84 research outputs found
Bayesian Learning-Based Adaptive Control for Safety Critical Systems
Deep learning has enjoyed much recent success, and applying state-of-the-art
model learning methods to controls is an exciting prospect. However, there is a
strong reluctance to use these methods on safety-critical systems, which have
constraints on safety, stability, and real-time performance. We propose a
framework which satisfies these constraints while allowing the use of deep
neural networks for learning model uncertainties. Central to our method is the
use of Bayesian model learning, which provides an avenue for maintaining
appropriate degrees of caution in the face of the unknown. In the proposed
approach, we develop an adaptive control framework leveraging the theory of
stochastic CLFs (Control Lyapunov Functions) and stochastic CBFs (Control
Barrier Functions) along with tractable Bayesian model learning via Gaussian
Processes or Bayesian neural networks. Under reasonable assumptions, we
guarantee stability and safety while adapting to unknown dynamics with
probability 1. We demonstrate this architecture for high-speed terrestrial
mobility targeting potential applications in safety-critical high-speed Mars
rover missions.Comment: Corrected an error in section II, where previously the problem was
introduced in a non-stochastic setting and wrongly assumed the solution to an
ODE with Gaussian distributed parametric uncertainty was equivalent to an SDE
with a learned diffusion term. See Lew, T et al. "On the Problem of
Reformulating Systems with Uncertain Dynamics as a Stochastic Differential
Equation
Temporal Logic Control of POMDPs via Label-based Stochastic Simulation Relations
The synthesis of controllers guaranteeing linear temporal logic specifications on partially observable Markov decision processes (POMDP) via their belief models causes computational issues due to the continuous spaces. In this work, we construct a finite-state abstraction on which a control policy is synthesized and refined back to the original belief model. We introduce a new notion of label-based approximate stochastic simulation to quantify the deviation between belief models. We develop a robust synthesis methodology that yields a lower bound on the satisfaction probability, by compensating for deviations a priori, and that utilizes a less conservative control refinement
LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time
A reliable odometry source is a prerequisite to enable complex autonomy
behaviour in next-generation robots operating in extreme environments. In this
work, we present a high-precision lidar odometry system to achieve robust and
real-time operation under challenging perceptual conditions. LOCUS (Lidar
Odometry for Consistent operation in Uncertain Settings), provides an accurate
multi-stage scan matching unit equipped with an health-aware sensor integration
module for seamless fusion of additional sensing modalities. We evaluate the
performance of the proposed system against state-of-the-art techniques in
perceptually challenging environments, and demonstrate top-class localization
accuracy along with substantial improvements in robustness to sensor failures.
We then demonstrate real-time performance of LOCUS on various types of robotic
mobility platforms involved in the autonomous exploration of the Satsop power
plant in Elma, WA where the proposed system was a key element of the CoSTAR
team's solution that won first place in the Urban Circuit of the DARPA
Subterranean Challenge.Comment: Accepted for publication at IEEE Robotics and Automation Letters,
202
Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments
In this article, we propose a novel LiDAR and event camera fusion modality
for subterranean (SubT) environments for fast and precise object and human
detection in a wide variety of adverse lighting conditions, such as low or no
light, high-contrast zones and in the presence of blinding light sources. In
the proposed approach, information from the event camera and LiDAR are fused to
localize a human or an object-of-interest in a robot's local frame. The local
detection is then transformed into the inertial frame and used to set
references for a Nonlinear Model Predictive Controller (NMPC) for reactive
tracking of humans or objects in SubT environments. The proposed novel fusion
uses intensity filtering and K-means clustering on the LiDAR point cloud and
frequency filtering and connectivity clustering on the events induced in an
event camera by the returning LiDAR beams. The centroids of the clusters in the
event camera and LiDAR streams are then paired to localize reflective markers
present on safety vests and signs in SubT environments. The efficacy of the
proposed scheme has been experimentally validated in a real SubT environment (a
mine) with a Pioneer 3AT mobile robot. The experimental results show real-time
performance for human detection and the NMPC-based controller allows for
reactive tracking of a human or object of interest, even in complete darkness.Comment: Accepted at IFAC World Congress 202
Temporal Logic Control of POMDPs via Label-based Stochastic Simulation Relations
The synthesis of controllers guaranteeing linear temporal logic specifications on partially observable Markov decision processes (POMDP) via their belief models causes computational issues due to the continuous spaces. In this work, we construct a finite-state abstraction on which a control policy is synthesized and refined back to the original belief model. We introduce a new notion of label-based approximate stochastic simulation to quantify the deviation between belief models. We develop a robust synthesis methodology that yields a lower bound on the satisfaction probability, by compensating for deviations a priori, and that utilizes a less conservative control refinement
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