Navegação em ambientes dinâmicos tirando partido de agentes móveis

Doutoramento em Engenharia MecânicaEsta tese propõe uma forma diferente de navegação de robôs em ambientes dinâmicos, onde o robô tira partido do movimento de pedestres, com o objetivo de melhorar as suas capacidades de navegação. A ideia principal é que, ao invés de tratar as pessoas como obstáculos dinâmicos que devem ser evitados, elas devem ser tratadas como agentes especiais com conhecimento avançado em navegação em ambientes dinâmicos. Para se beneficiar do movimento de pedestres, este trabalho propõe que um robô os selecione e siga, de modo que possa mover-se por caminhos ótimos, desviar-se de obstáculos não detetados, melhorar a navegação em ambientes densamente populados e aumentar a sua aceitação por outros humanos. Para atingir estes objetivos, novos métodos são desenvolvidos na área da seleção de líderes, onde duas técnicas são exploradas. A primeira usa métodos de previsão de movimento, enquanto a segunda usa técnicas de aprendizagem por máquina, para avaliar a qualidade de candidatos a líder, onde o treino é feito com exemplos reais. Os métodos de seleção de líder são integrados com algoritmos de planeamento de movimento e experiências são realizadas para validar as técnicas propostas.This thesis proposes a di erent form of robotic navigation in dynamic environments, where the robot takes advantage of the motion of pedestrians, in order to improve its own navigation capabilities. The main idea is that, instead of treating persons as dynamic obstacles that should be avoided, they should be treated as special agents with an expert knowledge of navigating in dynamic scenarios. To bene t from the motion of pedestrians, this work proposes that the robot selects and follows them, so it can move along optimal paths, deviate from undetected obstacles, improve navigation in densely populated areas and increase its acceptance by other humans. To accomplish this proposition, novel approaches are developed in the area of leader selection, where two methods are explored. The rst uses motion prediction approaches while the second uses a machine learning method, to evaluate the leader quality of subjects, which is trained with real examples. Finally, the leader selection methods are integrated with motion planning algorithms and experiments are conducted in order to validate the proposed techniques

Framework for visual guidance of an autonomous robot using learning

Mestrado em Engenharia de Automação IndustrialEste documento apresenta os trabalhos de desenvolvimento de uma infraestrutura de aprendizagem para a condução de robôs móveis. Este método de aprendizagem utiliza redes neuronais artificias para calcular uma direcção capaz de manter um robô dentro de uma estrada. A rede "aprende"a calcular esta direcção baseada em exemplos de condutores humanos, replicando e, de uma certa forma, imitando comportamentos. Uma abordagem de aprendizagem pode superar alguns aspectos de algoritmos clássicos para o cálculo da direcção de um robot. No que se relaciona à velocidade de processamento, as redes neuronais artificiais são muito rápidas, o que as torna ideais para navegação em tempo real. Além disso as redes tem a capacidade de extrair informações que não foram detectadas por humanos e, por conseguinte, não podem ser codificadas em programas clássicos. A implementação desta nova forma de interacção entre humanos e robôs, que estão simultaneamente "ensinando"e "aprendendo", também vai ser destacada neste trabalho. A plataforma de testes utilizada nesta investigação será um robô do Projecto Atlas, desenvolvido como robô autónomo de competição, para participar da prova de Condução Autónoma que ocorre anualmente como parte do Festival Nacional de Robótica. Para transformar o robô numa plataforma robusta para testes, uma série de revisões e melhorias foram implementadas. Estas intervenções foram conduzidas a nível mecânico e electrónico, e também a nível de software, sendo este último de grande importância por estabelecer uma nova infraestrutura de desenvolvimento e programação para investigadores. ABSTRACT: This work describes the research and development of a learning infrastructure for mobile robot driving. This method uses artificial neural networks to compute the steer direction that a robot should perform to stay inside a track. The network "learns" to compute a direction based on examples from human drivers, replicating and sometimes even improving human-like behaviors. A learning approach can overcome some aspects of classical algorithms used for robot steering computation. Regarding the processing speed, artificial neural networks are very fast, which make them well suited for real-time navigation. They also have the possibility to perceive information that was undetected by humans and therefore could not be coded in classical programs. The implementation of this new form of interaction between humans and robots, that are simultaneously "teaching" and "learning" from each other, will also be emphasized in this work. The platform used for this research is one of the robots of the Project Atlas, designed as an autonomous robot to participate in the Autonomous Driving Competition, held annually as part of the Portuguese Robotics Open. To render this robot able to serve as a robust test platform, several revisions and improvements were conducted. These interventions were conducted at mechanical, electronic and software levels, with the latter having a big importance as it establishes a new framework for group and modular code development

On Leader Following and Classification

International audienceService and assistance robots that must move in human environment must address the difficult issue of navigating in dynamic environments. As it has been shown in previous works, in such situations the robots can take advantage of the motion of persons by following them, managing to move together with humans in difficult situations. In those circumstances, the problem to be solved is how to choose a human leader to be followed. This work proposes an innovative method for leader selection, based on human experience. A learning framework is developed, where data is acquired, labeled and then used to train an AdaBoost classification algorithm, to determine if a candidate is a bad or a good leader, and also to study the contribution of features to the classification process

Experiments in Leader Classification and Following with an Autonomous Wheelchair

International audienceWith decreasing costs in robotic platforms, mobile robots that provide assistance to humans are becoming a reality. A key requirement for these types of robots is the ability to efficiently and safely navigate in populated environments. This work proposes to address this issue by studying how robots can select and follow human leaders, to take advantage of their motion in complex situations. To accomplish this, a machine learning framework is proposed, comprising data acquisition with a real robot, data labeling, feature extraction and the training of a leader classifier. Preliminary experiments combined the classification system with a multi-mode navigation algorithm, to validate this approach using an autonomous wheelchair

Experiments in Leader Classification and Following with an Autonomous Wheelchair

International audienceWith decreasing costs in robotic platforms, mobile robots that provide assistance to humans are becoming a reality. A key requirement for these types of robots is the ability to efficiently and safely navigate in populated environments. This work proposes to address this issue by studying how robots can select and follow human leaders, to take advantage of their motion in complex situations. To accomplish this, a machine learning framework is proposed, comprising data acquisition with a real robot, data labeling, feature extraction and the training of a leader classifier. Preliminary experiments combined the classification system with a multi-mode navigation algorithm, to validate this approach using an autonomous wheelchair

Integration of ADAS algorithm in a Vehicle Prototype

International audienceFor several years, INRIA and Toyota Europe have been working together in the development of algorithms directed to ADAS. This paper will describe the main results of this successful joint project, applied to a prototype vehicle equipped with several sensors. This work will detail the framework, steps taken and motivation behind the developed technologies, as well as address the requirements needed for the automobile industry

Human Aware Navigation for Assistive Robotics

The original publication is available at www.springerlink.comInternational audienceEnsuring proper living conditions for an ever growing number of elderly people is a significative challenge for many countries. The difficulty and cost of hiring and training specialized personnel has fostered research in assistive robotics as a viable alternative. In this context, an ideally suited and very relevant application is to transport people with reduced mobility. This may involve either autonomous or semi-autonomous transportation devices such as cars and wheelchairs. For a working solution, a number of problems including safety, usability and economic feasibility have to be solved. This paper presents PAL's robotic wheelchair, an experimental platform to study and provide solutions to many of the aforementioned problems

Integration of ADAS algorithm in a Vehicle Prototype

International audienceFor several years, INRIA and Toyota Europe have been working together in the development of algorithms directed to ADAS. This paper will describe the main results of this successful joint project, applied to a prototype vehicle equipped with several sensors. This work will detail the framework, steps taken and motivation behind the developed technologies, as well as address the requirements needed for the automobile industry

Leader Selection and Following in Dynamic Environments

International audienceA crucial requirement for service robots is to be able to move in dynamic environments shared with humans as well as interact with them. Navigation in such environments is a challenging task, as the environment is constantly changing, future states have to be predicted and planning and execution must be carried on-line. However, even in very complex situations, humans can easily find a path that avoid both dynamic agents and static obstacles. This paper proposes a technique to take advantage of the human movement in such populated environments, using a probabilistic approach for the leader selection, according to the robot's desired destination. By choosing a leader to be followed in dynamic environments, the robot can take advantage of the paths traveled by humans or other robots, effortlessly avoiding dynamic and static features as its leader does, relieving the robot from the burden of having to generate its own path. Both the leader selection and the leader following algorithms have been tested in a real environment, with a robotic wheelchair

Leader following: A study on classification and selection

International audienceThis work proposes a different form of robotic navigation in dynamic environments, where the robot takes advantage of the motion of pedestrians, in order to improve its own navigation capabilities. Instead of treating persons as dynamic obstacles that should be avoided, here they are treated as special agents with an expert knowledge on navigating in dynamic scenarios. This work proposes that the robot selects and follows leaders, in order to move along optimal paths, deviate from undetected obstacles, improve navigation in densely populated areas and increase its acceptance by other humans. To accomplish this proposition, two novel approaches are developed in the area of leader selection. In the first, a motion prediction approach is used, to detect candidates that are moving to the same place that the robot is. In the second, a machine learning algorithm is trained with real examples and is used to select the best leader among several candidates. Experiments with a real robot are performed to validate the proposed approaches