1306 results about "Intelligent robots" patented technology

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 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 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 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 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.

The invention provides a robot control method based on a visual language action model and related equipment thereof, and relates to the technical field of intelligent robots, and the method comprises the steps: obtaining visual observation information, a target language instruction and robot body state information of a target robot; reasoning the visual observation information, the target language instruction and the robot body state information through a pre-trained visual language action model to obtain a predicted low-dimensional potential vector; reconstructing the predicted low-dimensional potential vector into a robot action instruction through a pre-trained high-dimensional action reconstruction module; wherein the dimension of the robot action instruction is matched with the action space dimension of the target robot; and the target robot is driven to execute the high-dimensional robot action instruction. According to the method, the limitation of the existing visual language action model on the action output dimension can be solved, so that the high-degree-of-freedom robot is effectively controlled.

The invention relates to the technical field of intelligent robot health monitoring, and discloses a health monitoring management system based on an intelligent robot. The system comprises a data cleaning unit, an attribute extraction unit, a period division unit, a coordinate conversion unit and a mode analysis unit. The data cleaning unit collects multi-source health data of the robot, and generates an integrated health data set through format standardization, data mode unification, structural difference analysis and redundancy elimination according to a time sequence. The attribute extraction unit separates a monitoring timestamp, a health index code and an individual type label; a period division unit divides the integrated data set into time slices according to timestamps to form a period health data set; the coordinate conversion unit maps the health state to a standard health coordinate system by using the periodic data set and the health index code to generate a health distribution map; and the mode analysis unit classifies health change events according to the atlas, the periodic data set and the individual type labels, calculates a target periodic health activity degree and outputs a health change trend index.

The invention discloses an intelligent robot navigation system with a body based on a multi-modal large model, and the system comprises an RGB-D camera module which captures a color image and a depth image of an environment in real time, and carries out the preprocessing of the color image and the depth image, and outputs the preprocessed image; the laser radar SLAM module provides a basis for subsequent coordinate calculation and navigation path planning; the multi-modal VLM module is used for calculating the three-dimensional coordinates of the target location in the map; and the navigation control module controls the robot to move. The beneficial effects of the invention lie in that the system realizes efficient environmental perception and natural language understanding through multi-modal fusion, and can accurately identify a target location, calculate a three-dimensional coordinate and plan a safe path, thereby improving the autonomous navigation precision and reliability of the robot, and being suitable for intelligent movement control in a complex scene.

The invention discloses a multi-module collaborative navigation system for an intelligent robot, which relates to the field of robot navigation and comprises a multi-mode perception decision module, a semantic visual navigation module, a path memory fusion module and a rolling predictive control module which are electrically connected in sequence and work cooperatively. The multi-modal perception decision module performs weighted fusion on heterogeneous information, outputs scene representation and decision, and provides data support; the semantic visual navigation module analyzes the natural language instruction and combines visual data to complete path planning; the path memory fusion module carries out backtracking correction in an abnormal environment of semantic ambiguity and perception mismatch; and the rolling predictive control module realizes trajectory tracking and dynamic obstacle avoidance. According to the system, dependence on a prior map is reduced, instruction performability and dynamic obstacle avoidance capability are improved, collision risks are reduced, navigation real-time performance, stability and robustness are enhanced, and the system is compatible with multiple chassis and is suitable for complex dynamic scenes.

The invention belongs to the technical field of intelligent robots and environment perception, and particularly relates to a multi-mode autonomous positioning and inspection method and system for a hazardous chemical substance warehouse environment. According to the method, robust continuous positioning in a high-pollution characteristic environment is realized through combination of a layered confidence scheduling mechanism, an observation judgment mechanism and a time sequence clustering compensation algorithm; through path local reconstruction based on task semantics, the inspection system has task understanding and autonomous adjustment capabilities and adapts to multiple types of inspection tasks; the autonomous sustainable operation capability of the system in continuous inspection is improved through energy consumption and risk double-constraint control; a multi-mode degradation identification and self-healing mechanism ensures long-term stable operation of the sensor, and manual intervention is reduced; the whole inspection system can realize all-weather and unattended multi-mode autonomous inspection in a dangerous chemical warehouse scene, and provides stable mobile platform support for subsequent dangerous symptom identification and automatic early warning.

The invention provides a training method and device for a VLA model, and relates to the technical field of intelligent robots with bodies, and the method comprises the steps that the VLA model outputs a joint angle sequence of a mechanical arm of a robot with a body based on visual input and language input; the differentiable forward kinematics module outputs a real-time pose of an end effector of the mechanical arm in a task space; based on joint space loss formed by the joint angle sequence and the joint angle in the teaching data, task space loss formed by the real-time pose and the tail end pose in the teaching data, and task space constraint loss, a multi-objective loss function is constructed, and total loss is calculated; calculating the gradient of the total loss to the joint angle sequence and the real-time pose through back propagation, and optimizing preset parameters of the VLA model; the above steps are repeatedly executed until the total loss meets the expectation, and a trained target VLA model is obtained; the technical problems that when a VLA model is trained in a joint space, the hardware coupling performance is high, and the generalization ability is insufficient are solved.

The invention discloses a fork type mobile robot power distribution unit dynamic distribution control method and system, and relates to the technical field of intelligent robots, and the method comprises the steps: arranging sensors at a robot PDU input end and each output channel, collecting an operation data set in real time, and carrying out the preprocessing to form a dynamic feature data set; the main control microcontroller carries out power response analysis and evaluates a channel response state based on the dynamic characteristic data set, generates a preliminary power distribution instruction, calculates a target current MI during preliminary power distribution, carries out thermal stress analysis, and constructs a thermal stress identification index Htt to carry out thermal accumulation risk evaluation; when a heat accumulation risk exists, calculating a channel target current MII when the secondary power supply is distributed, and adjusting the duty ratio of an MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor) switch to carry out heat suppression regulation and control; and after the distribution is completed, calculating an energy utilization rate Nsys and executing energy efficiency state evaluation. According to the method, adaptive distribution and safe operation management of the multi-channel power supply of the robot are realized.

The invention relates to the technical field of robots, in particular to a complicated scene-oriented intelligent robot perception decision-making method and system. The method comprises the following steps: acquiring multi-modal environment sensing data; generating a warehouse point cloud map based on the multi-modal sensing data; marking shelf positions by using a warehouse point cloud map; when the intelligent robot with the body is driven to advance to the goods shelf position, goods posture information is collected, and goods surface features are recognized; cargo grabbing points are selected based on cargo surface features, and the grabbing posture of a mechanical arm is planned; a grabbing track is generated according to the grabbing posture of the mechanical arm; executing cargo grabbing simulation according to the grabbing track, and calculating a cargo slipping risk; and when the goods sliding risk is higher than a preset risk threshold value, the grabbing posture of the mechanical arm is adjusted, and mechanical arm grabbing optimization data is generated. According to the method, the success rate and the execution efficiency of the grabbing and placing tasks of the intelligent robot with the body in a complex scene are improved based on the robot technology.

The invention relates to the technical field of data processing, in particular to a BIM (Building Information Modeling)-based intelligent robot construction progress and quality linkage analysis system, which comprises a data acquisition module for acquiring robot sensor data and environment monitoring data, a semantic fusion module for generating structured data with semantic tags through a railway station building ontology library and outputting abnormal flag bits, the time sequence twinborn module generates a digital twinborn new version and a change vector based on an abnormal flag bit, the space matching module adopts an incremental learning mechanism to optimize a coordinate registration strategy, and the coupling analysis module establishes a quantitative evaluation model of the influence of the quality defect on the construction period and generates a risk early warning signal; the decision optimization module simulates a disposal scheme in a virtual environment to output a Pareto optimal solution set, and the execution tracing module generates a machine instruction set and constructs an auditable operation chain. Dynamic linkage analysis of construction progress and quality is realized through cooperation of multiple modules, and the problem of response delay is solved.

The invention discloses an automatic courtyard pool floating object salvage system based on image recognition, and relates to the technical field of intelligent robots, and the system comprises an image sequence acquisition module which is used for continuously capturing and transmitting an image sequence; the hydrological state sensing module is used for quantifying the dynamic and morphological characteristics of the water surface to obtain an average drift velocity vector and a water surface gradient field; the visual target positioning module is used for detecting a floating object to obtain an apparent two-dimensional coordinate vector; the trajectory fusion prediction module is used for fusing the average drift velocity vector, the water surface gradient field and the apparent two-dimensional coordinate vector, and calculating predicted physical coordinates; and the salvage operation execution module is used for receiving the predicted physical coordinates and driving the mechanical arm to complete salvage operation. According to the method, hydrological perception and visual positioning are fused, visual errors caused by dynamic disturbance of the water surface are corrected, the floating object track is predicted in combination with system delay, salvage operation is innovated from passive following to active interception, and the success rate and efficiency are remarkably improved.

The invention relates to the field of modern intelligent manufacturing and automation, and discloses a method for planning a path of an intelligent robot with a body based on visual inspection, which comprises the following steps: acquiring real-time image data, preprocessing and extracting position coordinates of an obstacle, and generating an obstacle distribution diagram; an A star algorithm is adopted to generate a preliminary optimal route sequence, and map information is updated; uncertain interference in the path is judged, and a path alternative set is generated through coordinate calibration; a time-varying parameter and a security constraint optimization path strategy are integrated through adaptive path optimization; key node coordinates are extracted and are fused with obstacle boundary identification to obtain a path execution sequence; real-time performance is checked, if calculation is overtime, a rollback mechanism is triggered, and an initial path is loaded and fused with real-time obstacle information to generate a final feasible route; a control instruction sequence is generated according to the final route and transmitted to the robot for execution. According to the method, efficient and adaptive path planning in a dynamic environment can be realized, and the real-time response capability and task execution reliability of the robot are improved.

The invention provides an underwater maintenance intelligent robot which is used for solving the problems that in the prior art, passive maintenance efficiency is low, cost is high, and safety risks exist. Comprising a frame main body, a cutting and recycling module and a washing module, the frame main body comprises a first main body, a second main body with the same structure as the first main body, and a self-adaptive connecting part for connecting the first main body with the second main body; the cutting and recycling module comprises a mounting seat fixedly connected with one side of the first main body, a sheath, a plurality of support arms, an elastic rod, a pulling power part used for pulling the sheath to bend and located in the circumferential direction of the anchor chain, a flexible chain holding part, a cutting part used for cutting a fishing net wound on the anchor chain, and a recycling part used for recycling and storing the fishing net cut by the cutting part; the washing module comprises a first washing part and a second washing part, and the first washing part and the second washing part are installed on the two sides of the second main body correspondingly and arranged in a staggered mode. The robot is high in operation efficiency, low in cost and capable of improving safety.

The invention relates to a robot path planning method, electronic equipment and a medium, and belongs to the technical field of intelligent robos.The method comprises the steps that an obtained static cost map and an obtained dynamic cost map are combined to obtain a global cost map; performing global path planning on the global cost map to obtain a global optimal path; performing local path planning based on the global optimal path to obtain a local optimal track, and controlling the robot to execute the local optimal track until a global target point is reached; and determining the dynamic cost of the local optimal trajectory, updating the dynamic cost map, further updating the global cost map, and performing path planning again. According to the method, the dynamic cost of executing the local optimal track by the robot is updated into the dynamic cost map, so that the robot can actively avoid a historical difficult passing area in the path planning process, and the navigation stability and long-term operation efficiency of the robot in a complex and narrow environment are improved.

The invention discloses a VLA task space control method and system for a multi-degree-of-freedom mechanical arm, and relates to the technical field of intelligent robot control, visual information and language instructions when the mechanical arm executes a task are obtained, and a trained VLA model is adopted to obtain a target pose of an end effector in a task space; an IK solver corresponding to the mechanical arm configuration is adopted, and according to the target pose, multiple sets of joint angle solutions of all joints of the mechanical arm are obtained; selecting an optimal joint angle of each joint from the multiple groups of joint angle solutions by adopting a preset optimization criterion; and after the optimal joint angle sequence of the mechanical arm is subjected to track smoothing processing, the mechanical arm is driven to move. Mechanical arm motion control is carried out in combination with a VLA model and an IK solver, the problems that the generalization ability between different mechanical arm configurations is poor, the data collection cost is high and the model interpretability is insufficient are solved, and the generalization ability and data efficiency of mechanical arm control 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 insulator detection method and system based on an intelligent robot, and relates to the field of image detection, and the method comprises the steps: collecting an image through an inspection robot carrying a visible light and infrared camera, processing a visible light image through employing a dynamic correction algorithm fusing Retinex-HSV, and achieving the registration fusion of a multispectral image through employing an SURF feature point and an RANSAC algorithm; based on an improved Mask R-CNN network, precise segmentation of the insulator is achieved, and then defect features such as surface dirt, structural cracks and core rod deterioration are extracted hierarchically; and finally, generating a defect comprehensive probability by fusing multiple features through a D-S evidence theory, and automatically alarming when the defect comprehensive probability exceeds a threshold value. The method has the advantages that the data quality is improved through multi-spectral image adaptive acquisition, Retinex-HSV dynamic correction and SURF-RANSAC fusion, and efficient and accurate full-automatic defect detection of the power transmission line insulator is realized in combination with lightweight Mask R-CNN segmentation and D-S evidence theory multi-feature decision.

The invention relates to a multifunctional on-site material evidence investigation and evidence obtaining system based on an intelligent robot dog, and belongs to the technical field of criminal investigation. According to the system, a robot dog is driven by a communication device and a positioner to reach a trace detection site, and collected trace detection image data are transmitted to a main control device; attitude precision adjustment is carried out through a joint encoder arranged on the angle driving framework in combination with the collected trace detection image data; calculating the actual distance between the microscope and the target trace through a trace detection distance model; based on the actual distance, the focusing driving framework is controlled to move in combination with the corresponding initial focusing parameters; and performing real-time definition evaluation on the acquired trace detection image through a definition evaluation framework, recording an optimal focusing position and related trace detection image data after the evaluation is passed, and updating a database. The multi-scene application of trace detection is realized, the problem of blurring of the trace detection image caused by environmental interference or sample movement is solved, and the focusing quality and the shooting success rate of trace detection shooting are effectively improved.

The invention relates to the technical field of intelligent robot control, in particular to a small robot dynamic balance control system based on DDPG, which comprises a sensor and a preprocessing module, provides accurate and reliable state data through denoising and standardization processing, and enhances the accuracy of subsequent processing; the ESO observer module estimates the system state and the total disturbance in real time, and the adaptive capacity and the anti-interference performance to the complex environment are improved; the information fusion and time sequence alignment module ensures information synchronization and accuracy through fusion processing and multi-dimensional state vector construction, meanwhile, the timeliness of a control decision is ensured, and the overall control effect is improved; the DDPG strategy network and the security layer module are combined with strategy optimization and multi-layer security constraints, continuous and safe control actions are output, and the stability and security of control are improved; and the actuator interface module realizes control instruction transmission, forms closed-loop feedback, adjusts a control strategy in real time, and improves the dynamic balance capability and the overall stability of the system.

The invention relates to a multi-source sensor fusion-based man-machine interaction method for an intelligent robot with a body, which belongs to the field of robot intelligent control, realizes high-precision and high-efficiency fusion of point clouds and images through calibration parameters and a self-adaptive weight algorithm, integrates a YOLO model and a semantic segmentation network, and improves the fusion accuracy of the intelligent robot with the body. Fine identification and motion trend pre-judgment of multiple types of targets are realized in combination with a trajectory prediction model; meanwhile, a visual explanation function is creatively added, previews of an original image, a point cloud fusion result and a target recognition result are provided, a radar map is supported to visually display point cloud distribution characteristics, and a user can visually evaluate a processing effect; in addition, the system also has a point cloud fusion algorithm test function, can calculate multiple indexes such as fusion time and errors, and provides data support for algorithm optimization. Through fusion of point cloud radar information and image data, the target identification accuracy and decision-making efficiency are further improved, and the reliability and robustness of the system in a complex scene are enhanced.

The invention discloses a ground mobile robot path planning method and system, and relates to the technical field of intelligent robot autonomous control. The method comprises the following steps: constructing a grid map model of a robot moving environment; based on the grid map model, establishing a kinematic model of the robot and setting a movement rule, and initializing to generate a plurality of paths from a starting point to an ending point to form an initial path set; iteratively merging and optimizing the paths in the initial path set by adopting a group merging strategy, and screening out a current global optimal path; based on the current global optimal path guidance, a derivative path is updated based on an improved pigeon inspired optimization map and a compass operator, and a first-stage optimization set is obtained; and through iterative optimization of a landmark operator, dynamically calculating a landmark center according to a cost value and updating a path position to obtain a second-stage optimization set. And selecting an optimal path and carrying out connection smoothing processing to generate an optimal complete path. According to the method, the global optimal path can be efficiently and stably planned in a complex environment.

The utility model discloses a high-sensitivity flexible tactile sensor, a sensor module and an intelligent robot with a body, the high-sensitivity flexible tactile sensor comprises a flexible chamber substrate, an anti-corrosion protection ring, a flexible cover film, an induction material layer and a plurality of convex structures, the flexible chamber substrate comprises a flexible substrate, the anti-corrosion protection ring is arranged on the flexible substrate, and the anti-corrosion protection ring is arranged on the anti-corrosion protection ring. The electrode layer and the flexible bonding layer are sequentially and regularly arranged on the front face of the flexible substrate, the flexible bonding layer surrounds an interdigital electrode area of the electrode layer to form a cavity structure, and an electrode lead of the electrode layer is led out of the cavity structure. According to the high-sensitivity flexible tactile sensor, due to the innovative design of the flexible sealing cavity substrate, the flexible cover film and the protruding structure, the flexible tactile sensor shows high sensitivity and low detection limit.