Robot Tracking of Human Subjects in Field Environments

Future planetary exploration will involve both humans and robots. Understanding and improving their interaction is a main focus of research in the Intelligent Systems Branch at NASA's Johnson Space Center. By teaming intelligent robots with astronauts on surface extra-vehicular activities (EVAs), safety and productivity can be improved. The EVA Robotic Assistant (ERA) project was established to study the issues of human-robot teams, to develop a testbed robot to assist space-suited humans in exploration tasks, and to experimentally determine the effectiveness of an EVA assistant robot. A companion paper discusses the ERA project in general, its history starting with ASRO (Astronaut-Rover project), and the results of recent field tests in Arizona. This paper focuses on one aspect of the research, robot tracking, in greater detail: the software architecture and algorithms. The ERA robot is capable of moving towards and/or continuously following mobile or stationary targets or sequences of targets. The contributions made by this research include how the low-level pose data is assembled, normalized and communicated, how the tracking algorithm was generalized and implemented, and qualitative performance reports from recent field tests

Proposal for the Initiation of General and Military Specific Benchmarking of Robotic Convoys

This paper identifies the need for a standard method of benchmarking emerging robotic systems with a focus on military, multi-robot convoys. Benchmarking is commonly used throughout academia and industry as a method of evaluating and comparing products. In this paper we propose a generic form that these benchmarks may take in the future. Classification categories, such as, obstacle avoidance, area mapping, and convoy coherence are all possible elements of this benchmark. The goal is a standard benchmark that can be used to evaluate military multi-robot convoy systems

Sviluppo di nuovi algoritmi di pianificazione per sistemi non olonomici e con dinamica non linerare

L'obiettivo del kinodynamic motion planning è quello di determinare una sequenza di input di controllo per guidare un agente da uno stato iniziale ad uno finale, rispettando la dinamica del corpo e i vincoli fisici. In questa tesi sono presentate diverse versioni di algoritmi basati su Rapidly-exploring Random Tree in grado di risolvere questo tipo di problema. In particolare è preso in considerazione il caso di un sistema non lineare con vincoli non olonomici, rappresentativo del rover in dotazione al progetto europeo SHERPA. La qualità degli approcci proposti è inoltre provata con alcuni test di navigazione, in ambiente simulato, confrontando gli algoritmi proposti con alcuni presi nella letteratura di riferimento

Navigational algorithms evaluation and benchmarking

One of the fundamental problems in mobile robotics is navigating unexplored environments safely and efficiently. Efforts to address this issue are classified into three categories: reactive-based approaches, which make instantaneous decisions; map-based approaches, involving grid or topological representations; and learning-based approaches. Evaluating and comparing approaches is essential to better understand them, particularly in how they perform in different problem environments and in relation to each other. This information serves to guide the development of further approaches, highlight problem environments, and provide a clear mapping between approaches and environments. However, current comparative studies within a single category have been limited by the existence of a degree of similarity between the different approaches. There has not yet been a comparative framework across different categories in navigational robotics. Thus, this work aims to develop an evaluation method to compare a variety of different approaches in the same environment to achieve a better understanding of navigational algorithms. To this end, a framework has been proposed that simulates these approaches in a common set of problem environments and evaluates them with the same set of metrics to compare their effectiveness and efficiency. The most common reactive and map-based approaches are implemented and a generic, precise, and empirical way to evaluate their performance to the set of environments they are in and compared to the other different approaches is demonstrated. The resulting analysis shows that methods like RRT* don’t improve on the RRT when benchmarked and the evaluation of the problem areas of the Potential Field approach led to the development of the novel Pheromone Potential Field approach. This work opens the doors to more in-depth research into benchmarking across the different navigational categories in static and dynamic environments, which will result in a better understanding and significantly impact the future development of navigational approaches. This research is a step toward dynamic navigational planners that match the different approaches to a set of problems or environments

GeNeSys - sistema de co-evolución genética y neuro-memética para la auto-organización senso-motriz y conductual en una sociedad de robots

Bio-inspired computing can be used to model natural and social systems, including societies with cultural development. Currently, two positions on cultural evolution stand out: with and without replicators. The existence of memes, as cultural replicators, is still hypothetical, and it seems better to look for them in the brain, because they can only be: neuro-memes. In literature there are only two models inspired by the neuro-memetics, and culture evolves side by side with genetics, so it’s necessary to model a gene-culture co-evolution, with neuro-memes. Such a model would be used to help validate the neuro-memetics, on the one hand, and on the other hand, it would help to understand and heal serious problems in human societies. Here, a genetic and neuro-memetic co-evolutionary system was achieved, and a robotic society used it for survive by developing behavioural patterns as a cultural tradition.La computación bio-inspirada puede ser empleada para modelar sistemas naturales y sociales, entre los cuales están las sociedades con desarrollo cultural. En la actualidad, sobresalen dos posturas sobre la evolución cultural: con y sin replicadores. La existencia de memes, como replicadores culturales, es aún hipotética, y parece mejor buscarlos en el cerebro, porque solo pueden ser: neuro-memes. En la literatura hay apenas dos modelos inspirados en la concepción neuro-memética, y como la evolución cultural va de la mano con la genética, se requiere entonces modelar una co-evolución gene-cultura, basada en neuro-memes. Un modelo así, se usaría para ayudar a validar la hipótesis neuro-memética, por un lado, y por el otro, ayudaría a comprender y atender serias problemáticas en las sociedades humanas. Con este proyecto se logró un sistema de co-evolución genética y neuro-memética, que fue usado por una sociedad de robots para sobrevivir, desarrollando un comportamiento cultural.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí