Mitsubishi Electric Research Laboratories, Inc.

Mitsubishi Electric Corporation

Mitsubishi Electric US

Arvind Raghunathan

Devesh Jha

Daniel Nikolaev Nikovski

Diego Romeres

Yuki Shirai

David Bergman

Ajit Gopalakrishnan

Kenji Ueda

Stefano Di Cairano

Satoru Takahashi

Toshihiro Wada

Zafer Sahinoglu

Ming-Yu Liu

Yuichi Taguchi

Mouhacine Benosman

Oncel C Tuzel

Srikumar Ramalingam

Christopher Laughman

Radu Ioan Corcodel

Seiji Shaw

Andre Cire

Hiroyuki Hashimoto

Carlos Nohra

Yunwen Xu

Nikolaos V Sahinidis

Thiago Serra

Patrik Kolaric

The present disclosure provides a system and a method for controlling a fleet of Unmanned Aerial Vehicles (UAVs) to perform a mission. The method comprises receiving request data for the mission, obtaining assignments corresponding to one or more UAVs of the fleet of UAVs, and obtaining assignment data corresponding to each assignment. The method further comprises determining an assignment for the one or more UAVs by performing a first stage of a multi-stage optimization subject to a first set of constraints and updating the first list of assignments by adding the determined assignment to the first list of assignments. The method further comprises determining a second list of assignments for the one or more UAVs by performing a second stage of the multi-stage optimization subject to a second set of constraints and controlling the one or more UAVs based on the second list of assignments.

System and method for controlling a fleet of unmanned aerial vehicles based on multi-stage optimization

The present disclosure discloses a system and a method for controlling an operation of a robotic arm holding a tool for manipulating object. The method comprises collecting measurements of tactile sensors associated with the robotic arm, estimating, based on the collected measurements and constraints enforced by a Model Predictive Controller (MPC), a feedback signal indicative of a pose of the object, and executing the MPC configured to produce, based on the pose of the object, control commands for actuators of the robotic arm by optimizing a cost function minimizing a deviation of the pose of the object from a target pose of the object. The optimization of the cost function is subject to the constraints that constrain one or more forces acting on the object at one or more point of contacts to be within corresponding friction regions. The method further comprises controlling the actuators according to the control commands.

System and method for controlling an operation of a robotic arm

A manipulation controller is provided for reorienting an object by a manipulator of a robotic system. The manipulation controller includes an interface controller configured to acquire measurement data from sensors arranged on the robotic system, at least one processor, and a memory configured to store a computer-implemented method. The instructions of the method include acquiring measurement data from vision sensors and force sensors arranged on the robotic system, determining an input-output relation for the object based on a nonlinear static model representing input-output relationships between contact forces and movements of the object on the workbench, representing interaction between the object and the manipulator using complementarity constraints to capture the contact state between the object and the manipulator, formulating a representation for frictional stability of the object based on the non-linear static model at the external contacts with the workbench; formulating a bilevel optimization problem so as to maximize the frictional stability over a position trajectory of the object being manipulated on the workbench, estimating uncertainty value in physical parameters to be compensated by performing the bilevel optimization problem, solving the bilevel optimization problem using the non-linear optimization solver and generating control data with respect to a sequence of the contact forces being applied to the object by using the manipulator.

System and method for robust pivoting for re-orienting parts during robotic assembly

A device for controlling an operation of a system performing a task determines a current control input based on the feedback signal by solving a polynomial optimization of a polynomial function with a reformulation derived by introducing additional variables reducing the degree of the polynomial function till a target degree subject to constraints on a structure of the additional variables. The device solves a mixed-integer optimization problem to find a subset of encodings among all possible encodings of factorizations of the polynomial function that reduce the degree of the polynomial function to the target degree with a predetermined minimum number of additional variables and selects an optimal encoding from the subset of encodings with an optimal relaxation bound.

Device for controlling a system with polynomial dynamics

A controller for controlling an operation of a robot to execute a task is provided. The controller comprises a memory configured to store a set of dynamic movement primitives (DMPs) associated with the task. The set of DMPs comprise a set of at least two dynamical systems: a function representing point attractor dynamics and a forcing function corresponding to a learned demonstration of the task. The controller comprises a processor configured to transform the set of DMPs to a set of constrained DMPs (CDMPs) by determining a perturbation function associated with the forcing function. The perturbation function is associated with a set of operational constraints. The processor is further configured to solve, a non-linear optimization problem for the set of CDMPs based on the set of operational constraints and generate, a control input for controlling the robot for executing the task, based on the solution.

System and method for controlling a robot using constrained dynamic movement primitives

The present disclosure provides a system and a method for controlling an operation of a manipulation system. The method comprises formulating an optimization problem based on a Stochastic Discrete-time Linear Complementarity Model (SDLCM) of a manipulation task, and a sample average approximation; solving the formulated optimization problem using an important-particle algorithm to compute an optimal state trajectory, an optimal feedforward control trajectory, an optimal complementarity variable trajectory, a state feedback gain, and a complementarity feedback gain; collecting, measurements indicative of a current state trajectory and a current complementarity variable trajectory; determining an online control input based on the optimal feedforward control trajectory, a deviation of the current state trajectory from the optimal state trajectory, a deviation of the current complementarity variable trajectory from the optimal complementarity variable trajectory, the state feedback gain, and the complementarity feedback gain; and controlling actuators of the manipulation system according to the online control input.

System and method for controlling an operation of a manipulation system

The present disclosure discloses a system and a method for controlling an operation of a machine according to a task. The method comprises formulating an original quadratic program (QP) for optimizing an objective function subject to equality constraints and inequality constraints, lifting the equality constraints and the inequality constraints into a lifted space by a lifting operation introducing an additional non-negative variable, and transforming the objective function of the original QP into a quadratic objective function. The quadratic objective function subject to the lifted equality and inequality constraints forms a homogeneous QP in the lifted space. The method further comprises solving the homogeneous QP to produce a solution in the lifted space and controlling the machine according to an infeasibility protocol when a value of the additional non-negative variable in the solution in the lifted space equals zero.

System and method for controlling an operation of a machine according to a task

System and methods for controlling a movement of elevator cars of an elevator system. Determine, for each elevator car, an individual waiting time of accommodating each hall call. Determine, for each pair of hall calls assigned to each elevator car, a pairwise delay over the individual waiting time of each hall call in the pair caused by a joint assignment of the elevator car to accommodate the pair of the hall calls. Approximate a cumulative waiting time of an assignment of the elevator cars. Determine the assignment of the elevator cars using a branch and bound algorithm using spectral relaxations based on a generalized eigenvalue and spectral branching that assigns the plurality of hall calls to the elevator cars to minimize the approximated cumulative waiting time. Use a controller for controlling the movement of the elevator cars according to the assignment.

Systems and methods for group elevator scheduling based on quadratic semi-assignment programs

A robotic system for manipulating an object with a robotic manipulator is provided. The robotic system is configured to collect a digital representation of a task for manipulating the object; solve a robust control problem to optimize a sequence of control forces to be applied by the robotic manipulator to change a state of the object, where an evolution of the state of the object is governed by a stochastic complementarity system modeling the task with a predefined probability. The robust control problem optimizes a cost function to generate the sequence of control forces performing the task subject to joint chance constraints including a first chance constraint on the state of the object being manipulated and a second chance constraint on stochastic complementarily constraints modeling manipulation of the object. The robotic system is further configured to control the manipulation of the object based on the sequence of control forces.

System and method for robust robotic manipulation using chance constrained optimization

A controller controls a motion of an object performing a task for changing a state of the object from a start state to an end state while avoiding collision of the object with an obstacle according to an optimal trajectory determined by solving an optimization problem of the dynamics of the object producing an optimal trajectory for performing the task subject to constraints on a solution of first-order stationary conditions modeling a minimum distance between the convex hull of the object and the convex hull of the obstacle using complementarity constraints.

Object manipulation with collision avoidance using complementarity constraints

A device for controlling an operation of a system performing a task is disclosed. The device submits a sequence of control inputs to the system thereby changing states of the system according to the task and receives a feedback signal. The device determines a current control input for controlling the system based on the feedback signal including a current measurement of a current state of the system by solving a polynomial optimization of a polynomial function with a reformulation derived by introducing additional variables reducing a degree of the polynomial function till a target degree subject to constraints on a structure of the additional variables. The device solves a mixed-integer optimization problem to find an optimal solution among all possible encodings of factorizations of the polynomial function that reduces the degree of the polynomial function till the target degree with a minimum number of additional variables.

A system for designing a circuitry configuration of heat-exchanger units includes an interface to acquire design parameters the heat-exchanger units, a memory to store computer-executable programs including a relaxed decision diagram formation module, and a processor, in connection with the memory, configured to perform the computer-executable programs. The computer-executable programs include steps of providing a configuration of the heat-exchanger units, providing the design parameters of the heat-exchanger units acquired via the interface, generating a relaxed decision diagram based on the design parameters, creating constraints with respect to connections of the heat-exchanger units according to the relaxed decision diagram, and generating feasible configurations of the heat-exchanger units by a mixed-integer-programing method using the constraints.

Method and system for circuiting in heat exchangers

A computer-implemented learning method for optimizing a control policy controlling a system is provided. The method includes receiving states of the system being operated for a specific task, initializing the control policy as a function approximator including neural networks, collecting state transition and reward data using a current control policy, estimating an advantage function and a state visitation frequency based on the current control policy, updating the current control policy using the second-order approximation of the objective function, a second-order approximation of the KL-divergence constraint on the permissible change in the policy using a quasi-newton trust region policy optimization, and determining an optimal control policy, for controlling the system, based on the average reward accumulated using the updated current control policy.

System and method for policy optimization using quasi-Newton trust region method

Systems and methods for scheduling early riders (ERs) and late riders (LRs) to vehicles in a multi-modal transportation network (MTN). Stored instructions, when executed, cause a processor to perform acts of forecasting a finite set of scenarios, each scenario having a possible set of forecast LRs (FLRs) itinerary requests. Iteratively, generate ER groups and FLR groups for each scenario, based on a desired time of arrival at a destination. Assign a commuter vehicle (CV) for each ER and FLR group in each scenario. Iteratively, determine for each ER and FLR group a start time and an arrival time at the destination in the corresponding CV, for which, the ER and FLR group are assigned. The iterations continue until a joint schedule for the ERs and the FLRs form each scenario that minimizes an objective function. Formulate assignment information, and transmit the assignment information to the ERs and the assigned CVs.

System and method for scheduling multiple modes of transport with incomplete information

A power generation planning system for controlling on/off sequence of generators according to operational parameters includes an interface to receive the operational parameters including a power demand, state-data of the generators and operational histories of the generators from a power control system, a memory to store an objective function, a mixed-integer programming solver, generator parameters of each the generators and planning modules including a state-space representation module, a variable assignment module, a network flow module and a tight constraint module, a processor to perform the planning modules based on the operational parameters received by the interface. The processor is configured to construct decision diagrams for each of the generators by using the state-space representation module, generate arc-variables representing state-transformations of the generators by assigning binary variables to arcs of the decision, generate network flow constraints to represent feasible operations of each of the generators, generate tight constraints of each of the generators by using the tight constraint module and formulate a mixed-integer problem, solve the mixed-integer problem, by using the mixed-integer programming solver, based on the states-data of the generators, the network flow constraints and the tight constraints, and transmit on/off sequence data of each of the generators obtained from the solved mixed-integer problem to a power control system via the interface.

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Brookline, MA, United States of America

Arvind Raghunathan