Mitsubishi Electric Research Laboratories, Inc.

Rien Quirynen

Stefano Di Cairano

Karl Berntorp

Yebin Wang

Ankush Chakrabarty

Eyad Zeino

Andrei Kniazev

Akshay Bhagat

Pedro Hespanhol

William T Vetterling

Scott Bortoff

Bingnan Wang

Uros Kalabic

Devesh Jha

Chungwei Lin

Diego Romeres

Claus Danielson

Marcus Greiff

Toshisada Mariyama

Siddarth Jain

Mohit Srinivasan

Yunus Emre Sahin

Weinan Gao

Astha Tiwari

Shuangxuan Ran

Sixiong You

Hassan Iqbal

Jiaming Liang

Xuhui Feng

Sean Vaskov

Nilesh Suriyarachchi

Jonathan Frey

Shreejith Ravikumar

Nobuyuki Yoshikawa

Roya Firoozi

Ahmad Ahmad

Zejiang Wang

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

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)

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

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

The present disclosure provides a system and a method for controlling a motion of a robot from a starting point to a target point within a bounded space with a floorplan including one or multiple obstacles. The method includes solving for an electric potential in a bounded virtual space formed by scaling the floorplan of the bounded space with the one or multiple obstacles and applying charge to at least one bound of the bounded virtual space while treating the scaled obstacles as metallic surfaces with a constant potential value, wherein the electric potential provides multiple equipotential curves within the bounded virtual space. The method further includes selecting an equipotential curve with a potential value different from a potential value of an obstacle equipotential curve, determining a motion path based on the selected equipotential curve, and controlling the motion of the robot based on the determined motion path.

System and method for controlling a motion of a robot

A controller for controlling a motion of at least one device subject to constraints on the motion, is disclosed. The controller comprises a processor and a memory, where the controller inputs parameters of the task including the state of the at least one device to a neural network trained to output an estimated motion trajectory for performing the task. Further, the controller extracts at least some of the integer values of a solution to a mixed-integer optimization problem for planning an execution of the task that results in the estimated motion trajectory. Further, the controller solves the mixed-integer optimization problem for the parameters of the task with corresponding integer values fixed to the extracted integer values to produce an optimized motion trajectory subject to the constraint and changes the state of the at least one device to track the optimized motion trajectory.

Controller for optimizing motion trajectory to control motion of one or more devices

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.

Stochastic nonlinear predictive controller and method based on uncertainty propagation by Gaussian-assumed density filters

A global multi-vehicle decision making system is disclosed for providing real-time motion planning and coordination of one or multiple connected and automated and/or semi-automated vehicles (CAVs) in an interconnected traffic network that includes one or multiple non-controlled vehicles (NCVs), one or multiple conflict zones and one or multiple conflict-free road segments. The system includes a receiver configured to acquire infrastructure sensing signals, at least one memory configured to store map and programs, and at least one processor configured to perform steps of formulating a global mixed-integer programming (MIP) problem using the infrastructure sensing signals, computing a motion plan for each CAV and each NCV in the traffic network by solving the global MIP problem, computing an optimal sequence of entering/exiting times and a sequence of average velocities for each CAV and each NCV, and computing a velocity profile and/or one or multiple planned stops for each CAV.

Global multi-vehicle decision making system for connected and automated vehicles in dynamic environment

A control system controls a vehicle using a probabilistic motion planner and an adaptive predictive controller. The probabilistic motion planner produces a sequence of parametric probability distributions over a sequence of target states for the vehicle with parameters defining a first and higher order moments. The adaptive predictive controller optimizes a cost function over a prediction horizon to produce a sequence of control commands to one or multiple actuators of the vehicle. The cost function balances a cost of tracking of different state variables in the sequence of the target states defined by the first moments. The balancing is performed by weighting different state variables using one or multiple of the higher order moments of the probability distribution.

Adaptive control of autonomous or semi-autonomous vehicle

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.

Stochastic model-predictive control of uncertain system

A control system for controlling a motion of a vehicle to a target driving goal uses a decision-maker configured to determine a sequence of intermediate goals leading to the next target goal by optimizing the motion of the vehicle subject to a first model and tightened driving constraints formed by tightening driving constraints by a safety margin, and uses a motion planner configured to determine a motion trajectory of the vehicle tracking the sequence of intermediate goals by optimizing the motion of the vehicle subject to the second model. The driving constraints include mixed logical inequalities of temporal logic formulae specified by traffic rules to define an area where the temporal logic formulae are satisfied, while the tightened driving constraints shrink the area by the safety margin, which is a function of a difference between the second model and the first model approximating the second model.

Adaptive optimization of decision making for vehicle control

To control a hybrid dynamical system, a predictive feedback controller formulates a mixed-integer nonlinear programming (MINLP) problem including nonlinear functions of continuous optimization variables representing the continuous elements of the operation of the hybrid dynamical system and/or one or multiple linear functions of integer optimization variables representing the discrete elements of the operation of the hybrid dynamical system. The MINLP problem is formulated into a separable format ensuring that the discrete elements of the operation are present only in the linear functions of the MINLP problem. The MINLP problem is solved over multiple iterations using a partial convexification of a portion of a space of the solution including a current solution guess. The partial convexification produces a convex approximation of the nonlinear functions of the MINLP without approximating the linear functions of the MINLP to produce a partially convexified MINLP.

Sequential convexification method for model predictive control of nonlinear systems with continuous and discrete elements of operations

A system is controlled by solving a mixed-integer optimal control optimization problem using branch-and-bound (B&B) optimization that searches for a global optimal solution within a search space. The B&B optimization iteratively partitions the search space into a nested tree of regions, and prunes at least one region from the nested tree of regions before finding a local optimal solution for each region when a dual objective value of a projection of a sub-optimal dual solution estimate for each region into a dual feasible space is greater than an upper bound or lesser than a lower bound of the global optimal solution maintained by the B&B optimization.

Controller with early termination in mixed-integer optimal control optimization

A predictive controller controls a system under uncertainty subject to constraints on state and control variables of the system. At each control step, the predictive controller solves an inequality constrained nonlinear dynamic optimization problem including probabilistic chance constraints representing the uncertainty to produce a control command, and controls an operation of the system using the control command. The predictive controller solves the dynamic optimization problem based on a two-level optimization that alternates, until a termination condition is met, propagation of covariance matrices of the probabilistic chance constraints within the prediction horizon for fixed values of the state and control variables with optimization of the state and control variables within the prediction horizon for fixed values of the covariance matrices.

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