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公开(公告)号：US20200293010A1
公开(公告)日：2020-09-17
申请号：US16298271
申请日：2019-03-11
发明人： Karl Berntorp
摘要： A system is controlled using particle filter executed to estimate weights of a set of particles based on fitting of the particles into a measurement model, wherein a particle includes a motion model of the system having an uncertainty modeled as a Gaussian process over possible motion models of the system and a state of the system determined with the uncertainty of the motion model of the particle, wherein a distribution of the Gaussian process of the motion model of one particle is different from a distribution of the Gaussian process of the motion model of another particle. Each execution of the particle filter updates the state of the particle according to a control input to the system and the motion model of the particle with the uncertainty and determines particle weights by fitting the state of the particle in the measurement model subject to measurement noise.
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公开(公告)号：US10589784B2
公开(公告)日：2020-03-17
申请号：US15681728
申请日：2017-08-21
发明人： Uros Kalabic , Karl Berntorp , Stefano Di Cairano
摘要： Systems and methods for an imaging system having a memory with a historical localization dictionary database having geo-located driving image sequences, such that each reference image is applied to a threshold to produce a binary representation. A sensor to acquire a sequence of input images of a dynamic scene. An encoder to determine, for each input image in the sequence, a histogram of each input image indicating a number of vertical edges at each bin of the input image and to threshold the histogram to produce a binary representation of the input image. A visual odometer to compare the binary representations of each input image and each reference image, by matching an input image against a reference image. Wherein the visual odometer determines a location of the input image based on a match between the input image and the reference image.
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公开(公告)号：US20170168485A1
公开(公告)日：2017-06-15
申请号：US14968848
申请日：2015-12-14
发明人： Karl Berntorp , Oktay Arslan
CPC分类号： G05D1/0088 , B60W30/00 , G01C21/20 , G01C21/26 , G01C21/3453 , G05D1/0214 , G05D1/0217 , G05D1/0274
摘要： A method determines iteratively a motion of the vehicle from an initial location and a target location. An iteration of the method determines a location between the initial location and the target location that satisfies spatial constraints on locations of the vehicle and determines state transitions of the vehicle moved to the location from a set of neighboring locations determined during previous iterations. The method selects a neighboring location resulting in an optimal state transition of the vehicle and updates a graph of state transitions of the vehicle determined during previous iterations with the optimal state transition. The motion of the vehicle is determined a sequence of state transitions connecting the initial location with the target location and the vehicle is controlled according to the determined motion.
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24.发明授权公开(公告)号：US09568915B1
公开(公告)日：2017-02-14
申请号：US15041639
申请日：2016-02-11
发明人： Karl Berntorp , Stefano Di Cairano
CPC分类号： G05D1/0214 , B60W30/00 , B60W30/095 , B60W50/0097 , G05D2201/0213 , G06N7/005
摘要： A method controls a motion of a vehicle using a model of the motion of the vehicle that includes an uncertainty. The method samples a control space of possible control inputs to the model of the motion of the vehicle to produce a set of sampled control inputs and determines a probability of each sampled control input to move the vehicle into state satisfying constraints on the motion of the vehicle. The method determines, using the probabilities of the sampled control inputs, a control input having the probability to move the vehicle in the state above a threshold. The control input is mapped to a control command to at least one actuator of the vehicle to control the motion of the vehicle.
摘要翻译： 一种方法使用包括不确定性的车辆运动模型来控制车辆的运动。 该方法对车辆运动模型的可能的控制输入的控制空间进行采样，以产生一组采样的控制输入，并且确定每个采样的控制输入的概率以将车辆移动到满足对车辆的运动的限制的状态 。 该方法使用采样的控制输入的概率来确定具有在高于阈值的状态下移动车辆的概率的控制输入。 控制输入被映射到对车辆的至少一个致动器的控制命令，以控制车辆的运动。
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公开(公告)号：US12078972B2
公开(公告)日：2024-09-03
申请号：US17654580
申请日：2022-03-12
发明人： Karl Berntorp , Marcel Menner
CPC分类号： G05B13/048 , G05B13/042 , G06N7/01
摘要： A probabilistic feedback controller for controlling an operation of a robotic system using a probabilistic filter subject to a structural constraint on an operation of the robotic system is configured to execute a probabilistic filter estimates a distribution of a current state of the robotic system given a previous state of the robotic system based on a motion model of the robotic system perturbed by stochastic process noise and a measurement model of the robotic system perturbed by stochastic measurement noise having an uncertainty modeled as a time-varying Gaussian process represented as a weighted combination of time-varying basis functions with weights defined by corresponding Gaussian distributions. The probabilistic filter recursively updates both the distribution of the current state of the robotic system and the Gaussian distributions of the weights of the basis functions selected to satisfy the structural constraint indicated by measurements of the state of a robotic system.
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26.发明授权公开(公告)号：US12005914B2
公开(公告)日：2024-06-11
申请号：US17651923
申请日：2022-02-22
发明人： Marcel Menner , Stefano Di Cairano , Karl Berntorp , Ziyi Ma
CPC分类号： B60W50/10 , B60W30/09 , B60W30/12 , B60W30/16 , B60W2050/0075 , B60W2556/45
摘要： A vehicle includes an advanced driver-assistance system (ADAS) configured to intervene in an operation of the control system by complementing or overriding the driving input in response to detecting a driving condition dependent on a calibration parameter indicative of a preference of execution of the driving maneuver. The ADAS is calibrated based on a crowd-local distribution function of the calibration parameter indicative of a distribution of the preference of execution of the driving maneuver by other drivers of other vehicles at a specific location or a specific environment in response to detecting that the vehicle approaches the specific location or the specific environment.
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公开(公告)号：US11947022B2
公开(公告)日：2024-04-02
申请号：US17216832
申请日：2021-03-30
摘要： A server jointly tracks states of multiple vehicles using measurements of satellite signals received at each vehicle and parameters of the probabilistic distribution of the state of each vehicle. The server fuse states and measurements into an augmented state of the multiple vehicles and an augmented measurement of the augmented state subject to augmented measurement noise defined by a non-diagonal covariance matrix with non-zero off-diagonal elements, each non-zero off-diagonal elements relating errors in the measurements of a pair of corresponding vehicles. The server executes a probabilistic filter updating the augmented state and fuses the state of at least some of the multiple vehicles with a corresponding portion of the updated augmented state.
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公开(公告)号：US11932262B2
公开(公告)日：2024-03-19
申请号：US17365338
申请日：2021-07-01
发明人： Rien Quirynen , Karl Berntorp
CPC分类号： B60W50/0097 , B60W50/06 , B60W60/001 , G05B13/048 , B60W2050/0052
摘要： Stochastic nonlinear model predictive control (SNMPC) allows to directly take uncertainty of the dynamics and/or of the system's environment into account, e.g., by including probabilistic chance constraints. However, SNMPC requires the approximate computation of the probability distributions for the state variables that are propagated through the nonlinear system dynamics. This invention proposes the use of Gaussian-assumed density filters (ADF) to perform high-accuracy propagation of mean and covariance information of the state variables through the nonlinear system dynamics, resulting in a tractable SNMPC approach with improved control performance. In addition, the use of a matrix factorization for the covariance matrix variables in the constrained optimal control problem (OCP) formulation guarantees positive definiteness of the full trajectory of covariance matrices in each iteration of any optimization algorithm. Finally, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is described that considerably reduces the computational cost and allows a real-time feasible implementation of the proposed ADF-based SNMPC method to control nonlinear dynamical systems under uncertainty.
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29.发明授权公开(公告)号：US11879964B2
公开(公告)日：2024-01-23
申请号：US16939250
申请日：2020-07-27
发明人： Pu Wang , Karl Berntorp , Yuxuan Xia , Hassan Mansour , Petros Boufounos , Philip Orlik
IPC分类号： G01S13/72 , G06T7/277 , G06N20/00 , B60W30/08 , G06V20/58 , G06F18/214 , G06F18/2113 , G06F18/2415
CPC分类号： G01S13/726 , B60W30/08 , G06F18/214 , G06F18/2113 , G06F18/2415 , G06N20/00 , G06T7/277 , G06V20/58 , B60W2420/52
摘要： A system and a method for tracking an expanded state of an object including a kinematic state indicative of a position of the object and an extended state indicative of one or combination of a dimension and an orientation of the object is provided herein. The system comprises at least one sensor configured to probe a scene including a moving object with one or multiple signal transmissions to produce one or multiple measurements of the object per the transmission, and a processor configured to execute a probabilistic filter tracking a joint probability of the expanded state of the object estimated by a motion model of the object and a measurement model of the object, wherein the measurement model includes a center-truncated distribution having predetermined truncation intervals. The system further comprises an output interface configured to output the expanded state of the object.
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公开(公告)号：US20220187793A1
公开(公告)日：2022-06-16
申请号：US17117159
申请日：2020-12-10
发明人： Karl Berntorp , Rien Quirynen , Sean Vaskov
IPC分类号： G05B19/4155 , G05D1/00
摘要： A stochastic model predictive controller (SMPC) estimates a current state of the system and a probability distribution of uncertainty of a parameter of dynamics of the system based on measurements of outputs of the system, and updates a control model of the system including a function of dynamics of the system modeling the uncertainty of the parameter with first and second order moments of the estimated probability distribution of uncertainty of the parameter. The SMPC determines a control input to control the system by optimizing the updated control model of the system at the current state over a prediction horizon and controls the system based on the control input to change the state of the system.
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