14,318 research outputs found
Toward Specification-Guided Active Mars Exploration for Cooperative Robot Teams
As a step towards achieving autonomy in space exploration missions, we consider a cooperative robotics system consisting of a copter and a rover. The goal of the copter is to explore an unknown environment so as to maximize knowledge about a science mission expressed in linear temporal logic that is to be executed by the rover. We model environmental uncertainty as a belief space Markov decision process and formulate the problem as a two-step stochastic dynamic program that we solve in a way that leverages the decomposed nature of the overall system. We demonstrate in simulations that the robot team makes intelligent decisions in the face of uncertainty
Topology-Guided Path Integral Approach for Stochastic Optimal Control in Cluttered Environment
This paper addresses planning and control of robot motion under uncertainty
that is formulated as a continuous-time, continuous-space stochastic optimal
control problem, by developing a topology-guided path integral control method.
The path integral control framework, which forms the backbone of the proposed
method, re-writes the Hamilton-Jacobi-Bellman equation as a statistical
inference problem; the resulting inference problem is solved by a sampling
procedure that computes the distribution of controlled trajectories around the
trajectory by the passive dynamics. For motion control of robots in a highly
cluttered environment, however, this sampling can easily be trapped in a local
minimum unless the sample size is very large, since the global optimality of
local minima depends on the degree of uncertainty. Thus, a homology-embedded
sampling-based planner that identifies many (potentially) local-minimum
trajectories in different homology classes is developed to aid the sampling
process. In combination with a receding-horizon fashion of the optimal control
the proposed method produces a dynamically feasible and collision-free motion
plans without being trapped in a local minimum. Numerical examples on a
synthetic toy problem and on quadrotor control in a complex obstacle field
demonstrate the validity of the proposed method.Comment: arXiv admin note: text overlap with arXiv:1510.0534
Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo
Asymptotically-optimal motion planners such as RRT* have been shown to
incrementally approximate the shortest path between start and goal states. Once
an initial solution is found, their performance can be dramatically improved by
restricting subsequent samples to regions of the state space that can
potentially improve the current solution. When the motion planning problem lies
in a Euclidean space, this region , called the informed set, can be
sampled directly. However, when planning with differential constraints in
non-Euclidean state spaces, no analytic solutions exists to sampling
directly.
State-of-the-art approaches to sampling in such domains such as
Hierarchical Rejection Sampling (HRS) may still be slow in high-dimensional
state space. This may cause the planning algorithm to spend most of its time
trying to produces samples in rather than explore it. In this paper,
we suggest an alternative approach to produce samples in the informed set
for a wide range of settings. Our main insight is to recast this
problem as one of sampling uniformly within the sub-level-set of an implicit
non-convex function. This recasting enables us to apply Monte Carlo sampling
methods, used very effectively in the Machine Learning and Optimization
communities, to solve our problem. We show for a wide range of scenarios that
using our sampler can accelerate the convergence rate to high-quality solutions
in high-dimensional problems
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