Motion Planning with Dynamics
Combining Sampling-based Motion Planning with Discrete Search
The deployment of robots in exploration, navigation, search-and-rescue missions requires the capability to efficiently plan motions that enable the robots to reach desired goal regions while avoiding collisions. In order to follow the planned motions in the physical world, it is essential to take into account the underlying motion dynamics during planning. Motion planning with dynamics, however, poses significant computational challenges. In addition to collision avoidance, the planned motions need to satisfy differential constraints imposed by the dynamics on position, orientation, velocity, acceleration, and curvature. As an illustration, differential constraints ensure, for example, that wheels on a car-like robot do not slide sideways.
To effectively incorporate dynamics, we propose treating motion planning not just as a search problem in a continuous space but as a search problem in a hybrid space consisting of discrete and continuous components. A multi-layered framework is developed, which combines discrete search and sampling-based motion planning. The overall effect is that the framework significantly improves the computational efficiency, as demonstrated by simulation experiments with dynamical models of ground and flying vehicles.
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