LQR-Based Heuristics for Rapidly Exploring State Space
نویسندگان
چکیده
Kinodynamic planning algorithms like RapidlyExploring Randomized Trees (RRTs) hold the promise of finding feasible trajectories for rich dynamical systems with complex, non-convex constraints. In practice, these algorithms perform very well on configuration space planning, but struggle to grow efficiently in systems with dynamics or differential constraints when using conventional proximity metrics like the Euclidean distance. Here we argue that linear quadratic regulator (LQR) design can be used to produce a pseudodistance metric which captures the essential properties of proximity in state space at a reasonable computational cost. We demonstrate improved exploration of the state spaces of the double integrator, torque-limited pendulum, and acrobot when using this metric within the RRT framework.
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