12,187 research outputs found
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Learning-based control algorithms require data collection with abundant
supervision for training. Safe exploration algorithms ensure the safety of this
data collection process even when only partial knowledge is available. We
present a new approach for optimal motion planning with safe exploration that
integrates chance-constrained stochastic optimal control with dynamics learning
and feedback control. We derive an iterative convex optimization algorithm that
solves an \underline{Info}rmation-cost \underline{S}tochastic
\underline{N}onlinear \underline{O}ptimal \underline{C}ontrol problem
(Info-SNOC). The optimization objective encodes both optimal performance and
exploration for learning, and the safety is incorporated as distributionally
robust chance constraints. The dynamics are predicted from a robust regression
model that is learned from data. The Info-SNOC algorithm is used to compute a
sub-optimal pool of safe motion plans that aid in exploration for learning
unknown residual dynamics under safety constraints. A stable feedback
controller is used to execute the motion plan and collect data for model
learning. We prove the safety of rollout from our exploration method and
reduction in uncertainty over epochs, thereby guaranteeing the consistency of
our learning method. We validate the effectiveness of Info-SNOC by designing
and implementing a pool of safe trajectories for a planar robot. We demonstrate
that our approach has higher success rate in ensuring safety when compared to a
deterministic trajectory optimization approach.Comment: Submitted to RA-L 2020, review-
Multiobjective optimization to a TB-HIV/AIDS coinfection optimal control problem
We consider a recent coinfection model for Tuberculosis (TB), Human
Immunodeficiency Virus (HIV) infection and Acquired Immunodeficiency Syndrome
(AIDS) proposed in [Discrete Contin. Dyn. Syst. 35 (2015), no. 9, 4639--4663].
We introduce and analyze a multiobjective formulation of an optimal control
problem, where the two conflicting objectives are: minimization of the number
of HIV infected individuals with AIDS clinical symptoms and coinfected with
AIDS and active TB; and costs related to prevention and treatment of HIV and/or
TB measures. The proposed approach eliminates some limitations of previous
works. The results of the numerical study provide comprehensive insights about
the optimal treatment policies and the population dynamics resulting from their
implementation. Some nonintuitive conclusions are drawn. Overall, the
simulation results demonstrate the usefulness and validity of the proposed
approach.Comment: This is a preprint of a paper whose final and definite form is with
'Computational and Applied Mathematics', ISSN 0101-8205 (print), ISSN
1807-0302 (electronic). Submitted 04-Feb-2016; revised 11-June-2016 and
02-Sept-2016; accepted for publication 15-March-201
Faster Motion on Cartesian Paths Exploiting Robot Redundancy at the Acceleration Level
The problem of minimizing the transfer time along a given Cartesian path for redundant robots can be approached in two steps, by separating the generation of a joint path associated to the Cartesian path from the exact minimization of motion time under kinematic/dynamic bounds along the obtained parameterized joint path. In this framework, multiple suboptimal solutions can be found, depending on how redundancy is locally resolved in the joint space within the first step. We propose a solution method that works at the acceleration level, by using weighted pseudoinversion, optimizing an inertia-related criterion, and including null-space damping. Several numerical results obtained on different robot systems demonstrate consistently good behaviors and definitely faster motion times in comparison with related methods proposed in the literature. The motion time obtained with our method is reasonably close to the global time-optimal solution along same Cartesian path. Experimental results on a KUKA LWR IV are also reported, showing the tracking control performance on the executed motions
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