Kinodynamic Planning on Constraint Manifolds
This addresses motion planning for robots with kinematic loops or contacts, but it is incremental as it extends existing RRT methods to implicitly-defined state spaces.
The paper tackles motion planning for systems with kinematic and dynamic constraints by constructing an atlas of the state space manifold and using it to build a rapidly-exploring random tree, resulting in the first randomized kinodynamic planner for implicitly-defined state spaces validated on complex systems.
This paper 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 in this paper validate the approach in significantly-complex systems.