426 results about "Robot dynamic" patented technology

The invention relates to the technical field of intelligent assessment and decision making, in particular to a robot dynamic risk assessment and decision making system and method based on multi-modal perception, and the system comprises a multi-modal sensor module which is used for collecting environment vision, acoustics, mechanics and position data in real time; the edge calculation unit is used for carrying out space-time alignment and feature fusion on the sensor data; the dynamic risk assessment model is used for integrating the environment uncertainty quantification module and the robot state prediction module based on a reinforcement learning framework; the decision execution interface is used for outputting a risk level and obstacle avoidance, speed reduction and shutdown instructions; by integrating visual, acoustic, mechanical and position multi-source sensor data and the like, the system can comprehensively capture various risk factors in a complex dynamic environment, so that the defect that a traditional single sensor system is insufficient in sensing dimension is overcome, and the system is particularly suitable for terrains and weather conditions with variable regions.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention discloses a robot dynamic environment adaptive sensing and navigation system based on a three-dimensional laser radar, relates to the technical field of robots, and solves the technical problems that comprehensive environment information is difficult to obtain, and the weight is difficult to adjust by fusing weather types and sensor confidence coefficients. Comprising the following steps: generating original point cloud data by combining a bionic compound eye type laser radar with a silicon photon integrated chip; marking point clouds based on a KITTI data set, performing preprocessing, training a Transform model to output a dynamic obstacle mask, and filtering background point clouds; a weather detection model is constructed, the weight is dynamically adjusted according to the weather type and the sensor confidence coefficient, and position and attitude estimation is fused; laser radar point cloud constructs a geometric map, a camera depth map generates a dense map, and semantic tags are mapped to generate an environmental semantic map; and converting the environmental semantic map into a three-dimensional grid map, and planning an obstacle avoidance path based on the grid map by using an A * algorithm.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention relates to a robot dynamic grabbing control method based on visual sense and tactile sense depth fusion. The method comprises the following steps that firstly, based on visual information, surface geometric features of a target object are extracted, grabbing adaptability scores are calculated, an optimal grabbing point is selected, and a collision-free approaching track is planned; secondly, in a contact establishment stage, detecting a contact event through a touch sensor, fusing vision and touch data to unify a coordinate system, calculating a vision-touch consistency score, and applying an initial grabbing force; and thirdly, in the stable holding stage, slip detection is conducted through wavelet packet energy entropy and pressure gradient, and the grabbing force and impedance parameters are dynamically adjusted in combination with self-adaptive impedance control. According to the method, intelligent control over the whole process from approaching to contact to stable holding is achieved, and the adaptability, stability and safety of robot grabbing in the dynamic environment are improved.

The invention relates to a rigid-flexible trans-mass self-adaptive robot force position and rigidity collaborative optimization method and system, and relates to the technical field of robot control, and the method comprises the steps: firstly, carrying out the linearization processing of a robot dynamic model through a semi-implicit integral method, and obtaining a robot dynamic model; and in combination with an extended Kalman filter, stiffness parameters in the contact operation process are estimated in real time, a dynamic stiffness sensing model is constructed, and the system response capability is effectively improved. Then, a rigidity parameter is embedded into a Hertz contact model, the dependence of a traditional model on prior information of a contact surface is broken through, a dynamic interaction model between the robot and an operation object is established, and high-precision real-time sensing of normal force and friction force is achieved. And finally, under a nonlinear model predictive control framework, multi-dimensional physical constraints are constructed based on the contact force, the position and the contact rigidity, an optimal control model is obtained, control input is dynamically and adaptively updated through rolling optimization, and the stability and the control precision of the robot in rigid-flexible heterogeneous contact operation are improved.

The invention relates to the technical field of service robot navigation and motion control, in particular to a service robot dynamic obstacle pre-judgment obstacle avoidance system, which comprises an environment sensing unit for outputting sensing data; the target state estimation unit outputs a target state sequence; the future evolution prediction unit is used for outputting a target future evolution result; the risk quantification unit outputs the current collision risk degree; operating the strategy management unit, and outputting strategy parameters; the evasion decision and path generation unit is used for outputting candidate evasion tracks and speed curves; the execution and control interface unit is used for outputting a chassis control instruction and implementing trajectory tracking; and the device communication and calculation platform is used for outputting a time sequence synchronization and data channel management result according to operation requirements and providing fault monitoring. According to the method, target state estimation, future evolution prediction, collision risk quantification and strategy linkage are completed within real-time constraints, consistent input and constraints are provided for planning and execution on a unified data channel, and safety and traceability are improved.

The invention relates to the technical field of intelligent mobile robot path planning, in particular to a dynamic sensing path planning method for a mobile robot in an unknown environment. According to the method, an environment complexity model is established based on the obstacle density and the dynamic activity, and map partition and obstacle avoidance judgment are adaptively adjusted according to the environment complexity model; carrying out incremental updating on the near, middle and far sub-regions based on the point cloud similarity; extracting contours of the obstacle point clouds and carrying out topological optimization; dynamic candidate points are identified through double marking of rays and scanning points, and collision time is predicted and estimated in combination with spatial clustering and trajectory; and according to the collision time, hierarchical obstacle avoidance and local or global re-planning are implemented, and a repaired path is smoothed. According to the method, the path planning efficiency and the real-time obstacle avoidance capability of the mobile robot in a dynamic complex environment are remarkably improved, the consumption of computing resources is reduced, and the method is suitable for application scenes needing high dynamic response, such as warehouse logistics and unmanned driving.

An automated system for a space includes: k navigating robots operating within the space to perform a set of orders, where k is an integer greater than or equal to two, where the space includes one or more waiting areas where any navigating robot may wait without blocking movement of another navigating robot; and a control module configured to receive the set of orders and communicate, to the k navigating robots, paths p for the k navigating robots to fulfil the set of orders, with each order in the set of orders including one or more tasks including a sequence of actions to be performed at a location in the space, having an order availability time after which any task of an order may start, and being associated with a set of precedence constraints arising from at least one of a relationship between orders and a resource of an order.

The invention discloses a dynamic digital twinning system based on 3D Gaussian splashing. The dynamic digital twinning system comprises a flexible electric power inspection robot, a 3DGS dynamic enhancement module and a fusion digital twinning background centralized control system. The flexible electric power inspection robot is used for collecting 2D images and environment point cloud data of substation equipment; the 3DGS dynamic enhancement module is used for carrying out 3DGS dynamic enhancement and generating a 3D Gaussian model; the standard digital twinborn centralized control platform is used for storing and calling data and digital twinborn bodies of the traditional static substation digital model; the model fusion port carries out space alignment and attribute interpolation fusion on the 3D Gaussian model and the static digital twin; and the visual terminal is used for displaying the fused enhanced digital twinborn body and the equipment state evaluation result. According to the invention, the dynamic enhancement of the digital twinborn model of the substation equipment can be realized, and the operation state and change condition of the equipment can be reflected more truly and intuitively.

The invention discloses a robot dynamics physical information prediction neural network construction method based on motor and external force coupling, and relates to the technical field of robots. Comprising the following steps: embedding a motor kinetic equation into a DeLaN network, and establishing an explicit dynamic mapping relation between a system motion state and a motor current through mapping between the motor current and a joint torque; introducing an external force item by fitting a Jacobian matrix, and constructing approximate mapping of the Jacobian matrix by adopting a data driving method; and an initial model is constructed, and after motor dynamics and external force modeling are incorporated into the initial model, an inverse dynamics model of the mechanical arm is obtained. Compared with an existing most advanced method, the method has higher modeling precision and generalization ability and better control performance. The innovative scheme provides solid technical support for realizing real-time accurate control in the field of robots.

The invention relates to the technical field of robots, and discloses a robot dynamic inspection control system based on anti-shake performance. The visual detection module is used for collecting image data of an inspection target; the motion compensation and anti-jitter module is used for monitoring the motion state of the robot in real time and compensating jitter generated by the robot; the environment sensing module is used for detecting and acquiring change data of external environment factors in real time; the data fusion and optimization control module is used for fusing data from the visual detection module, the motion compensation and anti-jitter module and the environment perception module; and the intelligent control module is used for fusing and optimizing the control instruction generated by the control module according to the data. By introducing the motion compensation and anti-jitter technology, the motion state of the robot is monitored and optimized in real time, interference caused by vibration is eliminated, the precision and stability of images and sensor data in the dynamic inspection process of the robot can be greatly improved, and the influence of external interference on a measurement result is reduced.

The invention relates to the technical field of intelligent control, in particular to a robot dynamic scheduling system and method based on cross-regional distribution collaboration.The system comprises a task scheduling center, a regional distribution management module, a dynamic path planning engine and a resource collaboration unit, real-time fusion of an order timeliness coefficient, a cargo type weight and a distribution area load balance degree is supported; the regional distribution management module is configured with distributed edge computing nodes; the dynamic path planning engine integrates a space-time constraint analyzer, and dynamically corrects a distribution path in combination with a real-time traffic situation map and a weather influence factor; the resource collaboration unit is deployed at a hub node of a distribution network and is configured with a dynamic task redistribution strategy. Therefore, the problems of multi-dimensional decision splitting, obvious cross-domain cooperative delay, dynamic constraint response lagging, insufficient resource elastic adaptation and the like in the prior art are solved.

The invention provides a teaching-free welding robot dynamic energy-saving path planning method and system, and relates to the technical field of industrial robos.The method comprises the steps that 1, a workpiece is scanned through a laser vision device, weld joint coordinates, obstacle information and workpiece thermal deformation data are obtained, weld joint feature points are extracted, and the weld joint feature points are obtained; establishing a mapping relation between a workpiece coordinate system and a robot base coordinate system; step 2, based on the mapping relationship, in combination with a thermal deformation prediction mechanism and sensor feedback data, implanting a dynamic response node in the initial path, calculating path point offset by using the feedback data, and generating an anti-offset path; and step 3, aiming at the anti-offset path, constructing a multi-dimensional collaborative evaluation mechanism, and carrying out synchronous adjustment and comprehensive balance on the path length, the energy consumption of the whole welding process and the welding quality parameters so as to obtain a multi-dimensional collaborative evaluation result. Weld joint deformation, clamp offset and obstacle displacement are sensed in real time, the path is dynamically planned, and the flexibility of path planning is improved.

The invention belongs to the technical field of robot navigation and path planning, and particularly discloses and provides a dynamic navigation planning method and system for a service robot, which overcomes the limitation of single-dimensional analysis in a complex dynamic scene by constructing a dynamic social thermodynamic diagram and deducing group dynamic intention data. The perception precision of the environment structure and the people stream situation is improved; a linkage mechanism among a dynamic social thermodynamic diagram, individual behavior characteristics and path planning is established, and a social cost function is generated to optimize a navigation path according to the traffic efficiency and social etiquette of group future dynamic vectorization evaluation; the actual influence of the navigation behavior of the robot on the environment is monitored in real time, and a planning strategy is adjusted in combination with feedback data, so that the adaptability of the navigation decision and the continuous improvement of the environment acceptance degree are realized.

According to the mechanical arm control method and system based on the self-adaptive force field, the motion state of a patient is monitored, the motion state is compared with a demonstration space trajectory, and training performance information of the patient is obtained. Based on the training performance information and the constructed adaptive force field model, auxiliary force applied to the patient is obtained through calculation, wherein the auxiliary force comprises normal force, tangential force and viscous force. And the applied auxiliary torque is calculated based on the tail end posture of the mechanical arm and the target posture, the joint control torque is obtained according to the auxiliary force and the auxiliary torque, and the dynamic torque and the null space control torque of the robot dynamic model are obtained. And the joint control torque, the dynamic torque and the null space control torque are combined to obtain the comprehensive control torque for controlling the mechanical arm. Therefore, the self-adaptive adjustment of the auxiliary force is realized based on the position, speed and direction of the patient, and the time degree of freedom can be obtained in the motion of the self-adaptive force field, so that the patient obtains more freedom of active motion under the assistance of the space force field.

The invention discloses a robot dynamic path planning method and system fused with lightweight LLM. The robot dynamic path planning method comprises the following steps of dynamically mapping a grid map, constructing a semantic analysis model, performing global path planning by adopting an improved A * algorithm, and performing local path optimization by adopting an improved DWA algorithm. Specifically, according to the method, the high-frequency capture and real-time updating of the dynamic obstacle are realized by introducing the fusion of the event flow of the event camera and the depth information of the laser radar; dynamic matching of semantic instructions and planning parameters is achieved by introducing lightweight LLM, meanwhile, sight distance penetration rate optimization path connectivity is fused in global planning, local obstacle avoidance adopts a semantic adaptive weight adjustment mechanism, the high-precision perception advantage of multi-sensor fusion is guaranteed, task adaptability and scene pertinence are improved, and the method is suitable for large-scale popularization and application. And the complex requirements of logistics scenes are met.

The invention provides a configuration optimization method before capturing of a space double-arm robot, and the method comprises the steps: constructing a kinematics and dynamics model of the space double-arm robot according to a three-dimensional model of the space double-arm robot; constructing a kinetic model of the to-be-captured target by using the motion parameters of the to-be-captured target in the space; according to the dynamic model of the space double-arm robot, the relative mass of the space double-arm robot under non-centering collision is obtained, and a maximum pre-contact collision force performance evaluation index is constructed; further constructing a double-arm closed chain inertia matching index performance evaluation index; constructing a configuration optimization model before the space double-arm robot captures the target; and through a multi-target optimization algorithm, the optimal configuration of the space double-arm robot before capturing the target is solved. The pre-contact collision force peak value of the double-arm robot during target capturing can be effectively reduced, and meanwhile the operability of a closed chain system formed after capturing is guaranteed.

The invention discloses a robot hierarchical path planning method and device in a complex environment, a medium and a product, and the method comprises the steps: responding to a path planning request of a robot in an airport terminal, dividing the airport terminal into a plurality of dynamic Voronoi regions according to an underlying environment grid map and people flow density information, a minimum path cost algorithm with people flow density weighting is adopted to construct a global path anchor point sequence, the global path anchor point sequence is further split into a plurality of local path planning requests, a double-heuristic cost function is constructed, an improved algorithm is adopted to generate local sub-paths, and according to surrounding environment information collected by a robot in real time, the local sub-paths are calculated. And optimizing each local sub-path by adopting a reinforcement learning algorithm, generating motion control instructions for the robot at different control time points, and realizing dynamic obstacle avoidance of the robot. The path planning efficiency and the motion safety of the mobile robot in a super-large-scale dynamic environment are remarkably improved, and the technical problems that a traditional method is high in calculation complexity and poor in real-time performance are effectively solved.

The invention discloses a wheel-foot humanoid robot operation control method and system based on maneuverability optimization, and relates to the technical field of robot intelligent control. According to a mechanical arm tail end trajectory tracking task and mechanical arm joint motion constraints, a weight matrix is calculated in a self-adaptive mode through state vectors of a base and a mechanical arm joint; a robot dynamic model is constructed, and the tail end contact force of the mechanical arm is estimated based on a disturbance observer; the direction of the control force is determined according to an estimation result, and then force maneuverability enhancement is carried out; a speed instruction of an end effector is obtained, the expected base speed and the expected mechanical arm joint speed are obtained through a weight matrix, a hierarchical control frame of a model prediction controller and a whole-body controller is constructed based on an integrated dynamic model, the optimal wheel-leg interaction force spinor and the optimal joint torque are obtained through solving, and the optimal wheel-leg interaction force spinor and the optimal joint torque are obtained. Therefore, the whole-body motion control of the robot is driven. Through organic combination of disturbance compensation and force maneuverability optimization, the motion stability and operation efficiency of the robot are enhanced.

The invention discloses a robot aided navigation device and method based on artificial intelligence, and relates to the technical field of aided navigation, and the method comprises the following steps: collecting navigation environment data in real time through a multi-mode sensing equipment group; establishing a preliminary environment three-dimensional map through a simultaneous positioning and mapping technology; reversely deducing an unknown area by using the preliminary environment three-dimensional map, deducing based on physical and geometric constraints of the environment, and restoring and perfecting the environment three-dimensional map through a reverse simulation model; compensating and optimizing the environment three-dimensional map by using deep learning, and continuously updating and maintaining the environment three-dimensional map by fusing the dynamic data of the robot; an optimal navigation path is searched through a global path planning algorithm, the optimal path is adjusted in real time to respond to dynamic environment changes, and the model is used for predicting future navigation environment changes. According to the method, environment data are collected in real time through the multi-mode sensing equipment, an unknown area is reversely deduced by utilizing a preliminary three-dimensional map, optimization and updating are carried out, and dynamic navigation path planning is realized.

The invention relates to a dynamic characteristic modeling method and system for an alternating impact disassembling operation robot, and relates to the technical field of robot control, and the method comprises the steps: firstly, comprehensively considering the dynamic characteristics of a robot joint side and a motor side, building a torque-driven flexible joint dynamic model, and determining the influence of joint flexibility on impact response; secondly, estimating normal impact force and tangential friction force by adopting an exponential inelastic continuous contact model, constructing an alternating impact dynamic model, and quantifying momentum change and a nonlinear dynamic effect in the impact process; and finally, a multi-space dynamic characteristic coupling model is established, the interaction mechanism of the joint space, the impact contact space and the task space is systematically analyzed, the accurate speed after impact is output, an impact force control strategy is generated, the dynamic response precision of the robot under the alternating impact operation is improved, the active utilization of the robot on the impact effect is achieved, and the reliability of the robot is improved. And the stability, the accuracy and the high efficiency of the disassembling operation are ensured.

The invention discloses a power transmission line maintenance robot dynamic path planning method, and relates to the technical field of robot maintenance, and the method comprises the steps: obtaining a three-dimensional topology model of a power transmission line, and constructing a path diagram; performing initial path planning based on a comprehensive scoring algorithm; surrounding environment data are continuously collected, dynamic obstacle recognition and prediction are carried out, a path is re-planned online, and an optimized path is regenerated; an energy model is introduced, and path-energy consumption joint decision making is carried out; and an abnormal feedback and path rollback mechanism is introduced. The method can achieve efficient planning and dynamic adjustment of the path, has high environmental adaptability and task safety, is suitable for intelligent maintenance operation of the power transmission line under the complex terrain, makes a decision by introducing an energy consumption model to cooperate with path comprehensive scoring, effectively prevents the robot from neglecting energy efficiency in the path planning process, and improves the maintenance efficiency of the power transmission line. The method guarantees the continuity and safety of task execution, and is especially suitable for long-distance, high-fall and energy consumption constraint scenes.

The invention discloses a spiking neural network driven robot dynamic adaptive control system, which relates to the technical field of robot control, and comprises seven modules: an environment sensing module which integrates various sensors and collects and transmits environment, attitude and interaction information; the signal preprocessing module processes data through composite filtering and feature extraction; the spiking neural network modeling module constructs a three-layer structure and performs training based on a fusion learning rule; the dynamic decision output module converts the pulse signal into a control instruction and adjusts gain; the actuating mechanism driving module drives the actuator to act; the state feedback monitoring module monitors and feeds back motion parameters and system states; and the adaptive optimization module optimizes the network and module parameters based on feedback data, and dynamically matches the environment. The control precision and the response speed of the robot in a complex environment are improved, the adaptive capacity is enhanced, the operation reliability and safety are guaranteed through multi-module cooperation, and the application scene is expanded.

The invention discloses a reconfigurable robot control method and system for a zero-sum game optimization event triggering mechanism. The method comprises the steps that a reconfigurable robot dynamic model based on joint torque feedback is established, an error dynamic state space is established, and a preset performance function is set to restrain the transient performance of the error dynamic state space; the error dynamics state space constraining the transient performance is mapped to the unconstrained error dynamics augmented system state space, and an event triggering mechanism is introduced, so that the unconstrained error dynamics augmented system state space meets the zero-sum game of maximizing the event triggering interval and minimizing the control input; setting a performance index function based on a zero-sum game, and obtaining an optimal control law under an event triggering mechanism through the performance index function; according to the invention, the precision and stability of robot system control are improved through the predetermined performance function; the zero-sum game of an event triggering mechanism is introduced to obtain an optimal control law under a Nash equilibrium condition, so that communication resources and calculation burdens are effectively reduced; and the control torque of each subsystem is smoother under the action of the optimal control law, so that the energy consumption is reduced and the service life of the motor is prolonged.

The invention discloses a non-flat terrain autonomous navigation method based on path pruning and trajectory smoothing. The method comprises the following steps: constructing an elevation map with three-dimensional map information as a navigation map according to robot navigation space information; based on the navigation map, performing global path search by adopting a path pruning strategy to obtain an initial path from the starting point to the target point; based on the initial path, track optimization is carried out by using a B spline curve, and a smooth track meeting robot dynamics constraints is generated; in response to an obstacle which is detected on the smooth trajectory and cannot be avoided, performing three-dimensional obstacle crossing trajectory planning based on terrain elevation information of the elevation map, and generating an obstacle crossing trajectory capable of crossing the obstacle; wherein the robot performs autonomous navigation based on the smooth track or the obstacle crossing track. According to the invention, efficient global search, smooth trajectory optimization and intelligent obstacle crossing decision of the robot in unknown and non-flat terrains can be realized.

The invention belongs to the technical field of path planning, and particularly relates to an A * hybrid ant colony path planning method for simulating a flame spreading grid. Comprising the following steps: interactively setting a plurality of fire source positions in an obstacle environment of an initial static grid simulation map, and selecting a starting point; obtaining terrain analysis data and fuel model data of the map; calculating the spreading rate value of each basic grid, and constructing a conversion rule among a non-combustion state, a combustion state and a burnout state to simulate flame spreading; the map information is input into a function formed by nesting a simplified ant colony algorithm into a traditional A * algorithm, and the optimal path for the robot to go to a plurality of target points is planned; simulating the movement of the robot to dynamically present the optimal global path; the flame spreading model and the robot dynamic path planning model are input into a map function together, optimal path planning of the simulation robot in the dynamically changing flame spreading environment is achieved, and the environment and robot moving conditions are displayed in a basic grid map in real time. According to the invention, the optimal path planning for the robot to go to a plurality of target fire sources to implement the fire extinguishing activity under the obstacle and dynamic flame environment can be realized.

The invention discloses a robot dynamic control method and system combined with multi-modal emotional interaction, and relates to the technical field of robot control and artificial intelligence. The method comprises the steps of collecting multi-modal data and performing entity extraction to obtain a first interaction entity set and a second interaction entity set; acquiring a real-time emotion category through a current emotion recognition component; performing trend analysis based on the historical entity data to obtain a predicted emotion category; respectively inputting the real-time emotion category and the predicted emotion category into a pre-constructed control parameter mapping model, querying to obtain a first model mapping parameter group and a second model mapping parameter group, and calculating a corresponding dynamic control parameter set; and finally, fusing the two parameter sets, generating a smooth control sequence by using a Bezier curve, and driving the robot to carry out anthropomorphic and adaptive dynamic response. According to the method, the problems of emotion perception lagging and abrupt control in the prior art are solved, accurate emotion recognition, trend prediction and smooth behavior control are realized, and the naturalness and intelligence of man-machine interaction are improved.

The invention discloses a robot dynamic voice interaction method based on multi-modal state management, and belongs to the technical field of intelligent robots, and the method comprises the steps: S1, collecting multi-modal instruction data inputted by a user; s2, carrying out data processing on the multi-mode instruction data, and then generating an input text packet with an environment context mark; s3, performing intention analysis on the input text packet, and binding the input text packet with a historical context to generate an intention vector; s4, updating the environment matrix in real time through the intention vector; s5, dynamically calculating the task weight W through the updated environment matrix, and dynamically adjusting the priority of each task; and S6, calling a corresponding function module according to the priority of each task through a plug-in interface, and executing the corresponding task, so as to achieve the purposes of realizing cross-scene context continuity, multi-task priority dynamic scheduling, supporting hot plug function extension and having active abnormal interaction capability.

The invention discloses an automatic calculation and compensation method and system for dynamic strike delay of a laser weeding robot, and belongs to the technical field of agricultural intelligent equipment.The method is achieved through the following steps that in the static calibration stage, a laser galvanometer, a camera and collaborative parameters of the laser galvanometer and the camera of a robot system are corrected; laser scanning nonlinear errors, camera imaging distortion errors and pose and mapping errors between a galvanometer coordinate system and a camera coordinate system are eliminated; in the dynamic testing stage, the robot advances at a constant speed, laser striking is conducted on the wood chips with preset marks, and the position deviation between an actual striking point and a theoretical target point is obtained through the image recognition technology; reversely deducing system delay time based on the deviation and the speed of the robot, and iteratively updating compensation parameters; finally, after the iterative optimization strike deviation is smaller than the set precision, the compensation parameters are solidified to a control system; the method is suitable for a laser weeding robot and other dynamic visual striking systems.