The aim of this paper is to describe the architecture of a Reactive Path Planner(GRPP) for Mobile Robots based on the paradigm of Cellular Automata. It works on flat (Euclidean) Workspace or on natural variable terrains. The environment and the robot shape representations are distributed. Because of these characteristics, the planner resuits to be very flexible, handling robots with quite different kinematics (omnidirectional, car-like, asymmetrical, etc.), with generic shapes (even with concavities and holes) and with generic cinematic center positions. The underlying algorithm is based on a Potential Fields Method, using an anisotropic propagation of potentials on a non-Euclidean manifold. The collision-free trajectories are found following the minimum valley of the potential hypersurface embedded in a 4D space. Thanks to the Multilayered Cellular Automata architecture, it turns out to be very fast, complete and optimal, allowing to react to the world dynamics (reactive planning), generating new optimal solutions every time the external conditions changes
Marchese, F. (2005). The architecture of GRPP: A flexible generic reactive path-planner for mobile robots. In Proceedings of the Sixth IASTED International Conference on Robotics and Applications (pp.283-288). ACTA Press.
The architecture of GRPP: A flexible generic reactive path-planner for mobile robots
Marchese, FMG
2005
Abstract
The aim of this paper is to describe the architecture of a Reactive Path Planner(GRPP) for Mobile Robots based on the paradigm of Cellular Automata. It works on flat (Euclidean) Workspace or on natural variable terrains. The environment and the robot shape representations are distributed. Because of these characteristics, the planner resuits to be very flexible, handling robots with quite different kinematics (omnidirectional, car-like, asymmetrical, etc.), with generic shapes (even with concavities and holes) and with generic cinematic center positions. The underlying algorithm is based on a Potential Fields Method, using an anisotropic propagation of potentials on a non-Euclidean manifold. The collision-free trajectories are found following the minimum valley of the potential hypersurface embedded in a 4D space. Thanks to the Multilayered Cellular Automata architecture, it turns out to be very fast, complete and optimal, allowing to react to the world dynamics (reactive planning), generating new optimal solutions every time the external conditions changes
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