Sensory Steering for Sampling-Based Motion Planning

Sampling-based algorithms offer computationally efficient, practical solutions to the path finding problem in high-dimensional complex configuration spaces by approximately capturing the connectivity of the underlying space through a (dense) collection of sample configurations joined by simple local planners. In this paper, we address a long-standing bottleneck associated with the difficulty of finding paths through narrow passages. Whereas most prior work considers the narrow passage problem as a sampling issue (and the literature abounds with heuristic sampling strategies) very little attention has been paid to the design of new effective local planners. Here, we propose a novel sensory steering algorithm for sampling- based motion planning that can “feel” a configuration space locally and significantly improve the path planning performance near difficult regions such as narrow passages. We provide computational evidence for the effectiveness of the proposed local planner through a variety of simulations which suggest that our proposed sensory steering algorithm outperforms the standard straight-line planner by significantly increasing the connectivity of random motion planning graphs. For more information: Kod*la

TOGGLE PRM: A SIMULTANEOUS MAPPING OF CFREE AND COBSTACLE FOR USE IN PROBABILISTIC ROADMAP METHODS

Motion planning for robotic applications is difficult. This is a widely studied problem in which the best known deterministic solution is doubly exponential in the dimensionality of the problem. A class of probabilistic planners, called sampling-based planners, have shown much success in this area, but still show weakness for planning in difficult parts of the space, namely narrow passages. The problem space is made of two subsets - free space and collision space, representing valid and invalid robot positions. A general method for probabilistic planners is the probabilistic roadmap method (PRM) which maps only free space to find a solution. This thesis proposes a new strategy, Toggle PRM, for probabilistic roadmap planners, which simultaneously maps both free space and collision space in order to guide the solution more efficiently. All sampled robotic configurations are kept in two separate maps. When the connection attempts between configurations in one roadmap fail, the witness to the failure is retained as a configuration in the opposing roadmap. By mapping both spaces, sampling density in narrow passages is greatly increased. A theoretical and experimental analysis of Toggle PRM shows the independence from the volume of a narrow passage and the volume of the obstacles surrounding the passage for sampling, overcoming a previous challenge of probabilistic planning. Additionally, Toggle PRM has increased efficiency as compared to other common sampling techniques in various motion planning problems because of this improved sampling in narrow passages

Toggle PRM: A Coordinated Mapping of C-Free and C-Obstacle in Arbitrary Dimension

Abstract Motion planning has received much attention over the past 40 years. More than 15 years have passed since the introduction of the successful sampling-based approach known as the Probabilistic RoadMap Method (PRM). PRM and its many variants have demonstrated great success for some high-dimensional problems, but they all have some level of difficulty in the presence of narrow passages. Recently, an approach called Toggle PRM has been introduced whose performance does not degrade for 2-dimensional problems with narrow passages. In Toggle PRM, a si-multaneous, coordinated mapping of both C f ree and Cobst is performed and every connection attempt augments one of the maps – either validating an edge in the cur-rent space or adding a configuration ’witnessing ’ the connection failure to the other space. In this paper, we generalize Toggle PRM to d-dimensions and show that the benefits of mapping both C f ree and Cobst continue to hold in higher dimensions. In particular, we introduce a new narrow passage characterization, α-ε-separable nar-row passages, which describes the types of passages that can be successfully mapped by Toggle PRM. Intuitively, α-ε-separable narrow passages are arbitrarily narrow regions of C f ree that separate regions of Cobst, at least locally, such as hallways in an office building. We experimentally compare Toggle PRM with other methods in a variety of scenarios with different types of narrow passages and robots with up to 16 DOF.

Diseño de un robot móvil autónomo de telepresencia

The recent rise in tele-operated autonomous mobile vehicles calls for a seamless control architecture that reduces the learning curve when the platform is functioning autonomously (without active supervisory control), as well as when tele-operated. Conventional robot plat-forms usually solve one of two problems. This work develops a mobile base using the Robot Operating System (ROS) middleware for teleoperation at low cost. The three-layer architec-ture introduced adds or removes operator complexity. The lowest layer provides mobility and robot awareness; the second layer provides usability; the upper layer provides inter-activity. A novel interactive control that combines operator intelligence/ skill with robot/autonomous intelligence enabling the mobile base to respond to expected events and ac-tively react to unexpected events is presented. The experiments conducted in the robot laboratory summarises the advantages of using such a system.El reciente auge de los vehículos móviles autónomos teleoperados exige una arquitectura de control sin fisuras que reduzca la curva de aprendizaje cuando la plataforma funciona de forma autónoma (sin control de supervisión activo), así como cuando es teleoperada. Las plataformas robóticas convencionales suelen resolver uno de los dos problemas. Este tra-bajo desarrolla una base móvil que utiliza el middleware Robot Operating System (ROS) para la teleoperación a bajo coste. La arquitectura de tres capas introducida añade o elimina la complejidad del operador. La capa más baja proporciona movilidad y conciencia robótica; la segunda capa proporciona usabilidad; la capa superior proporciona interactividad. Se presenta un novedoso control interactivo que combina la inteligencia/habilidades del op-erador con la inteligencia autónoma del robot, lo que permite que la base móvil responda a los eventos esperados y reaccione activamente a los eventos inesperados. Los experi-mentos realizados en el laboratorio robótica resumen las ventajas de utilizar un sistema de este tipoDepartamento de Ingeniería de Sistemas y AutomáticaMáster en Electrónica Industrial y Automátic