89 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 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 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 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 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 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 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 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.

The application discloses a kind of humanoid robot gait control method and system based on imitation and inverse reinforcement learning, collect human or simulation robot walking data to construct high-quality training dataset, then through graph convolution structure, the similarity of robot and expert data is calculated.Based on task target design multidimensional linear reward function, four kinds of indexes such as stability, gait periodicity, trajectory similarity and energy efficiency are comprehensively considered, and multi-objective optimization is realized through weight distribution.Neural network is used to construct strategy model, and training is carried out in combination with proximal policy optimization algorithm, and the update amplitude is constrained using strategy ratio clipping technology to improve learning stability.Maximal entropy principle is introduced to dynamically update reward function, and the adaptability to expert strategy is enhanced.Finally, simulation verification is carried out through MuJoCo physical engine, and visual analysis and performance evaluation of high human-like gait are realized.The application solves the problems of robot gait adaptability and robustness in complex dynamic environment.

The present application relates to a kind of soft landing reward function-based quadruped robot low-noise gait control method and system, the method includes: constructing quadruped robot motion control environment, and defining foot contact determination standard;Design soft landing reward function based on vertical contact force, for the negative punishment of large contact force at the moment of landing, guide quadruped robot to learn low contact force landing mode;Collect the real-time contact force of quadruped robot foot and historical contact state, with soft landing reward function as core optimization target, using reinforcement learning algorithm, in combination with gait stability constraint, iterative training is carried out to strategy network, and low-noise gait strategy network is obtained, for optimizing output gait control signal, to control the gait action of quadruped robot accordingly.Compared with prior art, the present application can consider the noise control and gait stability of robot by accurately determining the moment of landing, designing contact force negative reward and combining reinforcement learning to actively optimize gait strategy.

This invention discloses a semi-passive gait control method and system for bipedal robots based on adaptive Hopf-CPG, relating to the field of robotics. The method includes the following steps: establishing a dynamic model of the bipedal robot with knee joints; optimizing key structural parameters of the robot based on the dynamic model; constructing an adaptive Hopf-CPG network; introducing an adaptive mechanism, including frequency adaptation, amplitude adaptation, and phase adaptation; and fusing the adaptive Hopf-CPG network with semi-passive gait control, dynamically adjusting the active adjustment ratio of the CPG network according to the robot's walking environment to achieve a balance between passive energy saving and active stability. Compared with existing technologies, this method significantly improves motion stability, enhances anti-disturbance capabilities, reduces energy consumption, improves endurance, and exhibits strong environmental adaptability, eliminating the need for manual parameter adjustment.

This invention discloses a biomimetic cheetah spine elastic energy storage high-speed gait mechanism, belonging to the field of biomimetic legged robot motion execution and efficient gait control technology. Currently, quadrupedal and humanoid robots generally suffer from low energy utilization, high overall motion power consumption, significant gait rigidity impact, and limited continuous long-distance high-speed movement capability under high-speed running conditions. Conventional rigid linkage structures are difficult to achieve the efficient conversion and recycling of kinetic and elastic potential energy during biological movement. This invention mimics the biological movement mechanism of a cheetah during high-speed running, characterized by periodic bending and extension of the spine accompanied by elastic energy storage and release through tendons. It designs a multi-segment biomimetic flexible spinal main structure, combined with high-modulus energy storage tendon components, joint damping buffer units, and a collaborative drive linkage mechanism. This enables the robot to recover, store, and release energy within the complete gait cycle of stepping, takeoff, and landing, thereby significantly improving running speed, reducing drive system load, minimizing body vibration and impact, and enhancing overall stability and endurance under high-speed movement conditions. This technical solution has a highly original structure and is significantly different from existing bionic gait mechanisms. It can be widely used in equipment such as high-speed quadruped robots, emergency rescue robots, outdoor inspection robots, and racing robot platforms.

The invention discloses a self-adaptive stair-climbing manned stair-climbing machine and a bionic climbing gait control method, the stair-climbing machine comprises a seat and a driving mechanism, and the driving mechanism comprises a self-adaptive stair-climbing assembly, a dynamic posture stabilizing assembly and a closed-loop measurement and control module. The self-adaptive stair climbing assembly is alternately switched to be in the state of making contact with the step plane through the overturning wheel disc set and the multiple first rolling wheels, and the first rolling wheels rotate while the wheel disc set overturns. The dynamic attitude stabilization assembly provides dynamic multi-point support through liftable front and rear support units. The closed loop measurement and control module senses the environment through the sensing system. The three modules cooperatively execute a bionic climbing gait control strategy, and the strategy comprises the steps that after stepping overturning, based on feedback, rotation of a roller and micro-motion of a wheel disc are cooperatively adjusted, so that position and posture cooperative control of precise falling is achieved; the front supporting unit is controlled to asynchronously stretch out and draw back to achieve stable migration of the gravity center. According to the invention, stable and continuous full-scene self-adaptive movement is realized, and high-safety and comfortable riding experience is provided for a user.

The invention discloses a humanoid robot gait control method and system based on simulation and inverse reinforcement learning, and the method comprises the steps: collecting the walking data of a human body or a simulation robot to construct a high-quality training data set, and calculating the similarity between the robot and expert data through a graph convolution structure; a multi-dimensional linear reward function is designed based on a task target, four types of indexes including stability, gait periodicity, trajectory similarity and energy efficiency are integrated, and multi-target optimization is achieved through weight distribution. A neural network is adopted to construct a strategy model, training is performed in combination with a near-end strategy optimization algorithm, and a strategy ratio cutting technology is utilized to constrain an update amplitude so as to improve learning stability. And meanwhile, a maximum entropy principle is introduced to dynamically update a reward function, so that the adaptability to expert strategies is enhanced. Finally, simulation verification is conducted through a MuJoCo physical engine, and visual analysis and performance evaluation of the highly-simulated human gait are achieved. According to the method, the problem that the robot gait self-adaptability and robustness are insufficient in a complex dynamic environment is solved.

The invention discloses a limp control method, a limp control device, a hybrid vehicle and a storage medium, and relates to the technical field of vehicle control. The limping control method is applied to the hybrid power vehicle provided with a series-parallel hybrid power system and comprises the steps that when a high-voltage fault of a power battery occurs in the whole vehicle running process, a whole vehicle controller is used for controlling an engine, a generator and a driving motor to execute a torque zero clearing instruction, and a clutch is controlled to be separated; after the vehicle speed of the whole hybrid power vehicle is zero, the electric auxiliary machine is controlled to stop working, the first main positive contactor, the second main positive contactor and the main negative contactor are disconnected, and the emergency contactor is closed; and controlling an engine control mode of the engine to be switched from torque control to rotating speed control, controlling a generator control mode of the generator to be switched from torque control to non-control, and re-controlling the electric auxiliary engine to start working so as to control limp running of the hybrid vehicle.

This invention relates to the field of adaptive control technology, specifically to a gait adaptive control method and system for robotic dogs facing complex terrain. The specific implementation process includes: quantifying external disturbances generated by unexpected physical interactions through an endogenous disturbance observer; generating compensation control signals using a configurable gait control loop, and adjusting the actual gait by fusing it with the basic gait using a feedforward regulator; continuously collecting and generating historical disturbance sequences, and analyzing and extracting persistent disturbance factors; and using the persistent disturbance factors to perform online adaptive correction of the gait dynamics model and replanning the basic motion. This invention constructs a dual adaptive control architecture, combining feedforward compensation for instantaneous disturbances with online adaptive correction of persistent deviations. This effectively improves the problem of insufficient robustness of robotic dogs facing complex terrain, enhancing the walking ability of robotic dogs in complex terrain.

The present application belongs to the technical field of underwater bionic robots, and particularly relates to a bionic duty cycle gait control method for an elastic robotic fish, a program, an equipment and a storage medium. The present application is improved based on a traditional Hopf oscillator, a CPG oscillator simulating the red muscle activation mode of fish is constructed, a CPG network model based on the improved Hopf central pattern generator is constructed according to the structure of the elastic robotic fish, and actuator control mapping is completed; a proportional-integral-derivative (PID) CPG closed-loop controller is constructed, and through continuous iteration of the motion cycle, the actual maximum angle of each joint gradually approaches the maximum angle expected value. According to the physiological characteristics of the red muscle activation of fish, the present application constructs a general gait control method for bionic robotic fish, which can effectively reduce the power consumption of the elastic robotic fish and improve the efficiency.

The invention provides a multi-gait controller generation method, a controller and a quadruped robot, and relates to the technical field related to robot control, in the multi-gait controller generation method, a two-stage training framework is designed based on deep reinforcement learning, the controller is generated through the two-stage training framework, and the quadruped robot is controlled through the controller. The control mode can be divided into automatic posture control and manual posture control, so that when kinematics and dynamics models of the quadruped robot are not needed, the quadruped robot can be controlled to stably move in various gaits only by using an IMU and an encoder of a joint motor as body sensors, and in addition, the stability of the quadruped robot is improved. It is guaranteed that the quadruped robot can pass under the complex terrain and stably move in various gaits.