Mitsubishi Electric Research Laboratories, Inc.

Abraham P Vinod

Stefano Di Cairano

Avishai Weiss

Karl Berntorp

Saleh Nabi

Marcus Greiff

Gautam Dasarathy

Giorgos Mamakoukas

Parth Thaker

Terrence Skibik

The present disclosure discloses a system and method for controlling motion of a vehicle. The method comprises collecting a signal indicative of objectives of the motion and a value of the disturbances, and minimizing an objective function subject to constraints defined by the objectives of the motion to produce optimized values of parameters of a sequence of splines. The method further comprises controlling the motion of the vehicle based on a model of differentially flat dynamics of the vehicle according to an optimal path defined by the optimized values of the parameters of the sequence of splines.

System and method for controlling motion of a vehicle in a stochastic disturbance field

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 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

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

To control a motion of a device subject to constraints, a sequence of states and corresponding control inputs are transformed into a lifted space to determine a linear model of the dynamics of the device in the lifted space by minimizing fitting errors between the lifted states and approximation of the lifted states according to the linear control law. The fitting errors define an error model as a function bounding a data-driven envelope of a Lipschitz continuity on the fitting errors allowing to solve an optimal control problem in the lifted space according to the linear model subject to the constraints reformulated based on an evolution of the error model. The control input in the lifted space is transformed back to the original space for control.

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Cambridge, MA, United States of America

Abraham P Vinod