132 results about "Gait control" patented technology

The invention relates to the technical field of robot control methods, in particular to a simulation-to-reality migration-oriented robot gait optimization method, which comprises the following steps of: acquiring input characteristics of a robot; constructing a gait control model by adopting a near-end strategy optimization reinforcement learning algorithm; training the gait control model in parallel in a physical simulation environment; the input feature matrix is input into the trained gait control model, a joint target position control instruction is generated, and the robot is controlled to execute gait actions; executing domain randomization operation by applying simulation to a reality checking tool; the simulation parameters are adjusted according to the difference parameters, and optimized gait control model parameters are output. The uncertainty of the real environment is covered by applying simulation to reality troubleshooting tool execution domain randomization operation, the systematized difference positioning process is combined, the simulation and real machine difference parameters are recognized, and the accuracy of the gait control model is improved. And action malformation, poor stability or hardware damage of the gait strategy of simulation training during real machine deployment can be prevented.

The invention discloses a robot gait self-adaptive adjustment method and system combined with topographic feature recognition, and relates to the technical field of robot gaits, and the method comprises the steps: collecting a topographic image in front of a robot advancing path, carrying out the deblurring processing, and extracting ground feature data through a lightweight neural network; matching the ground characteristic data with a preset gait control parameter database to obtain a gait control parameter set, and generating a control signal through a proportional-differential control method according to the gait control parameter set; correcting the control signal based on a reinforcement learning algorithm, and generating a target control instruction for driving a joint actuator; in the process of executing the target control instruction, collecting state feedback data in real time, and comparing and optimizing the feedback data with the currently executed target control instruction to realize feedback adjustment; the problems of insufficient terrain recognition precision caused by image blurring, response lag caused by sensing and control decoupling and lack of adaptive ability are effectively solved.

The invention discloses a self-adaptive gait control method, system and device for a quadruped robot in a limited space and a medium, and belongs to the technical field of gait control. Identifying terrain categories through a classifier and acquiring initial gait parameters from a gait library; adjusting the gait parameters according to the terrain gradient and the roughness to obtain optimized gait parameters; calculating stability indexes and fusing scores; when the score is lower than a threshold value, an adjustment strategy is determined according to an instability mode to correct gait parameters; a foot end track is generated and control is executed; and the executed information is fed back to terrain perception to form a closed loop. According to the method, the terrain recognition accuracy is improved through multi-modal information fusion, the adaptive mapping from the terrain to the gait is established to achieve automatic gait switching and parameter optimization, the stability is evaluated through multi-index fusion, a targeted adjustment strategy is selected according to an instability mode, the adaptive gait control problem under the limited space and the complex terrain is solved, and the adaptive gait recognition accuracy is improved. And the movement efficiency and the stability are improved.

The invention discloses a humanoid robot reinforcement learning gait control method and system, and belongs to the technical field of robot control. Comprising the following steps: constructing a model prediction control optimization problem based on a linear inverted pendulum model, and generating a robot mass center track and a foothold sequence; collecting state-action pair data of model prediction control, and training a neural network model by adopting supervised learning; designing a reinforcement learning strategy network, taking the output of the neural network model as a physical guidance reference, calculating a training reward based on the constructed composite reward function, training the strategy network in a high-concurrency simulation environment by adopting a near-end strategy optimization algorithm, and outputting a joint action instruction; and verifying the trained strategy and the joint action instruction output by the trained strategy in a plurality of simulation environments, and deploying the trained strategy and the joint action instruction to a real machine for dynamic gait control. According to the method, the stability and adaptability of gait control of the humanoid robot are improved through a method of combining model predictive control and reinforcement learning.

The invention discloses a bionic walking feature fused quadruped robot gait reinforcement learning training method, which comprises the steps of S1, bionic gait feature modeling for extracting key features from a motion mode of a natural quadruped animal and constructing a bionic template capable of directly guiding robot gait control; and S2, constructing a reinforcement learning training environment, and simulating diversified actual scenes by constructing a high-fidelity simulation platform. According to the method, key features are extracted from natural four-footed animal gaits, a bionic template library is constructed, and natural features such as nonlinear rhythm and dynamic symmetry of animal movement are fused into robot gait control, so that the problem of action mechanical stiffness caused by dependence on manual design of a track in a traditional method is effectively solved; movement of the robot is closer to natural biological gaits, impact generated when the robot interacts with the environment is reduced while movement energy consumption is reduced, and movement smoothness is improved.

The invention discloses a humanoid robot and a shank structure thereof. The shank structure comprises a knee frame, a foot plate, a driving assembly and a cam; the foot plate is rotatably connected to the knee frame around the X axis; the knee frame is provided with a rotating seat; the inner shaft is rotationally connected to the rotating seat around the Y axis; the fixing sleeve is rotatably mounted on the inner shaft; the driven planetary gears are meshed with the driving planetary gears on the two sides; one end of the side shaft is limited to the fixing sleeve, and the driven planetary gear is rotationally arranged on the side shaft in a sleeving mode. The upper end of the connecting rod is rotatably connected to the connecting seat, and the lower end of the connecting rod is rotatably connected to the foot plate; when the cams at the left and right positions rotate towards the same direction, the connecting seat drives the foot plate and the knee frame to rotate around the Y axis through the connecting rod; when the cams at the left and right positions rotate in opposite directions, the connecting seat rotates around the side shaft and drives the foot plate to rotate around the X axis relative to the knee frame through the connecting rod. According to the scheme, more flexible gait control can be achieved, and the problem that the weight burden of shanks is too heavy due to a direct drive mode in a traditional system is solved.

The invention discloses a humanoid robot gait control method, and relates to the technical field of robot adaptive control, and the method comprises the steps: collecting the lifting height of a front foot; all joints of the rear legs are controlled to rotate, so that the rear feet are located under the gravity center of the humanoid robot; after the forefoot makes contact with the object, the pressure distribution condition of the forefoot is collected; whether the forefoot state is stable or not is judged, and if not, the rotation angle of the forefoot is collected; if the rotating angle is smaller than the angle threshold value, the front leg and the rear leg are converted, otherwise, the front foot is lifted, the joint angle of the front leg is adjusted, so that the object making contact with the front foot is changed, and the pressure distribution condition of the front foot is collected again. The walking stability is improved through inward rotation of the rear legs and reverse bending of the knee joints, the front feet can feel the pressure distribution condition of the contact surface, and when environment information detected by the sensing unit is not very accurate, the adaptability of gaits to the environment is improved through pressure sensing of the front feet, and the walking stability is improved. And the walking stability of the humanoid robot is further improved.

The invention provides a robot sensing terrain recognition gait control method and system, a medium and equipment, and belongs to the technical field of robot sensing. The method comprises the following steps: acquiring pitch angle data and plantar force data of the biped robot on the basis of optimal plantar force; inputting the pitch angle data and the plantar force data into a trained KNN model, and identifying the current walking terrain of the robot to obtain a terrain identification result; the training process of the KNN model comprises the steps that the biped robot is driven to pass through a rugged terrain and a flat terrain in a fixed stride frequency gait mode, pitch angle sample data and plantar force sample data passing through different terrains are collected and input into the KNN model, and the KNN model is used for conducting terrain classification; and the robot controller receives a terrain recognition result, and adjusts the stride frequency and the gait of the robot according to a preset gait control strategy based on the result. According to the method, an additional sensor does not need to be added to the robot, and the terrain can be identified only through state feedback of the robot.

The invention belongs to the field of robot motion control, and particularly relates to a humanoid robot multi-terrain gait control method and system fused with visual perception. The method comprises the following steps: acquiring multi-modal sensing data of the humanoid robot, and preprocessing the multi-modal sensing data; the preprocessed multi-modal sensing data are input into a pre-trained world model, and the world model updates and outputs a potential state at the current moment based on a historical recursive state, a historical random posteriori state and an action sequence in a historical updating interval; inputting the potential state at the current moment into a pre-trained strategy network, and outputting an action at the current moment; and based on the action at the current moment, the driving torque of each joint is calculated through a PD controller, and the humanoid robot is driven to move. According to the method, a world model structure is introduced, so that the robot can realize more stable and more efficient gait control and terrain adaptation of the humanoid robot under the condition that the robot only depends on perception information which can be acquired by the robot.

The invention discloses a deep reinforcement learning method for gait control of a digital twinning-assisted crocodile-imitating robot. The method is divided into a pre-training stage and a real-time training stage. The method comprises the following specific steps: step 1, constructing a twin system model of the crocodile-imitating robot by utilizing simulation and modeling technologies, and storing the twin system model in edge equipment; step 2, constructing a state space and an action space of gait control of the crocodile-imitating robot; step 3, designing a reward function considering forward movement gait speed, stability and low energy consumption; step 4, collecting data in parallel by adopting multiple processes for pre-training to obtain a twin decision model; and step 5, deploying the twinborn decision model in edge equipment, and carrying out real-time training on the crocodile-imitating robot by utilizing mutual information transmission between a communication module and a twinborn physical entity. According to the method, the pre-training is carried out by establishing the digital twin model, and the training efficiency of the gait control of the crocodile-imitating robot is improved by utilizing the strong computing power and storage resources of the edge equipment.

The invention discloses a flexible ankle joint exoskeleton control method based on a multi-modal large model, and the method comprises the steps: enabling a flexible ankle joint robot to collect the data of a surrounding environment through a visual camera part, carrying out the environment recognition, and enabling an image recognition module to carry out the property classification of a text language through a multi-modal technology; the lower limb master controller carries out operation processing analysis on original data collected by the IMU and the force sensor; the operation main control board is used for processing visual part information and human body actual movement gait information in real time; performing weight distribution on text features representing the environment by using CHATGPT, scheduling together with current gait information, and generating gait parameters at the next moment and scheduling a gait control function; and the main control board completes auxiliary control under real-time interaction of the ankle joint and the environment by executing a control algorithm. According to the scheme, a new-generation exoskeleton robot which is low in cost, light in weight and capable of achieving body intelligence is successfully built.

The invention provides a humanoid robot single-leg gait transition action design and a control method thereof, and through an algorithm module of a finite-state machine and the single-leg gait transition action design, the problems of center-of-gravity shift and insufficient control precision of a biped robot during gait transition can be effectively solved. By means of the method, the gait control capacity of the robot in the complex environment is remarkably improved, especially in the single-foot supporting and single-leg jumping stage, the robot can be stably switched to the corresponding gait, and the possibility of inclination and unbalance is reduced.

The invention relates to the technical field of adaptive control, in particular to a robot dog gait adaptive control method and system facing complex terrains. The specific implementation process comprises the following steps: quantifying external interference generated by unexpected physical interaction through an endogenous interference observer; a configurable gait control loop is used for generating a compensation control signal, and the actual movement gait is adjusted through fusion of a feedforward adjuster and the basic movement gait; continuously collecting and generating a historical interference sequence, and analyzing and extracting a continuous interference factor; and performing online self-adaptive correction on the gait dynamic model by using the continuous interference factor and re-planning the basic motion. According to the method, a dual adaptive control architecture is constructed, and feedforward compensation of instantaneous interference and online model adaptive correction of persistent deviation are combined. The problem that the robot dog is insufficient in robustness when facing complex terrains is effectively solved, and the walking ability of the robot dog in the complex terrains is improved.

The invention discloses an exoskeleton rehabilitation wheelchair robot with high safety and stability, and particularly relates to the technical field of exoskeleton wheelchairs, the exoskeleton rehabilitation wheelchair robot comprises a fixing support, a fixing chassis is fixedly arranged at the bottom of the fixing support, four driving wheels are rotatably arranged at the bottom of the fixing chassis, and a connecting frame is telescopically arranged at the top of the fixing support; a back supporting frame is telescopically arranged at the top of the connecting frame, two mounting seats are fixedly arranged on one side of the connecting frame, rehabilitation training devices are rotatably connected to the bottoms of the two mounting seats, and foot supporting plates are rotatably connected to the tops of the two rehabilitation training devices; the controller is pushed by the second telescopic rod to rotate so as to drive the rehabilitation training device to rotate, legs are hung and tightened, the structural strength is improved, the movement gait control is more accurate by adopting a push rod mode, the stability of a person standing on the seat cabin can be further improved, shaking is avoided, and the rehabilitation training effect is better. And the stability and the structural strength of the legs are improved to a certain extent.

The invention discloses a gecko-imitating intelligent robot, and relates to the technical field of biological bionic intelligent robots, the gecko-imitating intelligent robot comprises a gecko-imitating front body and a gecko-imitating rear body, and the gecko-imitating front body and the gecko-imitating rear body are connected through a body connecting part; light-weight mechanical structure design is adopted, 3D modeling is carried out, all parts are manufactured in combination with the 3D printing technology, a multi-joint driving system composed of an STM32F103C8T6 main control chip and an SC90 steering engine is carried, multi-foot gait control is achieved, water-based polyacrylate pressure-sensitive adhesive is adopted for the feet, gaps of the feet are filled, the feet of the gecko are covered, and the gecko gait control effect is achieved. The robot can be stably adhered to the surfaces of various materials such as glass and concrete, in addition, Bluetooth / Wi-Fi wireless control is supported, advancing, steering and obstacle crossing actions of the robot can be remotely operated through a mobile device APP, and the robot can be suitable for high-risk scenes such as urban pipeline flaw detection, high-rise building cleaning and earthquake ruin search and rescue.

The invention discloses an adaptive specified performance PMSM (permanent magnet synchronous motor) system control method based on a disturbance observer, which solves the asymmetric specified performance constraint suffered by the system by designing an effective funnel-shaped performance function without depending on an initial output error, ensures the transient and steady-state performance of the asymmetric funnel constraint, and improves the stability of the system. The defect that the initial tracking error in common funnel control and PPC must be accurately kept within the boundary is overcome; the problem of'complex explosion 'of classical anti-gait control is successfully solved by applying an instruction filter, and filtering errors caused by the instruction filter are effectively eliminated by constructing a proper compensation system; the robustness and the control precision of the control system are ensured by designing a fast convergence DOB, and the minimum learning parameters of the neural network are greatly reduced; by applying a Lyapunov function theory and a backstepping technology, virtual and actual controllers with adaptive laws are constructed, so that the stability of the system is ensured.

The invention discloses a reinforcement learning biped robot gait control method based on a multilayer adaptive exploration mechanism, and belongs to the field of biped robot reinforcement learning control. The method aims at solving the problems that in existing gait control based on a near-end strategy optimization (PPO) algorithm, strategy convergence is unstable, and exploration and utilization balance is insufficient. According to the method, a multi-layer adaptive exploration mechanism is introduced on the basis of a standard PPO framework: firstly, a state-dependent standard deviation network is designed to realize adaptive noise adjustment of a space layer for dynamically controlling action disturbance intensity; secondly, constructing an entropy regularization mechanism based on advantage weighting, and realizing exploration intensity adaptive control of a direction layer; thirdly, a dynamic entropy coefficient is designed according to the uncertainty of a value function, and exploration rhythm self-adaptive adjustment of a time layer is achieved. Through the synergistic effect of three layers of mechanisms, the strategy realizes dynamic balance of exploration and convergence in different training stages, so that the stability of the training process and the strategy convergence quality are improved.

The invention discloses a six-dimensional force measurement matrix system and method for multi-terrain gait analysis of a humanoid robot. The system comprises a modular terrain simulation platform, a six-dimensional force measurement matrix module, a data acquisition and synchronization module and robot gait performance analysis software. The modular terrain simulation platform comprises a rigid leveling module, a slope module and a step module; the six-dimensional force measurement matrix module comprises a plurality of six-dimensional force measurement plates; the data acquisition and synchronization module acquires synchronous matrix data of each six-dimensional force measurement plate; and the robot gait performance analysis software calculates key kinetic parameters and time-space parameters and generates a comprehensive report. According to the invention, three terrains of a rigid flat ground, a slope and steps can be simulated, force and torque of the humanoid robot walking on the terrains are continuously and synchronously acquired through the measurement matrix, and comprehensive, real and accurate evaluation of gait functions is realized. And high-precision and multi-dimensional quantitative evaluation data are provided for gait control algorithm development, dynamic performance calibration and mechanical foot design of the robot.

The invention belongs to the field of robot control, and particularly relates to a quadruped robot accurate gait control method based on dynamic optimization. The method comprises the steps that phase states of four legs of the quadruped robot are judged, supporting phase foot end force is calculated for the legs in a supporting phase according to a real-time motion state and an expected motion state, and swinging phase correction force is calculated for the legs in a swinging phase; an expansion state observer is constructed based on the leg Cartesian space dynamics model, and the expansion state observer is used for observing the total disturbance term of the four legs of the quadruped robot in real time; calculating to obtain a disturbance value to be compensated based on the total disturbance term, obtaining an uncompensated joint torque based on the support phase foot end force or the swing phase correction force, and superposing to obtain a joint torque instruction; and driving the quadruped robot according to the joint torque instruction. According to the method, an advanced observation and compensation mechanism is introduced in a bottom layer execution link, accurate implementation of an upper layer optimization target is ensured, and finally accurate gait control is achieved.

The invention discloses a quadruped robot cargo placement correction method fusing visual identification and voice interaction, and the method comprises the following steps: S1, collecting and preprocessing cargo images, and generating a standardized image input set; s2, constructing a spatial state sensing model, extracting image features and generating error vectors; s3, recognizing user voice and extracting semantic keywords such as direction and action instructions; s4, fusing the error vector and the semantic keyword, generating a multi-modal fusion input representation, and encoding the multi-modal fusion input representation into a fusion embedding representation; s5, generating a gait control parameter and an action control sequence based on the fusion embedding representation, and constructing an action plan structure; s6, executing the action plan structure to complete cargo position and posture correction, and recording state feedback data; and S7, in combination with state feedback and additional voice, constructing an interactive correction closed-loop control process, and updating control information. According to the method, the multi-modal understanding and action adjusting capability of the robot is enhanced.

The invention discloses a multi-mode sensing security humanoid robot and a control method thereof, and relates to the technical field of security humanoid robots, and the specific steps are as follows: step 1, integrating sensing devices and control chips of all parts of the robot, configuring scanning frequency parameters, and realizing environment sensing and action control functions; the method comprises a first step of establishing a dangerous sample feature library through multi-source data labeling, a second step of fusing multi-sensor data to realize robot gait control, and performing identification and verification on personnel based on pre-stored data, and a third step of establishing the dangerous sample feature library through multi-source data labeling, and realizing a whole process of danger identification and response by calculating a sample feature matching degree. And a sample equalization coefficient is optimized, and the feature difference learning ability of the model to dangerous actions / scenes and non-dangerous actions / scenes is improved, so that the danger identification precision is improved, and rapid identification and response to dangerous conditions are realized.

The embodiment of the invention provides a robot motion control method and device.The method comprises the steps that the current state of a robot is obtained, the current state comprises the current position and the target position of the robot, the current state is input into a motion control model, and the current action needing to be executed by the robot is obtained; the motion control model is obtained by performing multi-task training on the reinforcement learning model, the multi-task training comprises path planning task training and at least one of gait control task training and balance control task training, and the robot is controlled to execute the current action. And if the robot reaches the target position after completing the current action, completing the current motion task. According to the method provided by the embodiment of the invention, the robot can improve the stability on the basis of efficiently and reasonably planning the path.

The invention discloses a robot gait control method and device suitable for walking on a slippery road surface and a biped robot. A reinforcement learning and MPC combined control mode is adopted, the robot is controlled to carry out normal gait walking, and data of joint motors and foot sensors are collected in real time; the state of the robot is judged, specifically, when the robot possibly overturns, the robot enters a slow gait mode, and the judgment standard that overturning possibly occurs is that the states of n steps in the future are deduced in the simulation deduction environment, and whether the n step presents the overturning trend or not is judged; and when the robot overturns, the robot enters a high-frequency gait mode, and a related gait regulation and control mode is designed. The problem that the biped robot easily slips on a wet and slippery road surface is solved; and when slipping occurs, the problem is difficult to remedy. And the stable walking capability of the robot on a complex road surface is improved.

The invention belongs to the technical field of robots, and discloses a multi-motion-mode robot which comprises a trunk, a crawling leg assembly and a jumping assembly. The crawling leg assemblies are installed on the left front side, the right front side, the left rear side and the right rear side of the trunk respectively, and therefore the four-foot distributed crawling robot is formed. In cooperation with a gait control strategy, the multi-motion-mode robot can generate a collaborative motion mode similar to a biological limb, and compared with a biped or hexapod robot, the multi-motion-mode robot has better ground contact stability. A jumping assembly is arranged on the belly side of the trunk, and the multi-motion-mode robot can quickly advance or cross obstacles through a jumping control strategy. In addition, when the multi-motion-mode robot turns on one side, the reset structure can be combined with part of the crawling leg assemblies to achieve self-reset. The multi-motion-mode robot is compact in structure, high in adaptability and capable of achieving high-maneuverability obstacle crossing and rollover self-resetting in complex terrains, and integrates a crawling mode and a jumping mode.

The invention relates to the technical field of rehabilitation medical robots, and particularly provides a joint angle vector generation method and a gait control method.The generation method comprises the steps that a first gait phase and multi-sensor data at the current moment are obtained; the first gait phase is the gait phase of the lower limb exoskeleton robot at the previous moment, and the multi-sensor data comprises multi-modal motion data and visual data; predicting a second gait phase according to the first gait phase and the multi-modal motion data; the second gait phase is the gait phase of the lower limb exoskeleton robot at the current moment; adjusting a reference joint angle vector and a time scaling factor of a self-adaptive joint angle vector generator of which the weight is optimized in advance according to the visual data, and then generating an expected joint angle vector according to the second gait phase by utilizing the adjusted self-adaptive joint angle vector generator; according to the method, the joint angle vector which adapts to the current environment and is high in gait bionic performance can be generated.

The present invention discloses a gait control method for a quadruped robot using a dual-level central pattern generator (CPG) inspired by the basal ganglia, comprising: modeling the single legs of the quadruped robot using forward kinematics and inverse kinematics; using a dual-level central pattern generator for each leg, consisting of a rhythm generation part and a pattern formation part, wherein the rhythm generation part is mainly responsible for phase change, and the pattern formation part is mainly responsible for the coordination of each joint; determining the hierarchical structure and coupling topology of the CPG network; and introducing a behavior selection model of the basal ganglia, which can be used to select the optimal gait for different environments and improve environmental adaptability. Compared with traditional control methods, the control of the quadruped robot using the present invention has a high degree of biological rationality, stronger robustness to external interference, good adaptability to terrain, and can flexibly control various movement modes of the quadruped robot.

The application discloses a kind of foot and track type compound mobile robot drive control system and method, the system includes information acquisition sensor, host computer, foot type drive subsystem and track type drive subsystem, after information acquisition sensor gathers the obstacle size information of pre-specified active area, host computer compares the size of obstacle size and preset safety threshold to confirm whether foot type drive mode or track type drive mode is selected, and gait control instruction or track drive instruction is generated according to obstacle size information;When foot type drive subsystem receives gait control instruction sent by host computer, foot type drive mechanism is controlled to move according to gait control instruction;When track type drive subsystem receives track drive instruction sent by host computer, track type drive mechanism is controlled to move according to track drive instruction.The application can automatically switch the motion mode of foot and track type robot according to the size of obstacle, and can adapt to more complex working environment.

The invention belongs to the technical field of biological simulation, and relates to an analogue simulation method based on gait control data and related equipment thereof. Carrying out reinforcement learning on the gait simulation agent to obtain the gait simulation agent after reinforcement learning is completed and gait simulation continuous actions output by the gait simulation agent after reinforcement learning is completed; gait control data of the target object are collected; inputting the gait simulation intelligent body into the gait simulation intelligent body after reinforcement learning is completed, and obtaining a walking simulation continuous action corresponding to the target object; through comparison, identifying differential actions; and performing gait adjustment according to the gait control data corresponding to the differential actions. The method is applied to a medical biological simulation science and technology product optimization scene, product stress support adjustment can be carried out in combination with reinforcement learning and analogue simulation modes for an intelligent artificial limb user or an intelligent walking stick user, and a better product experience service is provided for related patients.

The present application belongs to the field of robotics technology, and discloses a multi-motion modal robot, including: a trunk, a crawling leg assembly and a jumping assembly. A crawling leg assembly is installed on the left front side, the right front side, the left rear side and the right rear side of the trunk, respectively, to form a four-legged distributed crawling robot. In conjunction with the gait control strategy, the multi-motion modal robot can produce a coordinated motion mode similar to that of biological limbs, and has better ground contact stability than bipedal or hexapod robots. A jumping assembly is arranged on the ventral side of the trunk, and the multi-motion modal robot can move forward quickly or cross obstacles through a jumping control strategy. In addition, when the multi-motion modal robot rolls over, the reset structure combined with part of the crawling leg assembly can reset itself. The multi-motion modal robot of the present application has a compact structure and strong adaptability. It can achieve high-maneuverability obstacle crossing and self-reset after rollover in complex terrain, and integrates crawling mode and jumping mode.

The invention belongs to the technical field of underwater bionic robots, and particularly relates to a bionic duty ratio gait control method for an elastic robotic fish, a program, equipment and a storage medium. According to the method, improvement is carried out based on a traditional Hopf oscillator, a CPG oscillator simulating a red muscle activation mode of fish is constructed, a CPG network model based on an improved Hopf central pattern generator is constructed according to the structure of the elastic robotic fish, and actuator control mapping is completed; and a PID-CPG closed-loop controller is constructed, and the actual maximum angle of each joint gradually approaches the maximum angle expected value through continuous iteration of the motion period. The universal gait control method of the bionic robotic fish is constructed according to the physiological characteristics of fish red muscle activation, the power consumption of the elastic robotic fish can be effectively reduced, and the efficiency can be improved.