4 research outputs found
Anticipatory Robot Navigation by Simultaneously Localizing and Building a Cognitive Map
This paper presents a method for a mobile robot to
construct and localize relative to a “cognitive map”, where
the cognitive map is assumed to be a representational
structure that encodes both spatial and behavioral
information. The localization is performed by applying a
generic Bayes filter. The cognitive map was implemented
within a behavior-based robotic system, providing a new
behavior that allows the robot to anticipate future events
using the cognitive map. One of the prominent advantages
of this approach is elimination of the pose sensor usage
(e.g., shaft encoder, compass, GPS, etc.), which is known
for its limitations and proneness to various errors. A
preliminary experiment was conducted in simulation and its
promising results are discussed
Collision Avoidance for UAVs Using Optic Flow Measurement with Line of Sight Rate Equalization and Looming
A series of simplified scenarios is investigated whereby an optical flow balancing guidance law is used to avoid obstacles by steering an air vehicle between fixed objects/obstacles. These obstacles are registered as specific points that can be representative of features in a scene. The obstacles appear in the field of view of a single forward looking camera. First a 2-D analysis is presented where the rate of the line of sight from the vehicle to each of the obstacles to be avoided is measured. The analysis proceeds by initially using no field of view (FOV) limitations, then applying FOV restrictions, and adding features or obstacles in the scene. These analyses show that using a guidance law that equalizes the line of sight rates with no FOV limitations, actually results in the vehicle being steered into one of the objects for all initial conditions. The research next develops an obstacle avoidance strategy based on equilibrating the optic flow generated by the obstacles and presents an analysis that leads to a different conclusion in which balancing the optic flows does avoid the obstacles. The paper then describes a set of guidance methods that with real FOV limitations create a favorable result. Finally, the looming of an object in the camera\u27s FOV can be measured and used for synthesizing a collision avoidance guidance law. For the simple 2-D case, looming is quantified as an increase in LOS between two features on a wall in front of the air vehicle. The 2-D guidance law for equalizing the optic flow and looming detection is then extended into the 3-D case. Then a set of 3-D scenarios are further explored using a decoupled two channel approach. In addition, a comparison of two image segmentation techniques that are used to find optic flow vectors is presented
Adaptive quadruped locomotion: learning to detect and avoid an obstacle
Dissertação de mestrado em Engenharia de InformáticaAutonomy and adaptability are key features in the design and construction of a robotic
system capable of carrying out tasks in an unstructured and not predefined environment.
Such features are generally observed in animals, biological systems that usually serve as an
inspiration models to the design of robotic systems. The autonomy and adaptability of these
biological systems partially arises from their ability to learn. Animals learn to move and
control their own body when young, they learn to survive, to hunt and avoid undesirable
situations, from their progenitors.
There has been an increasing interest in defining a way to endow these abilities into
the design and creation of robotic systems. This dissertation proposes a mechanism that
seeks to create a learning module to a quadruped robot controller that enables it to both,
detect and avoid an obstacle in its path. The detection is based on a Forward Internal Model
(FIM) trained online to create expectations about the robot’s perceptive information. This
information is acquired by a set of range sensors that scan the ground in front of the robot in
order to detect the obstacle. In order to avoid stepping on the obstacle, the obstacle detections
are used to create a map of responses that will change the locomotion according to what is
necessary. The map is built and tuned every time the robot fails to step over the obstacle and
defines how the robot should act to avoid these situations in the future. Both learning tasks
are carried out online and kept active after the robot has learned, enabling the robot to adapt
to possible new situations.
The proposed architecture was inspired on [14, 17], but applied here to a quadruped robot
with different sensors and specific sensor configuration. Also, the mechanism is coupled
with the robot’s locomotion generator based in Central Pattern Generators (CPG)s presented
in [22]. In order to achieve its goal, the controller send commands to the CPG so that the
necessary changes in the locomotion are applied.
Results showed the success in both learning tasks. The robot was able to detect the
obstacle, and change its locomotion with the acquired information at collision time.Autonomia e capacidade de adaptação são características chave na criação de sistemas
robóticos capazes de levar a cabo diversas tarefas em ambientes não especificamente preparados
nem configurados para tal. Estas características são comuns nos animais, sistemas biológicos
que muitas vezes servem de modelo e inspiração para desenhar e construir sistemas
robóticos. A autonomia e adaptabilidade destes sistemas advém parcialmente da sua capacidade
de aprender. Quando ainda jovens, os animais aprendem a controlar o seu corpo e a
movimentar-se, muitos mamíferos aprendem a caçar e a sobreviver com os seus progenitores.
Ultimamente tem havido um crescente interesse em como aplicar estas características
no desenho e criação de sistemas robóticos. Nesta dissertação é proposto um mecanismo
que permita que um robô quadrúpede seja capaz de detectar e evitar um obstáculo no seu
caminho. A detecção é baseada num Forward Internal Model (FIM) que aprende a prever
os valores dos sensores de percepção do robô, os quais procuram detectar obstáculos no seu
caminho. Por forma a evitar os obstáculos, os sinais de detecção dos obstáculos são usados
na criação de um mapa que permitirá ao robô alterar a sua locomoção mediante o que é
necessário. Este mapa é construído à medida que o robô falha e tropeça no obstáculo. Nesse
momento, o mapa é definido para que tal situação seja evitada no futuro. Ambos os processos
de aprendizagem são levados a cabo em tempo de execução e mantêm esse processo activo
por forma a possibilitar a readaptação a eventuais novas situações.
Este mecanismo foi inspirado nos trabalhos [14, 17], mas aplicados aqui a um quadrúpede
com uma configuração de sensores diferente e específica. O mecanismo será interligado
ao gerador da locomoção, baseado em Control Pattern Generator (CPG) proposto em [22].
Por forma a atingir o objectivo final, o controlador irá enviar comandos para o CPG a fim da
locomoção ser alterada como necessário.
Os resultados obtidos mostram o sucesso em ambos os processos de aprendizagem. O
robô é capaz de detectar o obstáculo e alterar a sua locomção de acordo com a informação
adquirida nos momentos de colisão com o mesmo, conseguindo efectivamente passar por
cima do obstáculo sem nenhum tipo de colisão