Zobrazeno 1 - 10
of 315
pro vyhledávání: '"Forbes, James Richard"'
Autor:
Dahdah, Steven, Forbes, James Richard
This paper proposes a robust nonlinear observer synthesis method for a population of systems modelled using the Koopman operator. The Koopman operator allows nonlinear systems to be rewritten as infinite-dimensional linear systems. A finite-dimension
Externí odkaz:
http://arxiv.org/abs/2410.01057
Autor:
Lortie, Louis, Forbes, James Richard
The Koopman operator framework can be used to identify a data-driven model of a nonlinear system. Unfortunately, when the data is corrupted by noise, the identified model can be biased. Additionally, depending on the choice of lifting functions, the
Externí odkaz:
http://arxiv.org/abs/2408.16846
Autor:
Moalemi, Sepehr, Forbes, James Richard
This paper considers gain-scheduling of very strictly passive (VSP) subcontrollers using scheduling matrices. The use of scheduling matrices, over scalar scheduling signals, realizes greater design freedom, which in turn can improve closed-loop perfo
Externí odkaz:
http://arxiv.org/abs/2408.06476
Bayesian estimation is a vital tool in robotics as it allows systems to update the belief of the robot state using incomplete information from noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and me
Externí odkaz:
http://arxiv.org/abs/2408.00907
This paper proposes a novel Hessian approximation for Maximum a Posteriori estimation problems in robotics involving Gaussian mixture likelihoods. Previous approaches manipulate the Gaussian mixture likelihood into a form that allows the problem to b
Externí odkaz:
http://arxiv.org/abs/2404.05452
This paper presents a data-driven method to identify an asymptotically stable Koopman system from noisy data. In particular, the proposed approach combines approximations of the system's forward- and backward-in-time dynamics to reduce bias caused by
Externí odkaz:
http://arxiv.org/abs/2403.10623
This paper introduces a set of customizable and novel cost functions that enable the user to easily specify desirable robot formations, such as a ``high-coverage'' infrastructure-inspection formation, while maintaining high relative pose estimation a
Externí odkaz:
http://arxiv.org/abs/2403.00988
Publikováno v:
IEEE Robotics and Automation Letters, vol. 5, no. 4, pp 5067-5074, June 2020
This paper presents an invariant Rauch-Tung- Striebel (IRTS) smoother applicable to systems with states that are an element of a matrix Lie group. In particular, the extended Rauch-Tung-Striebel (RTS) smoother is adapted to work within a matrix Lie g
Externí odkaz:
http://arxiv.org/abs/2403.00075
Autor:
Cohen, Mitchell, Forbes, James Richard
Publikováno v:
IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1151-1158, June 2020
This paper presents a solution for the state estimation and control problems for a class of unconventional vertical takeoff and landing (VTOL) UAVs operating in forward-flight conditions. A tightly-coupled state estimation approach is used to estimat
Externí odkaz:
http://arxiv.org/abs/2403.00076
Laser line scanners are increasingly being used in the subsea industry for high-resolution mapping and infrastructure inspection. However, calibrating the 3D pose of the scanner relative to the vehicle is a perennial source of confusion and frustrati
Externí odkaz:
http://arxiv.org/abs/2402.14993