4results about How to "Guaranteed optimality" patented technology

This invention proposes an artificial intelligence-based vehicle control method and system, comprising: acquiring raw data of the vehicle's surrounding environment to construct a digital twin model of the vehicle's surrounding environment; identifying the current driving intention type based on environmental information, historical vehicle driving data, and driver operation behavior characteristics, and calculating a multi-dimensional risk situation index by combining the spatiotemporal correlation between obstacle movement trajectories and the vehicle's current state; performing simulation based on the multi-dimensional risk situation index to generate an adaptive control strategy sequence and convert it into control commands; monitoring the vehicle's actual response parameters after the control commands are executed, and calculating the deviation between the predicted state and the vehicle's actual state; when the deviation exceeds a preset threshold, fine-tuning the deep neural network and reinforcement learning model using the actual response parameters; and storing the optimized model parameters and synchronizing them to other vehicle nodes in the vehicle network. This invention achieves accurate environmental perception and intelligent, flexible generation of control strategies.

The invention discloses an alternating current servo system control method for controlling the stability of an obstacle breaking vehicle body, and the method comprises the steps: constructing an unscented Kalman filter (UKF) load observer based on the UKF, and estimating a load torque value and the rotor angular speed of a permanent magnet synchronous motor in real time through the UKF load observer; feeding back a rotor angular velocity output based on a UKF load observer to an extended state observer (ESO), introducing a load torque value as a feedforward compensation amount into an NLSEF control law, introducing a Newton-Raphson optimization algorithm (NRBO), and performing global optimization on parameters of an active disturbance rejection controller (ADRC) by taking an improved ITAE index as a fitness function; compared with the prior art, the method can improve the tracking precision and the anti-interference performance of the barrier-breaking vehicle under the complex dynamic working condition, and guarantees the stability of the barrier-breaking vehicle body in the advancing process.

This application discloses a method, system, device, equipment, and medium for controlling the facial expressions of a bionic robot, relating to the field of simulated robot control. The method includes: constructing a virtual model of the bionic robot, setting several virtual attachment points, acquiring a continuous facial expression sequence, extracting several target facial expression moments and corresponding target facial expression states from the continuous facial expression sequence; setting the target facial expression state at each target facial expression moment as the target to be imitated, using the displacement parameters of each servo motor as optimization variables, and constructing an objective function based on the geometric error between the actual position of the virtual attachment point in the virtual model and the target position under the target facial expression state; iteratively sampling and simulating in the parameter space that satisfies the servo motor constraints, calculating the objective function values ​​corresponding to the displacement parameters, until the optimal servo motor parameter set at the corresponding target facial expression moment is obtained, and combining it into a continuous control sequence; converting the continuous control sequence into facial expression control commands, and deploying them to the servo motors of the bionic robot for execution. This achieves optimization of the bionic head's facial expressions.

The application discloses a robot autonomous navigation method and device fusing a patrol strategy, comprising the following steps: matching and screening from a pre-constructed feature vector library through a preset patrol rule, and extracting key nodes; performing cluster analysis on the extracted key nodes, and removing redundant nodes with too close distances to obtain topological nodes; analyzing the spatial connectivity among the topological nodes, establishing edge association among the nodes, and generating a topological graph; calculating the shortest comprehensive cost path from a start matching node to an end matching node in the topological graph to obtain a global node sequence; performing interpolation processing on the global node sequence, and generating a continuous and smooth trajectory curve as a navigation path of the robot. The application realizes environment space dimension reduction through construction of a topological graph, and fuses a preset patrol rule to perform node extraction and path planning, so that the search efficiency is significantly improved, and the calculation amount is reduced while the optimal path is ensured.