature extraction, and filtering. Feature extraction is the process of realizing significant features from the unknown environment such as corners, edges, walls, and interior features. In this work, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been constructed by combining the SONAR Salient Feature Extraction Algorithm and the Triangulation Hough Based Fusion with point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared to the maps obtained with existing feature extraction algorithms. Based on the results obtained, it is suggested that the proposed algorithm can be employed as an option for data obtained from SONAR sensors in environment, where other forms of sensing are not viable. The algorithm fusion for feature extraction requires the vehicle pose estimation as an input, which is obtained from a vehicle pose estimation model. For the vehicle pose estimation, the author uses sensor integration to estimate the pose of the mobile vehicle. Different combinations of these sensors are studied (e.g., encoder, gyroscope, or encoder and gyroscope). The different sensor fusion techniques for the pose estimation are experimentally studied and compared. The vehicle pose estimation model, which produces the least amount of error, is used to generate inputs for the feature extraction algorithm fusion. In the experimental studies, two different environmental configurations are used, one without interior features and another one with two interior features. Numerical and experimental findings are discussed. Finally, the SLAM algorithm is implemented along with the algorithms for feature extraction and vehicle pose estimation. Three different cases are experimentally studied, with the floor of the environment intentionally altered to induce slipping. Results obtained for implementations with and without SLAM are compared and discussed. The present work represents a step towards the realization of autonomous inspection platforms for performing concurrent localization and mapping in harsh environments.

Integrated micro fuel cells with on-board hydride reactors and autonomous control schemes

Miniaturization of power generators to the MEMS scale, based on the hydrogen-air fuel cell, is the object of this research. The micro fuel cell approach has been adopted for advantages of both high power and energy densities. On-board hydrogen production/storage and an efficient control scheme that facilitates integration with a fuel cell membrane electrode assembly (MEA) are key elements for micro energy conversion. Millimeter-scale reactors (ca. 10 µL) have been developed, for hydrogen production through hydrolysis of CaH2 and LiAlH4, to yield volumetric energy densities of the order of 200 Whr/L. Passive microfluidic control schemes have been implemented in order to facilitate delivery, self-regulation, and at the same time eliminate bulky auxiliaries that run on parasitic power. One technique uses surface tension to pump water in a microchannel for hydrolysis and is self-regulated, based on load, by back pressure from accumulated hydrogen acting on a gas-liquid microvalve. This control scheme improves uniformity of power delivery during long periods of lower power demand, with fast switching to mass transport regime on the order of seconds, thus providing peak power density of up to 391.85 W/L. Another method takes advantage of water recovery by backward transport through the MEA, of water vapor that is generated at the cathode half-cell reaction. This regulation-free scheme increases available reactor volume to yield energy density of 313 Whr/L, and provides peak power density of 104 W/L. Prototype devices have been tested for a range of duty periods from 2-24 hours, with multiple switching of power demand in order to establish operation across multiple regimes. Issues identified as critical to the realization of the integrated power MEMS include effects of water transport and byproduct hydrate swelling on hydrogen production in the micro reactor, and ambient relative humidity on fuel cell performance.