Approximate Kinodynamic Planning Using &-Norm Dynamic Bounds

Approximate Kinodynamic Planning Using L2-norm Dynamic Bounds

Approximate Kinodynamic Planning Using L2-norm Dynamic Bounds∗

In this paper we address the issue of kinodynamic motion planning. Given a point that moves with bounded acceleration and velocity, we wish to find the time-optimal trajectory from a start state to a goal state (a state consists of both a position and a velocity). As finding exact optimal solutions to this problem seems very hard, we present a provably good approximation algorithm using the L2 ...

Dynamic Policy Programming with Function Approximation

In this paper, we consider the problem of planning in the infinite-horizon discountedreward Markov decision problems. We propose a novel iterative method, called dynamic policy programming (DPP), which updates the parametrized policy by a Bellmanlike iteration. For discrete state-action case, we establish L∞-norm loss bounds for the performance of the policy induced by DPP and prove that it asy...

Planning Persistent Monitoring Trajectories in Spatiotemporal Fields using Kinodynamic RRT*

This paper proposes a sampling based kinodynamic planning technique for planning persistent monitoring trajectories for a sensing robot in a spatiotemporal environmental field. The robot uses a Kalman-Bucy filter to estimate the spatiotemporal field. Since the error covariance matrix of the Kalman-Bucy filter evolves according to the nonlinear Riccati differential equation, this requires planni...

Provably Good Approximation Algorithms for OptimalKinodynamic Planning

In optimal kinodynamic planning, given a robot system, we must nd a minimal-time trajectory that goes from a start state to a goal state while avoiding obstacles by a speed-dependent safety-margin and respecting dynamics bounds. With Canny and Reif 1], we approached this problem from an-approximation standpoint and introduced a provably-good approximation algorithm for optimal kinodynamic plann...