Kinodynamic planning on constraint manifolds
This report presents a motion planner for systems subject to kinematic and dynamic constraints. The former appear when kinematic loops are present in the system, such as in parallel manipulators, in robots that cooperate to achieve a given task, or in situations involving contacts with the environme...
| Autores: | , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/110931 |
| Acceso en línea: | https://hdl.handle.net/2117/110931 |
| Access Level: | acceso abierto |
| Palabra clave: | robot dynamics robot kinematics robot programming Kinodynamic motion planning constrained system dynamic simulation rapidly-exploring randomized tree Classificació INSPEC::Automation::Robots::Robot dynamics Àrees temàtiques de la UPC::Informàtica::Robòtica |
| Sumario: | This report presents a motion planner for systems subject to kinematic and dynamic constraints. The former appear when kinematic loops are present in the system, such as in parallel manipulators, in robots that cooperate to achieve a given task, or in situations involving contacts with the environment. The latter are necessary to obtain realistic trajectories, taking into account the forces acting on the system. The kinematic constraints make the state space become an implicitly-defined manifold, which complicates the application of common motion planning techniques. To address this issue, the planner constructs an atlas of the state space manifold incrementally, and uses this atlas both to generate random states and to dynamically simulate the steering of the system towards such states. The resulting tools are then exploited to construct a rapidly-exploring random tree (RRT) over the state space. To the best of our knowledge, this is the first randomized kinodynamic planner for implicitly-defined state spaces. The test cases presented validate the approach in significantly-complex systems. |
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