Encoderless position estimation and error correction techniques for miniature mobile robots

This paper presents an encoderless position estimation technique for miniature-sized mobile robots. Odometry techniques, which are based on the hardware components, are commonly used for calculating the geometric location of mobile robots. Therefore, the robot must be equipped with an appropriate sensor to measure the motion. However, due to the hardware limitations of some robots, employing extra hardware is impossible. On the other hand, in swarm robotic research, which uses a large number of mobile robots, equipping the robots with motion sensors might be costly. In this study, the trajectory of the robot is divided into several small displacements over short spans of time. Therefore, the position of the robot is calculated within a short period, using the speed equations of the robot's wheel. In addition, an error correction function is proposed that estimates the errors of the motion using a current monitoring technique. The experiments illustrate the feasibility of the proposed position estimation and error correction techniques to be used in miniature-sized mobile robots without requiring an additional sensor

Qualitative localization using vision and odometry for path following in topo-metric maps

International audienceWe address the problem of navigation in topo- metric maps created by using odometry data and visual loop- closure detection. Based on our previous work [6], we present an optimized version of our loop-closure detection algorithm that makes it possible to create consistent topo-metric maps in real-time while the robot is teleoperated. Using such a map, the proposed navigation algorithm performs qualitative localization using the same loop-closure detection framework and the odometry data. This qualitative position is used to support robot guidance to follow a predicted path in the topo-metric map compensating the odometry drift. Compared to purely visual servoing approaches for similar tasks, our path-following algorithm is real-time, light (not more than two images per seconds are processed), and robust as odometry is still available to navigate even if vision information is absent for a short time. The approach has been validated experimentally with a Pioneer P3DX robot in indoor environments with embedded and remote computations

Omnidirectional Vision Based Topological Navigation

Goedemé T., Van Gool L., ''Omnidirectional vision based topological navigation'', Mobile robots navigation, pp. 172-196, Barrera Alejandra, ed., March 2010, InTech.status: publishe

Navegación visual usando una descripción de la ruta con secuencias de imágenes

Desde el principio de su existencia, el hombre siempre se ha propuesto inventar artefactos que simplifiquen las tareas que realiza en su día a día. Los avances de la ciencia en los últimos años han permitido el desarrollo de máquinas que puedan realizar tareas complejas de manera autónoma, y entre todas ellas se debe destacar a los robots. Las tareas que realizan en la actualidad los robots suelen ser sencillas y de carácter repetitivo. En muchos casos, además, se trata de tareas que se realizan en espacios interiores, lo que implica que los entornos en los que se va a mover son poco cambiantes. Algunas de ellas requieren que un robot móvil repita constantemente un camino que ha aprendido para llevar y traer objetos. Un ejemplo de estas características podría ser un robot cartero, en el seno de una empresa. El robot repite todos los días la misma ruta para entregar las cartas a sus destinatarios. Los robots reales distan mucho de parecerse a los descritos en novelas o películas de ciencia ficción, máquinas pensantes con alta capacidad de raciocinio. Actualmente, en el marco de investigación y realizaciones de prototipos experimentales existen robots con capacidad de realizar algunas tareas sencillas en las que tanto el movimiento como la percepción se llevan a cabo de forma autónoma [...

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described