ROBOT NAVIGATION INCROWDS USING QUEADRILATERAL VELOCITY OBSTACLES (QVO)
This thesis tackles the problem of robot navigation within crowds. We propose a reactive methodology, i.e., such that the robot has no full knowledge of the environment and such that its decisions have to be taken in real time. We rely on a well-known geometric approach, the Velocity Obstacle approa...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2017 |
| País: | México |
| Institución: | Centro de Investigación en Matemáticas |
| Repositorio: | Repositorio Institucional CIMAT |
| OAI Identifier: | oai:cimat.repositorioinstitucional.mx:1008/871 |
| Acceso en línea: | http://cimat.repositorioinstitucional.mx/jspui/handle/1008/871 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/MSC/OBSTÁCULO A VELOCIDAD info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120317 |
| Sumario: | This thesis tackles the problem of robot navigation within crowds. We propose a reactive methodology, i.e., such that the robot has no full knowledge of the environment and such that its decisions have to be taken in real time. We rely on a well-known geometric approach, the Velocity Obstacle approach. We suppose that we are given a set of trajectory prediction models for a group of mobile obstacles (humans, robots, etc.). The robot takes this information from the environment to go from its initial position to its goal, avoiding collisions. We introduce a conservative discretization of the VOs, which we call Quadrilateral Velocity Obstacle, QVO. It consists of a trapezoid that bounds the VO and is computationally easy to model. We propose two approaches: one Deterministic, and one Probabilistic. The first considers the most probable trajectory for each mobile obstacle. This approach results in an optimization problem with linear constraints in the velocity space. The second approach handles a full trajectory distribution, and decides what velocity to take, both to avoid collisions with obstacles, and to reach its goal. Unlike the deterministic model, this approach contemplates the collision probabilistically. We present results of the two approaches in simulation, for holonomic and non-holonomic agents, and we provide exhaustive evaluations of the different parameters involved in our algorithms. |
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