1795 results about "Intelligent robots" patented technology

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

The invention relates to the technical field of body-equipped intelligent robots, in particular to a multi-mode body-equipped intelligent robot control method and device, and the method comprises the steps: synchronously collecting visual, auditory, tactile, force sense and body perception information, and unifying the information to the same time-space reference through a cross-mode time-space stamp alignment mechanism; hierarchical feature extraction and fusion are carried out on the multi-modal information, and unified multi-modal scene state representation is generated; reasoning a decision based on the representation by using a body agent framework, and outputting a control instruction; motion planning and control, visual servo tracking in a non-contact stage and dynamic parameter correction in a contact stage are executed according to instructions; optimizing the multi-modal strategy network through an incremental strategy distillation mechanism based on the interactive data flow; the problem of space-time asynchronization of multi-modal sensing information is solved through a cross-modal space-time stamp alignment mechanism.

The invention relates to the technical field of voice interaction, particularly discloses an AI intelligent robot voice interaction method and system, and aims to solve the problems of low voice interaction accuracy, insufficient reliability and lack of authority control in a complex noise environment. A dynamic noise feature library containing steady-state noise, impact noise and human voice interference features and a pre-stored gesture instruction library are constructed, audio signals are collected in real time, low-frequency-band, middle-frequency-band and high-frequency-band differential noise reduction is executed, Mel-frequency cepstral coefficient features are extracted, noise scenes are matched, corresponding voice recognition models are switched, and voice recognition is achieved. And calculating a confidence value of the voice instruction, outputting multi-modal verification data in combination with a dynamic confidence threshold, and outputting an authority control signal through voiceprint matching, authority verification and instruction consistency judgment. Through multi-modal fusion, dynamic adaptation and authority control, the voice recognition accuracy and interaction safety in a complex noise environment are remarkably improved, and the method is suitable for scenes such as factory intelligent inspection.

The invention discloses a control method and equipment for an intelligent robot with a body and a storage medium, and belongs to the technical field of robots. The method comprises the steps of receiving a task instruction and environment perception data, performing fusion processing on the task instruction and the environment perception data, generating task semantic information associated with the task instruction, analyzing the task semantic information through an implicit planner, generating an implicit action mark, and based on the implicit action mark, generating an implicit action. And decomposing a to-be-executed task corresponding to the task instruction into a plurality of task sub-targets layer by layer, generating an action instruction sequence based on the task sub-targets, and executing a control action corresponding to the action instruction sequence. According to the method, based on the constraint of the implicit action mark and the action instruction sequence, the robot with the body can flexibly respond to environment parameter changes such as object position deviation or new task requirements in the task execution process, the coupling degree of the action instruction of the robot with the body and the predefined scene is reduced, and high stability of task execution is ensured.

The invention relates to the technical field of intelligent robots and old-age care, and discloses a target navigation method and device based on environmental context, a robot and a medium, which can be applied to a household auxiliary robot intelligent medicine delivery scene, and the method comprises the following steps: receiving multi-modal input of a user, preprocessing the multi-modal input, and generating preprocessed multi-modal data; reading the current time and the environment state, and performing perceptual analysis based on a pre-constructed tetrad map, the current time and the environment state to generate a candidate search area list; determining a target candidate area from the candidate search area list; generating a target navigation path based on the SLAM map and the target candidate area, and moving the robot to the target candidate area according to the target navigation path; and obtaining a target candidate region image, and performing visual semantic verification based on the target candidate region image and the target object label in the structured text instruction. According to the invention, the robot navigation accuracy and the user experience are improved.

The invention relates to the technical field of body-equipped intelligent robots and intelligent equipment, in particular to a self-adaptive flexible tightening body-equipped intelligent robot which comprises a body-equipped moving platform, the top of the body-equipped moving platform is provided with a body-equipped lifting mechanism, and the top of the body-equipped moving platform is provided with a body-equipped articulated arm module. And a tightening actuator and a clamping jaw actuator are arranged at the tail end of the outer part of the body articulated arm module. According to the self-adaptive flexible tightening intelligent robot and the tightening process method, sensing and self-adaptive adjustment of external environment changes are achieved through multi-module integration, and the tightening process method based on a multi-mode sensing system and an intelligent control system is constructed; vision, force sense and touch sense data are fused, real-time monitoring and dynamic adjustment of the tightening process are achieved through a machine learning algorithm, and the intelligent level and quality stability of tightening operation are effectively improved.

The invention discloses an artificial intelligence robot path planning method and system, and the method comprises the steps: 1, carrying out the comprehensive perception of an environment geometric structure, object semantics and dynamic obstacles, eliminating scene differences, and further constructing the topological graph representation of an environment; 2, pre-training a path planning model, designing a scene context encoder, encoding a scene specific rule into a low-dimensional vector, and outputting a scene context code and updated planning model parameters to provide a planning capability adapted to a new scene for the step 3; 3, dynamically adjusting a track according to real-time sensor data by adopting a hierarchical decision-making architecture; step 2, recording success / failure path segments in the new scene, regularly updating a local planning module, regularly feeding back empirical data in the new scene to the step 2, and triggering a conservative obstacle avoidance mode when the confidence coefficient is lower than a threshold value; and 4, constructing a quantitative evaluation system, building an automatic test platform, simulating diversified scenes and dynamic interference, and automatically generating a test case.

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

The invention discloses a robot interactive question-answering method and system based on a large service model, and belongs to the technical field of intelligent robots, and the method comprises the steps: carrying out the feature extraction and fusion of the original input of a user, obtaining a multi-modal vector, building a recognition model, outputting an emotion recognition tag, an intention recognition tag, a user state vector and a session history, fusing the user state vector with a domain knowledge graph, outputting a structure reasoning vector and a joint condition vector, and splicing a knowledge abstract vector with a condition fusion vector to obtain the user state vector as the input of a text generator; and outputting a natural language answer and a user state vector, mapping the natural language answer into a multi-modal behavior action, and finally outputting a voice answer signal and the multi-modal behavior action. According to the method, the answer strategy of the robot is dynamically adjusted according to the emotional state of the user by combining context awareness and knowledge reasoning technologies, so that intelligent interaction of emotional resonance and professional depth coexistence is realized.

The invention belongs to the technical field of intelligent control of robots, and discloses a self-adaptive adjustment system for behavior modes of an intelligent robot with a body, which comprises a neuromorphic sensing module for acquiring and fusing multi-mode environment sensing information, performing space-time compression on the multi-mode environment sensing information by referring to a brain visual cortex sparse coding principle, and generating a neural network; generating an environment state representation vector, and constructing a neuromorphic perception path; the cognitive constraint decision module is used for generating an action strategy by adopting a neural symbol hybrid architecture based on the environment state representation vector; through a cognitive momentum optimization mechanism, a strategy inertia item is introduced to improve an updating process of an action strategy parameter, and a behavior decision vector is obtained; the variable impedance execution module is used for mapping the behavior decision vector into a plurality of virtual muscle cooperation elements through a muscle cooperation mapping rule, and solving an expected movement track of each execution joint of the robot; and the intelligent robot with the body has higher adaptability, stability and execution capability.

The invention relates to an intelligent robot feeding and discharging method and system, and the method comprises the steps: obtaining the image information of a target material, carrying out the recognition and analysis of the image information of the target material, and obtaining an analysis result which comprises the position information and posture information of the target material; inputting the analysis result into a pre-trained operation decision model for analysis, and generating an action planning instruction corresponding to the target material; task scheduling operation is executed based on the current task state and the resource information, a task scheduling result is obtained, and the scheduling operation comprises task distribution optimization and path generation control; according to the task scheduling result and the action planning instruction, the target robot is scheduled to complete feeding and discharging operation; and in the loading and unloading execution process, the operation state is monitored in real time, the operation state is detected and analyzed, an interference detection result is obtained, and the action planning instruction is dynamically adjusted based on the interference detection result. The production line has the effect of improving the efficiency of the production line.

The invention relates to the technical field of multi-modal posture recognition, and relates to a multi-modal posture recognition system based on Transform fusion, which fully excavates the complementary advantages of visual information in spatial detail representation and inertial information in time sequence dynamic capture through space-time alignment of multi-modal data and deep fusion based on an attention mechanism. The accuracy and the stability of attitude estimation under the conditions of visual occlusion, rapid movement and complex illumination are obviously improved; posture optimization is carried out by introducing physical constraints such as bone length constancy and joint movement range limitation, and time domain smoothing and contact state correction are applied, so that the precision of the generated three-dimensional posture sequence is ensured, a solid technical foundation is laid for improving the naturalness, safety and intelligent level of man-machine interaction, and the method is suitable for popularization and application. And meanwhile, reliable application and deep development of the intelligent robot in key fields of service robots, virtual reality, remote cooperation and the like are powerfully promoted.

The invention discloses a robot target identification method and system based on multi-source data driving, and relates to the technical field of intelligent robot environment awareness, and the method comprises the steps: inputting a dynamic environment vector into an LSTM network, and outputting the real-time fusion weight of vision, sonar and laser radar through an activation function; performing weighted fusion on vision, sonar and laser radar based on real-time fusion weights, constructing a causal graph neural network, separating false correlation features through anti-fact intervention samples, and outputting causal feature vectors; and inputting the causal feature vector into a pre-trained lightweight target recognition model for target classification, and when a new scene is detected, triggering knowledge base retrieval and comparative distillation to generate a recognition confidence coefficient. According to the method, the causality and correlation in the multi-modal sensor data are effectively distinguished by constructing the causality graph neural network and introducing an anti-fact intervention mechanism, so that the recognition accuracy and generalization ability of the model in a complex dynamic environment are improved.

The invention relates to an emotion accompanying robot system based on multi-dimensional perception and an interaction method, and belongs to the technical field of intelligent robots. The system comprises a sensing module which captures micro-expression, voice, body temperature, touch and other multi-source data through a biological radar array, multispectral imaging and a touch sensing fabric; the decision-making module quantifies the emotion intensity by using an emotion state calculation engine, constructs a user personalized emotion file in combination with the dynamic knowledge graph, and generates a dynamic emotion graph; and the execution module realizes anthropomorphic emotion expression through bionic face driving, joint compliance control and thermal feedback. The interaction method comprises the steps of data capture, emotion file construction, dynamic graph generation, interactive execution and feedback adjustment, definition of an emotion intensity quantification formula, a graph edge weight model and the like. According to the method, physiological signals, environment situations and bionic expression are fused, multi-modal precise emotion perception and safe interaction are achieved, man-machine naturalness is improved, the method is suitable for elderly accompanying, psychological counseling and other scenes, and the emotion accompanying effect is enhanced.

The invention belongs to the technical field of intelligent robot navigation and positioning, and discloses an intelligent robot autonomous mapping method in a GNSS rejection environment, which comprises the following steps of: monitoring a GNSS signal state in real time, and switching to pure laser SLAM mapping when detecting that the number of available satellites, PDOP or pseudo-range residual exceeds a threshold; the method comprises the following steps: acquiring three-dimensional point cloud data, filtering and denoising, obtaining a filtered and denoised current frame point cloud, performing laser radar mapping and positioning registration, selecting a key frame point cloud, constructing lightweight neural implicit scene representation, and training an MLP network; loopback detection is carried out, a sparse factor graph is constructed, key frame poses are corrected, then the key frame poses are applied to original three-dimensional point cloud data, and a two-dimensional grid map is generated. According to the invention, under the condition that the GNSS signal is blocked or fails, the environment map with high precision and low drift is continuously generated, and the real-time performance and robustness of autonomous mapping are remarkably improved.

The invention discloses a safety-enhanced trajectory planning method based on an Astar algorithm, and belongs to the technical field of intelligent robot navigation and path planning, and the method comprises the following steps: 1, constructing a grid map, and marking an obstacle region; 2, establishing a security evaluation mechanism, evaluating the surrounding environment of the path node, and quantifying a security index; 3, in a node expansion process, combining a heuristic item and a security index in a cost function to realize priority search for a security region during path selection; 4, key nodes of the obtained initial safety path are extracted based on a Douglas-Peucker algorithm to generate a rectangular safety corridor, and smoothing processing of the initial path is achieved based on a segmented Bezier curve. On the basis of guaranteeing path optimality, safety is used as an auxiliary guiding basis in the path searching process, and the path searching efficiency is improved. Therefore, consideration of the path length and the environmental risk is realized, and the navigation path with higher execution safety is generated.

The invention discloses an intelligent robot with a body and a robot motion control system and method.The intelligent robot with the body is provided with an execution component, a moving module and the robot motion control system, and the robot motion control system comprises a multi-modal input encoder, a multi-modal output encoder and a multi-modal output encoder, the multi-modal feature fusion module is used for collecting and processing multi-modal data to obtain multi-modal features and fusing the multi-modal features to obtain a multi-modal feature token sequence; the action expert network is used for mapping the fused multi-modal feature token sequence into an abstract robot action sequence block; and the execution controller is used for converting the abstract robot action sequence block into a control instruction which can be executed by bottom hardware. According to the method, direct mapping from environment perception and semantic understanding to movement control and execution of specified task actions can be realized, so that the task understanding, response speed and execution completion degree of the robot in a complex environment are improved.

The invention relates to the technical field of robot navigation, and discloses an automatic path-finding intelligent robot and a path-finding method thereof. According to the method, multi-dimensional spatial data including obstacle distribution, ground features and dynamic target motion information is acquired through an environment sensing device, spatial discretization processing is performed on the data, and a topological map which has a hierarchical structure and includes a node connection relation and a region passing weight is generated. Then, according to the node connection relation of the topological map, a path planning model based on manifold learning is established, and an optimal path candidate set is determined by calculating the manifold distance between adjacent nodes; then extracting a key node sequence in the candidate set, dynamically optimizing a path in combination with a region passing weight, and generating a preliminary navigation track; and finally, inputting the initial track into a motion control model, carrying out smooth processing according to kinematics constraint of the robot, and outputting a final execution path. According to the invention, the robot can efficiently find the way in a complex environment.

The invention discloses a power equipment inspection path planning method and device based on laser and vision, and relates to the technical field of intelligent robots and power inspection, and the method comprises the steps: constructing a multi-sensor system, carrying out the external parameter calibration and time synchronization of each sensor, and carrying out the environment modeling and positioning through employing an improved LIO-SAM algorithm; performing power equipment identification; generating an optimal inspection path based on the calculated coordinate set of the power equipment; according to the optimal inspection path, dynamic obstacles are detected in combination with semantic and geometric information, obstacle avoidance is achieved through local track adjustment and path re-planning, and a planned path is obtained; and driving the inspection robot to autonomously move according to the planned path, acquiring an image and infrared data at the target power equipment, and feeding back a result to the background system to complete closed-loop inspection. According to the invention, the autonomous inspection of the whole process of environment modeling, target equipment identification, path generation, dynamic obstacle avoidance and the like can be effectively realized.

The invention discloses a force sense feedback control method for an intelligent mechanical arm, which comprises the following steps of: 1, acquiring data through a multi-modal sensor and fusing the data to obtain a multi-dimensional perception vector; 3, calculating a force sense tracking error and a change rate and triggering an event-driven control decision mechanism; 4, designing a nonlinear compensation control rule and outputting a control torque instruction, wherein a control system comprises a multi-mode sensing module, a dynamic prediction module, an event-driven control module and a cooperative calculation module; according to the method, multi-mode sensing information is fused with the lifting force sense representation capacity, advanced adjustment is achieved in combination with a dynamic force sense prediction mechanism, event-driven control is used for reducing calculation redundancy, robustness to complex interference is enhanced through a nonlinear compensation strategy, and finally high-precision and low-delay force sense control of the mechanical arm in a dynamic interaction scene is achieved.

The invention provides a cleaning robot multi-mode motion control system, method and equipment and a medium, and belongs to the technical field of intelligent robot control. The system comprises a multi-modal data acquisition module used for performing multi-source synchronous acquisition on an environment image, distance information, a voice signal and a collision signal and outputting original sensing data; the data fusion and processing module is used for receiving the original sensing data, sequentially executing space-time alignment correction, multi-source feature fusion and instruction semantic analysis, and outputting an environment sensing result and a structured control instruction; the motion planning and decision-making module is used for executing global path planning, dynamic obstacle trajectory prediction and motion parameter decision-making based on the environment perception result and the structured control instruction, and outputting a motion control instruction containing a motion mode, a speed and an angle; and the motion control execution module is used for receiving the motion control instruction, converting the motion control instruction into an execution mechanism driving signal, realizing closed-loop control through real-time state feedback and outputting a motion execution result.

The invention discloses a control method, system and equipment of an intelligent throat surgical robot and a medium. According to the scheme, a three-dimensional tissue model is constructed according to visual image data, laser imaging data and tomography image data; determining lesion data according to the historical lesion data and the tomography image data, superposing the lesion data and the three-dimensional tissue model, and determining a target operation model; determining prediction trajectory data according to the tomography image data and the physiological motion data; planning a path according to the predicted trajectory data, the target operation model and the constraint condition, and controlling the operation robot to perform an operation according to the planned path; through multi-modal data fusion, a three-dimensional tissue model is established to improve the surgical accuracy; the data are analyzed to determine the lesion direction, the motion trail of the organ tissue is predicted, the three-dimensional tissue model is integrated for path planning, the interference of physiological activities of the organ tissue on the operation is avoided, and the safety of the operation is improved. The embodiment of the invention can be widely applied to the field of intelligent robots.

The invention relates to an intelligent robot active cooperation assembling method based on multi-mode large model cooperation driving. The method is used for improving the perceptual understanding, autonomous reasoning and action execution capabilities of the robot in a dynamic complex industrial environment, so that a more efficient, safer and more flexible man-machine autonomous cooperation assembly mode is realized. According to the method, based on brain-like structure design, a multi-center system including a sensing center, a reasoning center and a motor center is constructed, multi-modal large model sensing understanding and semantic reasoning capabilities are fused, and multi-modal large model sensing understanding and semantic reasoning capabilities are obtained through information collaboration and autonomous decision-making among the centers. The robot is driven to realize active perception, semantic understanding, task reasoning and autonomous action execution in an assembly scene, and the adaptive cooperation capability of the robot in a dynamic environment is improved. The method provided by the invention is finally applied to a case of assembling the engine through man-machine cooperation, and the effectiveness of the method is verified.

The invention relates to the technical field of spraying control, in particular to a control system of an intelligent robot for spraying automobile clothing and a control method and application of the control system, the control method and the application are applied to the intelligent robot, and the intelligent robot comprises a robot body, a scanning assembly and a spraying assembly; the control system comprises a scanning analysis unit which is used for controlling the scanning assembly to carry out omnibearing scanning and analysis on a target vehicle to obtain vehicle three-dimensional shape parameters, vehicle model information and damaged area characteristics; and the spraying planning unit is used for calling a preset vehicle type spraying template according to the vehicle three-dimensional shape parameters and the vehicle type information to determine an initial spraying path. The deviation in the spraying process is monitored in real time and optimized and adjusted, the situation of uneven thickness or incomplete coverage generated in the spraying process can be effectively avoided, and it is ensured that the spraying quality reaches the expected standard.

The invention relates to the technical field of artificial intelligence, in particular to an information interaction method and system for an intelligent robot with a body, and the method comprises the steps: achieving the precise time alignment of multi-source sensor data through a hardware clock synchronization protocol, carrying out the cross-modal feature fusion through a self-adaptive weight distribution mechanism, and constructing a unified environment state expression; analyzing the user instruction in combination with the language model and the dynamic knowledge graph, and generating an execution intention containing environmental adaptation parameters; and generating an action strategy considering a task target and environmental adaptability based on a multi-target optimization algorithm, and converting the action strategy into a control instruction through a hierarchical execution mechanism. The problems that in the prior art, multi-source sensing data synchronization precision is insufficient, and heterogeneous data fusion efficiency is low are effectively solved, and the interaction accuracy, the response speed and the self-adaptive capacity of the robot in the complex dynamic environment are remarkably improved.

The invention relates to the technical field of intelligent robots, and provides a cerebellum model and cross-modal attention fused control device, which comprises a three-layer cooperative control framework: a high-level strategy network for receiving global state data, outputting a macroscopic action element instruction and guiding the overall direction of task completion; the middle-layer cerebellar network is used for fusing multi-modal ontology sensing data in real time and outputting compensation torque; and the bottom layer actuator is used for controlling and executing joint torque output through a feed-forward PID. According to the invention, high-level action errors can be corrected online, and the dynamic anti-interference capability is remarkably improved; delay of torque compensation is reduced, and disturbances such as ground slipping and load sudden change are effectively handled; the strategy drift problem in traditional end-to-end training is solved.

The invention discloses a mobile robot path planning method in a strongly restricted environment based on improved RRT-STAR, and belongs to the technical field of intelligent robot navigation and path planning, and the method mainly comprises the steps: 1, initializing, and constructing a spatial index structure; 2, realizing efficient path planning through a density perception adjustment mechanism and a hybrid sampling strategy; and step 3, optimizing the path generated in the step 2. Local obstacle density and environmental risk are evaluated in real time through a density sensing and risk degree field weighted sampling mechanism, sampling bias probability and extended step length are adaptively adjusted, dynamic weighted fusion is performed among three sampling modes of target priority, local adaptive and global uniformity, and the balance of global exploration and local optimization is realized; adjustable curvature continuity and jerk amplitude limiting soft constraint are introduced, local optimization is carried out by means of Clothoid segmented interpolation, and executable track improving path smoothness and overall quality meeting the dynamic requirements of Ccontinuity, speed, minimum turning radius and the like are generated.

The invention relates to the technical field of bionic humanoid robots, in particular to a multi-expression driving bionic intelligent robot head which comprises an eyebrow movable layer, a cheekbone movable layer, an eye movable layer, an upper lip movable layer and a chin movable layer located below the upper lip movable layer, and the eyebrow movable layer, the cheekbone movable layer, the eye movable layer and the upper lip movable layer are spliced and assembled by hexagonal threaded copper columns through reserved hole positions. The robot head is composed of a shell, an aluminum alloy machining plate frame, a CNC metal part, a steering engine, a rail and a pull wire, and a camera module and a voice control module are arranged in the robot head. The shell can be customized according to different figure facial features, the shell is of a high-precision, compact and adjustable structure, 26 steering engines are integrated in a limited space to control opening and closing of the face including left and right eyeballs, upper and lower eyelids, canthus, eyebrows, cheeks, mouth corners and mouth and actions of lips, and the layered design and control mode of the head of the robot enable the robot to be more intelligent. And the mounting, the dismounting and the maintenance are more convenient.

The invention relates to the technical field of food and medicine product production safety risk management and control, in particular to a food and medicine industry special operation safety risk management and control intelligent robot system and method. The safety production knowledge graph comprises corresponding relations among personnel, equipment, materials, laws and regulations, environments and management systems; analyzing and judging the multi-dimensional data, and calculating a risk value of the multi-dimensional data according to the safety production knowledge graph; judging the risk level of the special operation based on the risk value; and different approval and control programs are configured according to the risk levels. According to the technical thought of the method, traditional manual and subjective approval is changed into intelligent, objective and quantitative dynamic risk assessment, the safety and approval efficiency of special operation are remarkably improved through full-process automatic closed loop from pre-event prevention, in-event monitoring to post-event tracing, and active and predictive safety risk management and control are achieved.

The invention provides a robot trajectory tracking and obstacle avoidance control method, system and device and a medium, and belongs to the field of intelligent robot control, and the method comprises the steps: obtaining motion state information, obstacle information and a reference trajectory; calculating a look-ahead distance based on preset prediction time, a safety coefficient and a current maximum speed in the motion state information; selecting a locally planned target point from the reference trajectory according to the look-ahead distance; based on a dynamic window algorithm, generating a plurality of candidate tracks according to the motion state information and the obstacle information; based on the multi-target cost function and the target point, evaluating each candidate track to obtain a comprehensive cost value; and determining the candidate trajectory with the minimum comprehensive cost value as an optimal trajectory, obtaining an expected linear velocity and an expected angular velocity based on the optimal trajectory, and generating a control instruction to drive the robot to move along the optimal trajectory. According to the invention, the motion stability, path continuity and overall robustness of the robot in a continuous multi-section inspection task are improved.