Abstract: A controller of a machine determines jointly a sequence of control inputs defining a state trajectory of the machine and a desired knowledge of the environment by solving a multivariable constrained optimization of a model of dynamics of the machine relating the state trajectory with the sequence of control inputs subject to a constraint on admissible values of the states and the control inputs defined based on the desired knowledge of the surrounding environment represented by the state of the environment and the uncertainty of the state of the environment determined from the measurements of the environment. In such a manner, the controller performs joint but imbalance optimization of the control inputs and the sensing instructions to the sensor for learning the environment.

Abstract: The present disclosure provides a controller for controlling an ego vehicle in an uncertain environment. The controller is caused to acquire knowledge of the environment from measurements associated with sensors the ego. The measurements are based on a state of the ego vehicle and sensing instructions associated with controlling an operation of the sensors. The controller is further caused to estimate a state of the environment, including uncertainty of a state of the at least one moving object or obstacle in the environment. Further a sequence of control inputs is determined by solving a multivariable and a multistage stochastic constrained optimization of a model of the motion of the ego vehicle. The controller is then caused to control the ego vehicle and the sensors based on the sequence of control inputs and the sequence of sensing instructions.

Abstract: A controller of a machine determines jointly a sequence of control inputs defining a state trajectory of the machine and a desired knowledge of the environment by solving a multivariable constrained optimization of a model of dynamics of the machine relating the state trajectory with the sequence of control inputs subject to a constraint on admissible values of the states and the control inputs defined based on the desired knowledge of the surrounding environment represented by the state of the environment and the uncertainty of the state of the environment determined from the measurements of the environment. In such a manner, the controller performs joint but imbalance optimization of the control inputs and the sensing instructions to the sensor for learning the environment.