527 research outputs found
A discrete methodology for controlling the sign of curvature and torsion for NURBS
This paper develops a discrete methodology for approximating the so-called convex domain of a NURBS curve, namely the domain in the ambient space, where a user-specified control point is free to move so that the curvature and torsion retains its sign along the NURBS parametric domain of definition. The methodology provides a monotonic sequence of convex polyhedra, converging from the interior to the convex domain. If the latter is non-empty, a simple algorithm is proposed, that yields a sequence of polytopes converging uniformly to the restriction of the convex domain to any user-specified bounding box. The algorithm is illustrated for a pair of planar and a spatial Bézier configuration
On the Construction of Safe Controllable Regions for Affine Systems with Applications to Robotics
This paper studies the problem of constructing in-block controllable (IBC)
regions for affine systems. That is, we are concerned with constructing regions
in the state space of affine systems such that all the states in the interior
of the region are mutually accessible through the region's interior by applying
uniformly bounded inputs. We first show that existing results for checking
in-block controllability on given polytopic regions cannot be easily extended
to address the question of constructing IBC regions. We then explore the
geometry of the problem to provide a computationally efficient algorithm for
constructing IBC regions. We also prove the soundness of the algorithm. We then
use the proposed algorithm to construct safe speed profiles for different
robotic systems, including fully-actuated robots, ground robots modeled as
unicycles with acceleration limits, and unmanned aerial vehicles (UAVs).
Finally, we present several experimental results on UAVs to verify the
effectiveness of the proposed algorithm. For instance, we use the proposed
algorithm for real-time collision avoidance for UAVs.Comment: 17 pages, 18 figures, under review for publication in Automatic
Expectations or Guarantees? I Want It All! A crossroad between games and MDPs
When reasoning about the strategic capabilities of an agent, it is important
to consider the nature of its adversaries. In the particular context of
controller synthesis for quantitative specifications, the usual problem is to
devise a strategy for a reactive system which yields some desired performance,
taking into account the possible impact of the environment of the system. There
are at least two ways to look at this environment. In the classical analysis of
two-player quantitative games, the environment is purely antagonistic and the
problem is to provide strict performance guarantees. In Markov decision
processes, the environment is seen as purely stochastic: the aim is then to
optimize the expected payoff, with no guarantee on individual outcomes.
In this expository work, we report on recent results introducing the beyond
worst-case synthesis problem, which is to construct strategies that guarantee
some quantitative requirement in the worst-case while providing an higher
expected value against a particular stochastic model of the environment given
as input. This problem is relevant to produce system controllers that provide
nice expected performance in the everyday situation while ensuring a strict
(but relaxed) performance threshold even in the event of very bad (while
unlikely) circumstances. It has been studied for both the mean-payoff and the
shortest path quantitative measures.Comment: In Proceedings SR 2014, arXiv:1404.041
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