127 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 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.