56 research outputs found
Distributed allocation of mobile sensing swarms in gyre flows
We address the synthesis of distributed control policies to enable a swarm of
homogeneous mobile sensors to maintain a desired spatial distribution in a
geophysical flow environment, or workspace. In this article, we assume the
mobile sensors (or robots) have a "map" of the environment denoting the
locations of the Lagrangian coherent structures or LCS boundaries. Based on
this information, we design agent-level hybrid control policies that leverage
the surrounding fluid dynamics and inherent environmental noise to enable the
team to maintain a desired distribution in the workspace. We establish the
stability properties of the ensemble dynamics of the distributed control
policies. Since realistic quasi-geostrophic ocean models predict double-gyre
flow solutions, we use a wind-driven multi-gyre flow model to verify the
feasibility of the proposed distributed control strategy and compare the
proposed control strategy with a baseline deterministic allocation strategy.
Lastly, we validate the control strategy using actual flow data obtained by our
coherent structure experimental testbed.Comment: 10 pages, 14 Figures, added reference
LEARNEST: LEARNing Enhanced Model-based State ESTimation for Robots using Knowledge-based Neural Ordinary Differential Equations
State estimation is an important aspect in many robotics applications. In
this work, we consider the task of obtaining accurate state estimates for
robotic systems by enhancing the dynamics model used in state estimation
algorithms. Existing frameworks such as moving horizon estimation (MHE) and the
unscented Kalman filter (UKF) provide the flexibility to incorporate nonlinear
dynamics and measurement models. However, this implies that the dynamics model
within these algorithms has to be sufficiently accurate in order to warrant the
accuracy of the state estimates. To enhance the dynamics models and improve the
estimation accuracy, we utilize a deep learning framework known as
knowledge-based neural ordinary differential equations (KNODEs). The KNODE
framework embeds prior knowledge into the training procedure and synthesizes an
accurate hybrid model by fusing a prior first-principles model with a neural
ordinary differential equation (NODE) model. In our proposed LEARNEST
framework, we integrate the data-driven model into two novel model-based state
estimation algorithms, which are denoted as KNODE-MHE and KNODE-UKF. These two
algorithms are compared against their conventional counterparts across a number
of robotic applications; state estimation for a cartpole system using partial
measurements, localization for a ground robot, as well as state estimation for
a quadrotor. Through simulations and tests using real-world experimental data,
we demonstrate the versatility and efficacy of the proposed learning-enhanced
state estimation framework.Comment: 7 pages, 3 figures, 1 tabl
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles
Nonlinear model predictive control (MPC) is a flexible and increasingly
popular framework used to synthesize feedback control strategies that can
satisfy both state and control input constraints. In this framework, an
optimization problem, subjected to a set of dynamics constraints characterized
by a nonlinear dynamics model, is solved at each time step. Despite its
versatility, the performance of nonlinear MPC often depends on the accuracy of
the dynamics model. In this work, we leverage deep learning tools, namely
knowledge-based neural ordinary differential equations (KNODE) and deep
ensembles, to improve the prediction accuracy of this model. In particular, we
learn an ensemble of KNODE models, which we refer to as the KNODE ensemble, to
obtain an accurate prediction of the true system dynamics. This learned model
is then integrated into a novel learning-enhanced nonlinear MPC framework. We
provide sufficient conditions that guarantees asymptotic stability of the
closed-loop system and show that these conditions can be implemented in
practice. We show that the KNODE ensemble provides more accurate predictions
and illustrate the efficacy and closed-loop performance of the proposed
nonlinear MPC framework using two case studies.Comment: 16 pages, 2 figures, includes Appendi
Online Estimation of the Koopman Operator Using Fourier Features
Transfer operators offer linear representations and global, physically
meaningful features of nonlinear dynamical systems. Discovering transfer
operators, such as the Koopman operator, require careful crafted dictionaries
of observables, acting on states of the dynamical system. This is ad hoc and
requires the full dataset for evaluation. In this paper, we offer an
optimization scheme to allow joint learning of the observables and Koopman
operator with online data. Our results show we are able to reconstruct the
evolution and represent the global features of complex dynamical systems.Comment: Accepted to 5th L4DC Conference. Proceedings of The 5th Annual
Learning for Dynamics and Control Conference, PMLR 211:1271-1283, 2023. 13
pages 6 figure
A Quadratic Programming approach for coordinating multi-AGV systems
This paper presents an optimization strategy to coordinate multiple Autonomous Guided Vehicles (AGVs) on ad-hoc pre-defined roadmaps used in logistic operations in industrial applications. Specifically, the objective is to maximize traffic throughput of AGVs navigating in an automated warehouse by minimizing the time AGVs spend negotiating complex traffic patterns to avoid collisions with other AGVs. In this work, the coordination problem is posed as a Quadratic Programming (QP) problem where the optimization is performed in a centralized manner. The optimality of the coordination strategy is established and the feasibility of the strategy is validated in simulation for different scenarios and for real industrial environments. The performance of the proposed strategy is then compared with a decentralized coordination strategy which relies on local negotiations for shared resources. The results show that the proposed coordination strategy successfully maximizes vehicle throughout and significantly minimizes the time vehicles spend negotiating traffic under different scenarios
Optimized Stochastic Policies for Task Allocation in Swarms of Robots
We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no communication among robots. It is based on the development of a continuous abstraction of the swarm obtained by modeling population fractions and defining the task allocation problem as the selection of rates of robot ingress and egress to and from each task. These rates are used to determine probabilities that define stochastic control policies for individual robots, which, in turn, produce the desired collective behavior. We address the problem of computing rates to achieve fast redistribution of the swarm subject to constraint(s) on switching between tasks at equilibrium. We present several formulations of this optimization problem that vary in the precedence constraints between tasks and in their dependence on the initial robot distribution. We use each formulation to optimize the rates for a scenario with four tasks and compare the resulting control policies using a simulation in which 250 robots redistribute themselves among four buildings to survey the perimeters
Online Dynamics Learning for Predictive Control with an Application to Aerial Robots
In this work, we consider the task of improving the accuracy of dynamic
models for model predictive control (MPC) in an online setting. Even though
prediction models can be learned and applied to model-based controllers, these
models are often learned offline. In this offline setting, training data is
first collected and a prediction model is learned through an elaborated
training procedure. After the model is trained to a desired accuracy, it is
then deployed in a model predictive controller. However, since the model is
learned offline, it does not adapt to disturbances or model errors observed
during deployment. To improve the adaptiveness of the model and the controller,
we propose an online dynamics learning framework that continually improves the
accuracy of the dynamic model during deployment. We adopt knowledge-based
neural ordinary differential equations (KNODE) as the dynamic models, and use
techniques inspired by transfer learning to continually improve the model
accuracy. We demonstrate the efficacy of our framework with a quadrotor robot,
and verify the framework in both simulations and physical experiments. Results
show that the proposed approach is able to account for disturbances that are
possibly time-varying, while maintaining good trajectory tracking performance.Comment: 8 pages, 4 figure
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