Sampling-based motion planning with differential constraints
Sampling-based motion planning with differential constraints
Planning Algorithms
Toward more efficient motion planning with differential constraints
Toward more efficient motion planning with differential constraints
Motion planning with dynamics by a synergistic combination of layers of planning
IEEE Transactions on Robotics
Bounding on rough terrain with the LittleDog robot
International Journal of Robotics Research
Goal-oriented stimulus generation for analog circuits
Proceedings of the 49th Annual Design Automation Conference
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Rapidly-exploring Random Trees (RRTs) are widely used to solve large planning problems where the scope prohibits the feasibility of deterministic solvers, but the efficiency of these algorithms can be severely compromised in the presence of certain kinodynamics constraints. Obstacle fields with tunnels, or tubes are notoriously difficult, as are systems with differential constraints, because the tree grows inefficiently at the boundaries. Here we present a new sampling strategy for the RRT algorithm, based on an estimated feasibility set, which affords a dramatic improvement in performance in these severely constrained systems. We demonstrate the algorithm with a detailed look at the expansion of an RRT in a swingup task, and on path planning for a nonholonomic car.