Mitsubishi Electric Research Laboratories, Inc.

Ford Global Technologies, LLC

Mitsubishi Electric Corporation

Stefano Di Cairano

Karl Berntorp

Avishai Weiss

Rien Quirynen

Uros Kalabic

Claus Danielson

Ankush Chakrabarty

Marcel Menner

Scott Bortoff

Abraham Goldsmith

Sohrab Haghighat

Philip Orlik

Yebin Wang

Kyeong Jin Kim

Marcus Greiff

Jianlin Guo

Andrei Kniazev

Heejin Ahn

Abraham P Vinod

Daniel Aguilar Marsillach

Eyad Zeino

Dimitar Petrov Filev

Kieran Parsons

Steven Joseph Szwabowski

Arvind Raghunathan

Christopher Laughman

Daniel J Burns

Akshay Bhagat

Mehmet Alphan Ulusoy

Spyridon Zafeiropoulos

Angelo Domenico Bonzanini

David Zlotnik

Jianbo Lu

Kwaku O Prakah-Asante

Gregory Michael Pietron

Bradley Dean Riedle

Yuji Fujii

Ilya V Kolmanovsky

John Ottavio Michelini

Diana Yanakiev

Matthew E Brand

Daniel Nikolaev Nikovski

Joseph F Kucharski

Mouhacine Benosman

Yiming Zhao

Diego Romeres

Toshisada Mariyama

Dan J Burns

Kyeong-Jin Kim

Di Chen

Greg Blaisdell Stevens

Fei Ye

Pedro Hespanhol

Yunus Emre Sahin

Gautam Dasarathy

Mohit Srinivasan

Perry R McNeille

Xuhui Feng

Jonathan Frey

Shreejith Ravikumar

Ali Mesbah

Purnanand Elango

Nobuyuki Yoshikawa

Jessica Leu

Gilsoo Lee

Ryan Caverly

Roya Firoozi

Ferdous Pervej

Ahmad Ahmad

Zejiang Wang

Alexander Schperberg

Terrence Skibik

Ziyi Ma

Rolf Findeisen

Giorgos Mamakoukas

Parth Thaker

Astha Tiwari

Shuangxuan Ran

Sixiong You

Abraham Puthuvana Vinod

BharaniPrabha Malladi

Kazuhide Okamoto

Tobias Baethge

Nilesh Suriyarachchi

Alex Walsh

Junqiang Zhou

Peizhen Zhu

Uros Kalabić

Hassan Iqbal

Jiaming Liang

Xiaodong Lan

Francesco Borrelli

Neera Jain

Yongfang Cheng

A control system for controlling a motion of an ego-vehicle traveling to a target destination is provided. The control system includes a memory and a processor to execute instruction stored by the memory. The memory stores multiple trajectory-generating functions corresponding to a maneuver defined by a parameter vector associated with a driving decision. The parameter vector is defined by one or multiple parameters. Each of the multiple trajectory-generating functions is configured to generate an achieving sequence of regions of states and values of the parameter vector reaching an input target region within a prediction horizon. The stored instructions cause the control system to test control admissibility of at least some of the driving decisions consistent with the target destination of the ego-vehicle at a current state. The stored instruction also caused the control system to control the ego-vehicle according to one of the admissible driving decisions.

Systems and methods for decision making for autonomous vehicles

A control system for controlling a legged robot comprises a processor and a memory. The control system initializes, in response to receiving a task, a probabilistic filter with parameters associated with a state of a reference trajectory of the legged robot, wherein the parameters are predetermined for the task and encode the reference trajectory including a combination of different reference trajectories for coordinated motion primitives of different actuators of the legged robot moving the legged robot according to the task, decodes the parameters to generate the reference trajectory, and executes, in response to receiving a feedback signal, the probabilistic filter to iteratively track the state of the reference trajectory satisfying a performance objective with respect to the state of the legged robot to update the parameters.

Task-specific and environment-specific adaptive control of legged robot

The present disclosure discloses a system and a method for controlling an operation of a system subject to an uncertainty of an operation variable of the system. The method comprises collecting a number of samples of the uncertainty of the operation variable, constructing, based on the collected samples, an empirical quantile function associated with the uncertainty of the operation variable, determining confidence bounds on the empirical quantile function to bound an approximation error between the empirical quantile function and a true quantile function, determining an uncertainty set based on the empirical quantile function bounded by the confidence bounds, reformulating, based on the uncertainty set, a chance constraint into a deterministic constraint, solving an optimal control problem subject to the deterministic constraint to produce one or more control commands to one or more actuators of the system, and controlling the operation of the system based on the control commands.

System and method for controlling an operation of a system subject to an uncertainty

A control system and a method for controlling search agents to perform search with noisy observations and probabilistic guarantees is provided. The control system collects confidence bounds of a probabilistic classification of at least one region within at least one path of a set of paths. The control system compares aggregations of the confidence bounds of the probabilistic classifications of each path of the set of paths based on the collected confidence bounds, a first path of a set of paths is selected, for visit by a first search agent based on the comparison. The control system commands the first search agent to visit the selected first path to collect measurements associated with each region within the selected first path. The control system updates the confidence bounds of the probabilistic classifications of each region within the selected first path based on the measurements associated with the corresponding regions.

Controlling search agents to perform search with noisy observations and probabilistic guarantees

A computer-implemented method is provided for training a global machine learning model using a learning server and a set of vehicle agents connected to roadside units (RSUs). It involves selecting agents, associating them with RSUs based on proximity, and transmitting training data and deadlines. Agents train models locally, which are then aggregated through RSUs to refine the global model to desired precision. The method includes steps of selecting vehicle agents from a pool of the vehicle agents connected to the RSUs, associating the selected vehicle agents and the RSUs respectively based on distances from the selected vehicle agents to the RSUs configured to provide measurements of the distances to the learning server, and transmitting a global model, a selected agent set and deadline thresholds in each global training round to the RSUs configured to transmit the global model and training deadlines to the selected vehicle agents.

Communication and computation aware distributed machine learning for vehicular networks

The present disclosure provides a system and a method for jointly controlling one or multiple connected and automated vehicles (CAVs) and one or multiple human-driven vehicles (HDVs) subject to integer constraints for crossing each of multiple intersections on a road. The method comprises collecting digital representation of states of each of the CAVs, HDVs, and traffic signs, solving an optimization problem jointly optimizing traffic flows based on a macroscopic traffic flow model in a centralized traffic controller (CTC) subject to convex relaxation of the integer constraints, solving a multi-variable mixed-integer programming (MIP) problem in each of multiple intersection traffic controllers (ITCs) optimizing a cost function and minimizing tracking errors in traffic flow values of a microscopic traffic flow model with respect to relaxed traffic flow values from the CTC, and transmitting the optimized values of the control commands to the corresponding CAVs and corresponding traffic signs.

Hierarchical optimization-based coordinated control of traffic rules and mixed traffic in multi-intersection environments

The present disclosure provides a feedback controller and method for controlling an operation of a device at different control steps. The feedback controller comprises at least one processor, and the memory having instructions stored thereon that, when executed by the at least one processor, causes the feedback controller, for a control step, to collect a measurement indicative of a state of the device at the control step, and execute, recursively until a termination condition is met, a probabilistic solver parameterized on a control input to an actuator operating the device to produce a control input for the control step. The feedback controller is further configured to control the actuator operating the device based on the produced control input.

System and method for controlling an operation of a device

A method for controlling a system by a controller comprises accepting a current state of the system and selecting, using a trained function of the current state, a solver from a set of solvers. The method further comprises solving an optimal control optimization problem using the selected solver to produce a current control input, such that for at least some different control steps, the predictive controller solves a formulation of the optimal control optimization problem with different solvers having different accuracies, requiring different computational resources, or both and submitting the current control input to the system thereby changing the current state of the system.

Apparatuses systems and methods for optimization-based control of physical systems

The present disclosure provides a system and a method for jointly controlling one or multiple connected autonomous vehicles (CAVs) and one or multiple manual connected vehicles (MCVs) moving to form traffic on the same or intersecting roads. The method includes collecting states of each of the CAVs, each of the MCVs, and each of traffic signs regulating the traffic, and solving a multi-variable mixed-integer problem (MIP) optimizing a cost function for values of control commands changing states of each CAV and values of control commands changing states of each of the traffic signs. The cost function is optimized subject to a motion model of each of the CAVs, subject to constraints modeling general traffic rules, subject to timing constraints, and subject to a motion model of each MCV. The method further includes transmitting the optimized values of the control commands to the corresponding CAVs and corresponding traffic signs.

System and method for jointly controlling connected autonomous vehicles (CAVs) and manual connected vehicles (MCVs)

The present disclosure provides a system and a method for controlling a motion of a spacecraft in a multi-object celestial system while avoiding an unauthorized entry into a keep-away region during a normal and an abnormal operation of the spacecraft. The method includes executing, during the normal operation of the spacecraft, a nominal control law subject to constraints on maintaining a state of the spacecraft within a union of a plurality of control invariant sets of values of the state of the spacecraft. The state of the spacecraft includes a location of the spacecraft and at least one or a combination of a velocity and an acceleration of the spacecraft. The method further includes executing, upon detecting the abnormal operation of the spacecraft, an abort control law associated with the control invariant set including a current state of the spacecraft.

System and method for controlling a motion of a spacecraft in a multi-object celestial system

A control system for controlling movement of a vehicle, is disclosed. The control system is configured to construct a graph having multiple nodes defining states of the vehicle. The multiple nodes comprise an initial node defining initial state and a target node defining target state. Each pair of nodes is connected by an edge defined by collision-free motion primitives where the nodes comprise motion cusps. Multiple nodes connected through edges form a first path. A first number of motion cusps in the first path is determined and the graph is expanded to add new nodes until a termination condition is met on determining that the first number of motion cusps is above a threshold. The expansion of the graph is subjected to a constraint associated with a total number of motion cusps. Further a second path is determined having lesser motion cusps than the first path.

System and method for controlling movement of a vehicle

The present disclosure discloses a system and a method for controlling a device using a compound probabilistic filter. The method comprises collecting a sequence of measurements indicative of the state of the device at different control steps. Further, the method comprises executing iteratively a compound probabilistic filter configured to track the state of the device at each of the different control steps using the sequence of measurements to produce a sequence of states of the device corresponding to the sequence of measurements. Furthermore, the method comprises controlling the device using the tracked state of the device.

System and method for controlling a device using a compound probabilistic filter

A computer-implemented method for maintaining a spacecraft near an orbit comprises steps of detecting that a distance from the spacecraft to the orbit is greater than a spacecraft threshold and in response, linearizing dynamics of the spacecraft from a current time over a time horizon with respect to a high-fidelity reference trajectory to produce a state transition matrix (STM) for an uncontrolled motion of the spacecraft within the time horizon. The STM includes non-expanding eigenvectors with magnitudes less than or equal to one and expanding eigenvectors with magnitudes greater than one. The method further comprises determining a control action that changes an upcoming state of the spacecraft to a linear combination of the non-expanding eigenvectors of the STM and generating a control command to an actuator of the spacecraft.

Eigenmotion control for near rectilinear halo orbits

A system for controlling motion of an articulated vehicle with one or multiple trailers is described. The system is configured to infer the state of a vehicle, determine a motion path with forward motions and backward motions for the vehicle based on the state, and formulate an optimal control problem for optimizing the motion path. The optimal control problem comprises an integral tracking error function indicating integral tracking error based on motion cusps relating to the motion path. The motion cusps indicate switching between forward motion and backward motion. The motion path is optimized over a prediction horizon based on solving the optimal control problem. A control command is generated for the vehicle based on the optimized motion path and a vehicle model, and the motion of the articulated vehicle is controlled based on the control command thereby changing its state.

System and method for controlling motion of articulated vehicles

A controller is provided for operating a system under admissible states. The controller includes an interface configured to connect the system storing a set of measured system states, a set of reference inputs and a set of system parameters in a storage arranged inside or outside the system, a memory storing measured system states, admissible reference inputs and admissible parameter sets and computer-executable programs including a parameter estimator and an adaptive reference governor (ARG), a processor, in connection with the memory. The processor is configured to perform the ARG and the parameter estimator. The parameter estimator extracts a pair of a reference input and the system state and compute a system parameter estimate based on the reference input and system state. The ARG is configured to update the reference input and compute a parameter-robust constraint admissible set based on the updated reference input and the system states, wherein the ARG generates and transmits a reference input to the system based on the parameter-robust constraint admissible set.

System and method for data-driven reference generation

Growing community of inventors

Somerville, MA, United States of America

Stefano Di Cairano