129 results about "Mobile robot control" patented technology

The invention discloses a mobile robot control device giving consideration to field navigation patrol and transportation load, and relates to the field of robot control, and the device comprises a sensor data collection module which forms multi-source sensor data; the environment understanding module is used for predicting terrain risks and weather influences based on the feature vectors and outputting environment feature data and risk prediction; the navigation control module is used for navigating a path based on the environment characteristic data and generating a robot motion control instruction; the load management module adjusts parameters of the navigation control module based on the load state; the feedback adjusting module is used for dynamically adjusting control parameters according to the load state and feeding back the control parameters to the navigation control module to optimize and modify a robot motion control instruction; and the intelligent detection module is used for detecting environment abnormity and dangerous case events and outputting an alarm signal. The method has the advantages that accurate prediction of environmental risks is realized through multi-sensor layered feature extraction and attention weighted fusion, and motion parameters are dynamically adjusted in combination with a moment balance model.

The invention discloses a mobile robot control method and system, and relates to the technical field of mobile robot intelligent motion control, and the method comprises the steps: collecting environment feature data and motion state data; performing preprocessing and feature fusion to generate standardized state data; constructing a reinforcement learning training environment in an offline simulation environment, and obtaining a convergent reinforcement learning strategy model; inputting the current robot standardized state data into the reinforcement learning strategy model, and obtaining and outputting a preliminary motion control instruction and a reference trajectory; a prediction control model MPC is established, current robot standardized state data are input, short-time motion prediction and optimization are carried out, and a final control instruction meeting constraint conditions is obtained through solving; instructions are distributed to the four Mecanum wheel drivers, and the rotating speed and the direction of each wheel are adjusted. According to the invention, high-precision trajectory tracking, adaptive obstacle avoidance and energy efficiency optimization of the mobile robot in a complex dynamic environment are realized, and the system stability and the intelligent level are improved.

The application relates to a multi-arm mobile robot control system, which comprises a robot, each joint position of the mechanical arm of which is configured with a joint controller for driving joint movement; a robot controller for realizing the operation of joint motors according to control quantities; a joint position hard analysis unit for calculating the coordinate positions of each joint or mechanical arm in a space coordinate system to output results; and a joint space point position trajectory constraint model for forming a correction quantity of a joint and superimposing the correction quantity on an original control quantity of the joint to become a final control quantity for controlling the joint, the final control quantity being used for output to the joint controller to realize joint movement. The application provides a multi-arm mobile robot control system, which effectively realizes the cooperative control among multi-arm robots, avoids the mutual collision among mechanical arms, improves the avoidance and dynamic response speed of the mechanical arms, and improves the agility of robot actions.

The invention relates to the technical field of mobile robot control, in particular to a multi-modal sensing and decision-making integrated system for a mobile robot, and provides the following scheme: performing joint smooth state estimation on multi-source sensing data to construct a state version diagram containing a time evolution relationship and a version evolution relationship; and under the constraint of an action locking window, establishing an action prefix index structure in combination with a historical action sequence, executing limited search on the state version diagram, and generating an updated action sequence with the minimum difference from the historical action structure. According to the method, the unstable change of event triggering and state machine transition can be inhibited under the condition that the smooth state estimation result is subjected to backtracking correction, the causal consistency and reproducibility of the action sequence are kept, and the operation stability and decision reliability of the mobile robot under the complex working condition are improved.

A computer-implemented method of planning actions for a mobile robot in the presence of occlusion The method comprises determining a first occluded region in a mobile robot field of view at a first time; sampling a first set of agent particles within the first occluded region; planning, using a robotic planner, based on a location and a motion state of a first agent particle of the first set of agent particles a first mobile robot action, the first mobile robot action accounting for predicted motion of the first agent particle; generating a first mobile robot control signal for causing execution of the first mobile robot action; determining a second occluded region in the mobile robot field of view at a second time; computing a propagated set of agent particles for the second time based on the first set of agent particles and a particle motion model; assigning an occlusion weight to each agent particle of the propagated set of agent particles with respect to the second occluded region; sampling a second set of agent particles within the second occluded region based on the propagated set of agent particles and the occlusion weight assigned to each agent particle; planning, using the robotic planner, based on a location and a motion state of a second agent particle of the second set of agent particles a second mobile robot action, the second mobile robot action accounting for predicted motion of the second agent particle; and generating a second mobile robot control signal for causing execution of the second mobile robot action.

The invention relates to a multi-arm mobile robot control system, which comprises a robot, and a joint controller for driving a joint to move is arranged at each joint position of a mechanical arm of the robot; the robot controller is used for enabling the joint controller to execute operation of the joint motor according to the control quantity; the joint position hard analysis unit is used for calculating the coordinate position of each joint or the mechanical arm in a space coordinate system so as to output a result; and the joint space point locus constraint model is used for forming the correction amount of the joint and superposing the correction amount to the original control amount of the joint to form the final control amount for controlling the joint, and the final control amount is used for being output to the joint controller to realize joint movement. According to the multi-arm mobile robot control system provided by the invention, cooperative control among multi-arm robots is effectively realized, mutual collision among mechanical arms is avoided, the avoidance and dynamic response speed of the mechanical arms is increased, and the flexibility of robot actions is improved.

The invention relates to the technical field of following mobile robots, in particular to a following loading mobile robot control method based on gait recognition and inertia fusion. The method comprises the following steps: presetting a fixed track span as a fixed value L frame; acquiring image data of a target human body, and locking the target human body based on the image data; acquiring inertial data of the robot, fusing the image data and the inertial data, and tracking a target human body; calculating target tracking credibility based on the similarity between the tracking human body gait feature and the initial target human body gait feature; and completing a robot control decision according to the target tracking credibility. The problems that an existing robot depends on beacons and is prone to being lost and rigid in behavior are solved, and the robot is suitable for dynamic indoor scenes such as supermarkets and airports.

The invention relates to a multi-mobile-robot intermittent formation tracking control method for denial of service attack, and belongs to the technical field of multi-mobile-robot control. Aiming at the problems of poor anti-interference performance, waste of communication computing resources, uncontrollable convergence time and strong model dependence in the prior art, a remote control terminal and ultrasonic radio station cooperative anti-attack switching scheme is provided, a distributed observer is adopted to realize leader state estimation, resource consumption is reduced by combining intermittent communication and an intermittent control mechanism, and the reliability of the system is improved. And a preset time adjustment function ensures user-defined convergence, and a reinforcement learning network optimization control strategy is introduced. The anti-attack capability and the self-adaptive performance of the system are improved, high-precision formation tracking is realized, the communication and calculation burden is remarkably reduced, and the expansibility is high.

The invention discloses a robot AI model dynamic compression method and control system, and the method comprises the steps: obtaining the hardware state data and task type of a robot, and carrying out the normalization processing and storage of the hardware state data; based on the hardware state data and / or the task type, selecting a corresponding compression strategy in real time to compress the AI model so as to obtain a lightweight model, the compression strategy comprises at least one of compression of the AI model based on the hardware state data, compression of the AI model based on the hardware state data in combination with the reward function and the task type, and compression of the AI model based on the task type and / or the hardware state data. According to the method, the computing power of the robot control system, the complexity of the AI model and the computing speed can be balanced, the problem of how to effectively operate the AI small model in the mobile robot control system is solved, and the balance between the robot control precision and the speed is realized.

The invention discloses a crawler-type dredging robot slip rate optimization control method and system, and relates to the technical field of crawler-type mobile robot control, and the method comprises the steps: 1, importing a dredging robot three-dimensional diagram, carrying out the modeling of a crawler chassis, obtaining a kinetic model, applying torque drive in the model, and obtaining motion data; 2, establishing a dynamic mathematical model according to the dynamic characteristics of the dredging robot, and obtaining the relation between the motion state of the dredging robot and torque driving; 3, the slipping condition of the robot is analyzed through the motion data, and the slipping rate of the robot in actual motion is obtained; 4, according to a slip rate calculation result, identifying an optimal slip rate through a self-adaptive variable step size strategy; 5, designing a driving anti-slip controller of the fuzzy sliding mode according to the dynamic mathematical model, the slip rate in actual motion and the optimal slip rate; and 6, the motion of the robot is dynamically adjusted according to the driving anti-skid controller.

A mobile robot can include a chassis and support wheels configured to support the chassis on a ground surface. The mobile robot can have a drive assembly that includes a drive wheel mounted to a control arm for moving the mobile robot. The control arm can pivot about a pivot axis. The pivot axis can be rearward of the axis of rotation of the drive wheel. The pivot axis can be lower than the axis of rotation of the drive wheel. The pivot axis can be lower than the axis of rotation for one or more of the support wheels. A biasing member can bias the control arm downward. Braking using the drive wheel can increase the force of the drive wheel against the ground. Accelerating using the drive wheel can decrease the force of the drive wheel against the ground.

The present invention is an autonomous mobile robot (1) that moves by being guided by a plurality of signs that have a plurality of types of sizes, are aligned along a movement path (10), and includes a first sign and a second sign, the autonomous mobile robot (1) including: an imaging unit (26); a storage unit (25) storing individual identification information of each of a plurality of signs and an individual actual size of each of the plurality of signs; and a calculation unit (27) calculating a distance (D1) to the first sign on the basis of a size of the first sign on image data captured by the imaging unit (26) and the individual actual size of the first sign corresponding to the individual identification information of the first sign.

The invention discloses an industrial autonomous mobile robot control method, device and equipment and a medium, is applied to a robot control system based on a hierarchical architecture, and relates to the field of automation, and the method comprises the steps: analyzing an input operation text instruction, and generating a navigation task and a grabbing task of a robot based on an obtained structured subtask sequence; calling preset laser equipment according to the navigation task to generate a three-dimensional semantic map of a target end point corresponding to the operation text instruction, and performing path planning and obstacle avoidance through the three-dimensional semantic map to obtain a target path; calling a preset image acquisition device to acquire a material image of a preset material area according to the grabbing task, and processing the material image to generate a material grabbing parameter; and converting the target path and the material grabbing parameter into a robot scheduling instruction corresponding to the robot, and controlling the robot to execute a navigation task and a grabbing task through the robot scheduling instruction. Therefore, autonomous operation of the industrial robot can be realized in combination with semantic understanding.

The present invention relates to a mobile robot control method and system based on fault prediction. The system includes: a multimodal inverse kinematics solver that dynamically selects a solution mode based on the Jacobian matrix condition number. The system selects, from smallest to largest condition number, the transposed matrix method, damped least squares method, and constrained quadratic programming to calculate joint angle adjustments, adapting to adverse working conditions such as singular configurations; an adaptive integral controller that independently adjusts the position / attitude loop gain via a decoupling gain matrix, dynamically suppresses vibration and reduces steady-state error by combining error and fault index; and a fault prediction and reconstruction subsystem that calculates the joint fault index in real time. This index integrates torque deviation, temperature, harmonic distortion, vibration, and wear data, and implements a graded response. The system significantly improves singularity stability, control decoupling, and fault response speed, reducing the risk of secondary faults.

1.The name of the design product: intelligent control system box. 2.The use of the design product: the design product belongs to the intelligent robot motion control hardware system platform, which can be used for commercial or industrial floor cleaning robots, patrol robots, sweeping robots, carrying robots and other mobile robot control system boxes. 3.The design points of the design product: in shape. 4.The picture or photo that best indicates the design points: perspective view 1.

This application discloses a lidar, a control method, and related equipment, relating to the field of mobile robot control. The lidar includes: a laser emitting component disposed on a rotating body of the lidar, the laser emitting component being used to emit coded laser light, the coded laser light being used for ranging and communication; a photosensitive component disposed on the rotating body, the photosensitive component being used to receive reflected laser light to obtain ranging information, the reflected laser light being the laser light reflected from the coded laser light onto the measured object and then onto the lidar; and an optoelectronic component disposed on the substrate of the lidar, the optoelectronic component being used to receive the coded laser light to obtain communication information.

The invention discloses a mobile robot control method and system based on multi-source fusion dynamic trajectory re-planning. The method comprises the steps that vehicle kinematics data and environment characteristic observation data are acquired in real time; state prediction is carried out based on a kinematic model; performing multi-dimensional feature extraction and confidence evaluation on the environment features; carrying out measurement updating by utilizing extended Kalman filtering; performing time domain differential analysis on the posterior state; when the state jump is detected, generating a smooth dynamic transition track by taking the current posterior state as a starting point; acquiring predicted curvature information of the front path from the global map; inputting the confidence score and the predictive curvature into a fuzzy logic controller, and calculating the adjustment amount of PID control gain; and calculating a motor control instruction based on the dynamic transition trajectory by using an adaptive PID algorithm, and outputting a final control signal. According to the method, high-precision positioning and stable control can be realized only by depending on a low-cost vehicle-mounted sensor, the cost is low, the universality is high, and the method is suitable for various complex indoor scenes.

The present disclosure relates to distributed positioning systems and methods and self-locating devices. A self-locating device calculates its position relative to transceivers using time-tagged signals transmitted by the transceivers as part of a distributed positioning system. The transceivers and self-locating devices are arranged to use digital and analog reception and transmission electronics and one or more highly precise clocks, compensation units, positioning units, position calibration units, scheduling units or synchronization units for highly precise time tagging. The transceivers and self-locating devices are further arranged to allow full scalability in the number of self-locating devices and to allow robust self-locating with latency and update rates useful for high performance applications such as autonomous mobile robot control.

The invention provides a hippocampus and prefrontal cortex function inspired mobile robot behavior decision-making method, and relates to the technical field of mobile robot control, the current state and the target position of a mobile robot are obtained from the environment, and a model-based reinforcement learning module is constructed through the spatial cognitive function of a hippocampus; reducing the dimension of the current state into a two-dimensional discrete state, inputting the two-dimensional discrete state into a model-based reinforcement learning module, and outputting an action to a decision controller; a model-free reinforcement learning module is obtained based on the basal ganglion and hippocampus memory function, the current state is input into the model-free reinforcement learning module, an action is output to a decision controller, and the decision controller is constructed by simulating the function that the prefrontal cortex is switched between the model-based reinforcement learning module and the model-free reinforcement learning module; and the decision controller outputs an execution action to complete the behavior decision of the mobile robot, so that the mobile robot is ensured to make a more reliable decision, and the performance of the mobile robot in the aspect of optimal decision is improved.

The application provides a mobile robot control method and device for server carrying, the method comprising: realizing accurate positioning and mapping through a multi-sensor fusion SLAM navigation technology, accurately identifying a target cabinet through a 3D vision system and obtaining installation position information, calculating deviation parameters of a current pose of the server and a target installation position through a feature matching algorithm, and coordinating pose adjustment of a cargo platform and a lifter body based on deviation compensation. Through an integrated multi-dimensional pose compensation control mechanism, the application realizes full-process automatic control of the mobile robot from navigation positioning to accurate alignment, and effectively improves the positioning accuracy and success rate of automatic server installation.

A self-localizing apparatus uses timestampable signals transmitted by transceivers that are a part of a distributed localization system to compute its position relative to the transceivers. Transceivers and self-localizing apparatuses are arranged for highly accurate timestamping using digital and analog reception and transmission electronics as well as one or more highly accurate clocks, compensation units, localization units, position calibration units, scheduling units, or synchronization units. Transceivers and self-localizing apparatuses are further arranged to allow full scalability in the number of self-localizing apparatuses and to allow robust self-localization with latencies and update rates useful for high performance applications such as autonomous mobile robot control.

By improving the responsiveness of the drive unit, the disruption of the movement of the mobile body caused by the reversal of the wheels is reduced, enabling stable movement of the mobile body. [Solution] The mobile robot control device 10 includes a robot speed target value acquisition unit 11 that acquires a first speed target value which is the speed target value of the mobile robot 100; a steering axis angle target value calculation unit 12 that calculates an angle target value for the steering axis 23 of the active caster 20 based on the first speed target value; a steering axis angle detection unit 13 that detects the angle of the steering axis 23; a correction speed calculation unit 14 that calculates a correction speed for the drive unit including the first motor 24 and the second motor 25 that promotes the turning of the wheels based on the angle target value and the detected angle; a speed conversion unit 15 that converts the first speed target value into a second speed target value which is the speed target value for the drive unit; and a synthesis unit 16 that synthesizes the second speed target value and the correction speed.

Embodiments of the present application provide a rudder wheel calibration method and device for a mobile robot and a mobile robot, and relate to the technical field of mobile robots. The method comprises: determining a to-be-calibrated rudder wheel group of the mobile robot; performing first calibration processing on each rudder wheel in the to-be-calibrated rudder wheel group in a manner of controlling the mobile robot to perform a plurality of first movement processes based on a calibration route, to obtain a first difference value between a real angle of each rudder wheel in the to-be-calibrated rudder wheel group and an angle fed back by a corresponding rudder wheel encoder; wherein in the first movement process, a vehicle head of the mobile robot at a starting position on the calibration route is parallel to the calibration route, and each rudder wheel in the to-be-calibrated rudder wheel group is oriented to a current actual zero position and a rudder wheel other than the to-be-calibrated rudder wheel group remains in a non-force mode; and determining a calibration result of the rudder wheel based on the obtained first difference value. Through the present solution, accurate calibration of the rudder wheel of the mobile robot can be achieved.

The invention relates to a method (100) for generating mutually synchronized control signals by means of a control system (300) to an elevator system (110) located in a building (100) and to at least one mobile robot (200) operating in the building. The method comprises: receiving (510) a service request from an external system (310); detecting (520) that completion of the requested service requires service from the elevator system (110) and the at least one mobile robot (200); generating (530) a first control signal to the elevator system (110) and a second control signal to the at least one mobile robot (200), the control signals causing the elevator system (110) and the at least one mobile robot (200) to operate in a synchronized manner to complete the service. The invention also relates to a control system and a computer program product.

The invention relates to a ground adaptive mobile robot control method based on Lyapunov stability and a backstepping method, and the method specifically comprises the steps: building a mobile robot motion model which comprises a mobile coordinate system and a fixed reference coordinate system, and constructing an error prediction parameter of a mobile robot; designing a Lyapunov function V1 (x) containing a saturation function according to the error prediction parameters, deducing and calculating an expected control rate omega d of the mobile robot, introducing auxiliary control input v, calculating a steering rate error e omega of the mobile robot according to the auxiliary control input v, and designing a Lyapunov function V2 (x1) containing the saturation function according to the error prediction parameters; in combination with the expected control rate omega d of the mobile robot, auxiliary control input v is obtained through deduction and calculation according to a backstepping method, and finally a real-time steering speed instruction omega c of the mobile robot is obtained through calculation according to the auxiliary control input v.

A mobile robot, a controller, and a mobile robot control method are provided. The mobile robot includes a processor. The processor is configured to execute program instructions to implement the following steps: when it is determined that the mobile robot deviates from a preset path, determining a return point of the preset path that is closest to the mobile robot; generating a temporary path based on the return point; and driving the mobile robot to return to the preset path according to the temporary path.

To provide a technology for easily controlling the group shape of a mobile robot.SOLUTION: A control device for controlling a plurality of mobile robots, the control device comprising: a target position determination unit configured to give input information including information for specifying a shape to be expressed by the plurality of mobile robots to a large scale language model, acquire output information related to a target position of each of the plurality of mobile robots for expressing the shape from the large scale language model, and determine the target position of each of the plurality of mobile robots based on the output information; and an output unit configured to output the target position to the plurality of mobile robots for movement of the plurality of mobile robots.SELECTED DRAWING: Figure 1

This invention relates to the field of mobile robot control and path planning technology, and particularly to a dynamic planning method for multi-robot cooperative patrol paths considering energy constraints. The method includes: first, fusing information from multiple sensor sources to acquire patrol area, key monitoring points, and robot energy status data; dynamically dividing the area into sub-regions using remaining battery life as a hard constraint, combined with task load and path constraints, and generating an initial patrol path through weighted coverage and energy consumption prediction; updating battery power in real time during execution and comparing local replanning results; initiating cooperative requests including incomplete paths when battery power is insufficient, and achieving cross-robot cooperative optimization based on path length, energy consumption, and patrol priority; finally, generating a review report based on actual energy consumption and path adjustment records, updating the energy consumption prediction model and allocation strategy, and using it for the next round of partitioning. This invention significantly improves the patrol reliability and energy utilization efficiency of multiple robots in dynamic environments.

This utility model discloses a control circuit for driving a mobile robot, including a charger module, a power supply module, a communication module, a control module, an IMU module, and an interface module. The power supply module includes a power switching module, a power filtering and voltage regulation module, a power management module, a power monitoring module, and a key switch for a host computer. The power management module includes a power control module and a power distribution module. The power control module receives voltage requirements from different circuit components and transmits them to the power distribution module. The power distribution module converts the input voltage into a specific output voltage to meet the needs of different circuit components. Through high-speed data transmission, coordinated and precise control, and real-time status feedback between the power supply module, control module, and communication module, the dynamic stability of the mobile robot's drive control, the response speed of the mobile robot, and its endurance are improved.

A mobile robot control system according to an embodiment is a mobile robot control system configured to control a mobile robot, a shape of the mobile robot having long sides and short sides in a top view, and the mobile robot control system including: an area setting unit configured to set a virtual bumper area defined by a first area and a second area, the first area being defined by a plurality of circles arranged along the long-side direction and the second area being formed by moving the first area in accordance with a moving speed of the mobile robot; and a control unit configured to control the mobile robot so as to decelerate or stop when the nearby object enters the virtual bumper area. Control may be performed using a machine learning model generated by supervised learning or the like.