72 results about "Gait planning" patented technology

The invention relates to a biped robot complex terrain self-adaptive gait planning method and a biped robot, and belongs to the technical field of robotics.The biped robot complex terrain self-adaptive gait planning method comprises the steps that a fractional order PID controller is constructed, and the fractional order PID controller comprises a multi-sensor network module, a self-adaptive module and a fractional order calculus control module; constructing a reinforcement learning model for biped robot gait planning, setting a state, an action, a decision and a reinforcement learning reward function of reinforcement learning, performing continuous iteration and optimization adjustment on the reinforcement learning model by using a strategy gradient method, updating strategy parameters, and outputting control parameters of the biped robot; and performing decision fusion on control parameters output by the fractional order PID controller and the reinforcement learning model through a comprehensive loss function to obtain globally optimal biped robot control parameters. According to the method, more accurate gait planning can be provided for the biped robot in complex terrains, and better walking flexibility and adaptability are achieved.

The invention discloses a humanoid robot motion control method based on gait planning and reinforcement learning, and belongs to the technical field of humanoid robot motion control. According to the method, the problems of low convergence speed in a training process and poor stability and reliability of a trained model of an existing reinforcement learning method are solved. According to the method, a reinforcement learning model runs in parallel under multiple environment instances, in the running process, a target foothold is generated in real time based on feedback state information, the generated reference foothold and a mass center track serve as an action reference or reward target of a controller, and a continuous and dynamically-adjusted foot end track is constructed in combination with gait phase information. And after the reinforcement learning model is trained by utilizing all the collected trajectory data, motion control can be performed on the humanoid robot through the trained reinforcement learning model, so that the robot realizes stable walking similar to human beings under the condition of not depending on a predefined template. The method can be applied to motion control of the humanoid robot.

The invention provides a robot gait planning and reinforcement learning motion control method and system, and the method comprises the following steps: employing a Bezier curve to plan the walking path of a robot, and obtaining an initial target foothold; a gait database is constructed, and a sensor is adopted to collect real-time motion data of the robot; training data in the gait database based on a machine learning algorithm to obtain a gait prediction model, and predicting whether a left leg or a right leg is used as an ideal supporting leg in the next step; whether the current leg is an ideal supporting leg or not is judged, if yes, the leg lifting action is executed, if not, the leg falling action is executed, the robot adopts a reinforcement learning algorithm to construct a step posture adjustment model in the process of executing the leg lifting and leg falling actions, and the walking action of the robot is optimized through iterative training and action strategy adjustment. Through combination of gait prediction and reinforcement learning algorithms, accurate control and dynamic adjustment of walking actions of the robot are realized, and walking stability and environment adaptability of the robot are improved.

The invention provides an external force interference resisting gait control method of a hydraulic quadruped robot. The external force interference resisting gait control method comprises the steps that firstly, a stable walking controller of the quadruped robot is built; comprising an impedance controller of a swing phase and a virtual force controller of a support phase; secondly, when the quadruped robot walks, the foot end contact force of the robot is processed through an impedance controller in the swing phase, the position compensation amount is output, the position output amount of a single position control closed loop is corrected through the compensation amount, and impedance closed loop control is conducted; the supporting phase adopts a virtual force control method through the virtual force controller, and the posture of the quadruped robot is adjusted from crossing gait planning and posture angle adjustment so as to be switched to a normal gait. Thirdly, the swing phase is controlled in a closed-loop mode according to impedance, and the supporting phase conducts stable walking of the robot according to the adjusted normal gaits. The stability of the hydraulic quadruped robot when the hydraulic quadruped robot is disturbed by the outside is improved, and the risks of rollover and toppling are reduced; the energy loss is reduced by reducing the impact force at the foot end and optimizing the gait.

The invention discloses a robot dog intelligent gait planning method based on deep learning, which comprises a gait acquisition module, a scene acquisition module, a virtual human positioning module, a path planning module, an execution control module and a deep learning module, the virtual human positioning module is used for determining and controlling the position of a virtual human in a three-dimensional scene, and the scene acquisition module is used for acquiring three-dimensional model information between the virtual human and a target location. The gait collection system can collect the motion state of a real organism in real time so as to convert and plan the motion posture suitable for walking of the robot dog, and continuous correction learning can be carried out according to continuously collected different data through the deep learning system, so that the walking flexibility of the robot dog is improved.

The invention relates to the technical field of robot control, in particular to a robot stable gait planning method based on an arc trajectory, and the method comprises the following steps: selecting trajectory points which can form a triangle in a robot motion space; calculating a corresponding circular arc track center and a circular arc track radius according to the track points; constructing an arc track plane equation according to the track point, the arc track center and the arc track radius; dividing the arc track passing through the track points into a plurality of segments to obtain a plurality of sampling points; calculating coordinates of each sampling point in the motion space of the robot, and substituting the coordinates into the arc track plane equation for inverse motion solution to obtain motion angles of the robot at the sampling points; and obtaining a gait planning result of the robot based on the motion angles corresponding to all the sampling points. According to the invention, the motion track of the robot is predictable, so that the control stability of the robot is ensured.

The invention provides a biped robot gait planning method and device for a step terrain, and belongs to the technical field of robot gait planning, the method comprises the following steps: based on a linear inverted pendulum model, respectively establishing walking modes of an upper step and a lower step of a biped robot; based on a walking mode, mapping the linear inverted pendulum model under the step terrain into a segmented virtual height linear inverted pendulum model under the plane terrain; based on a segmented virtual height linear inverted pendulum model, gait planning of the biped robot under the stepped terrain is achieved through model prediction control. According to the biped robot gait planning method and device for the step terrain, the linear inverted pendulum model with the virtual centroid height is used, the gait mode is established according to the terrain elevation information and the landing point information of the step environment, and the method and device can adapt to the irregular step terrain with the variable height and the variable length; and the gait has good terrain adaptability.

The invention relates to the field of kinematics of four-footed crawling robots, and discloses a four-footed crawling robot kinematics analysis and gait planning method based on foot end tracks. According to the method, firstly, modeling is conducted on the quadruped crawling robot, then the motion paths of the foot ends of the quadruped crawling robot are designed, inverse kinematics analysis is conducted on the quadruped crawling robot through an analytical method, the angle value of each joint is solved, and then each joint motor is controlled to reach the expected position. Then, the Walk gait and the Trot gait of the four-footed crawling robot are designed, and finally movement tracks of foot ends of the Walk gait and the Trot gait of the four-footed crawling robot are output. The method has the advantages that the analytical method is high in speed and precision, and the method is suitable for leg structure analysis of the four-footed crawling robot. Technical support is provided for autonomous decision making and path planning, and possibility is provided for application of the four-footed crawling robot in various tasks.

The invention belongs to the related technical field of biped robot gait planning, and discloses a method, application and equipment for generating a biped robot anthropomorphic foot end trajectory based on a full-connection network, and the method comprises the steps: (1) grouping the collected foot end trajectory data according to a starting step, an intermediate circulation step and a termination step; (2) mapping each group of foot end track data into foot end track data which can be used by a robot based on a kinematics mapping relation between a human body and the robot, and carrying out conformal interpolation; (3) training a full-connection neural network model by adopting the interpolated foot end trajectory data, further generating a foot end trajectory of each intermediate step by adopting the full-connection neural network model, and mapping the directly generated foot end trajectories of the starting step and the ending step to jointly form an anthropomorphic foot end trajectory; and the main loss of back propagation of the full-connection neural network model is punished by introducing the trajectory smoothness and the prediction precision of the start end and the tail end. According to the method, the anthropomorphism of the foot end track is improved.

The invention provides a truss crawling gait planning method for a modular robot, and aims to solve the problem of efficient and stable movement of the modular robot in a complex truss structure environment. Analytical design is carried out according to the structure of a modular unit, and modeling is carried out on the modular robot; researching a truss structure, and designing a method to process a truss map; semicircular motion and linear motion trails are researched, and the motion trail of the tail end of the modular robot is designed; solving an angle value of each joint of the modular robot by using an analytical method; and designing four gaits of an opposite side crossing gait, an adjacent side transverse climbing gait, a gait of rotating around a rod piece and a gait of inchworm along the rod piece, and selecting strategies for planning the stable crawling gait of the truss structure of the modular robot. The method adapts to a non-fixed structure and different truss structures of the modular robot, the accurate joint angle value can be rapidly obtained, the stability of truss crawling of the modular robot is improved, and the method is suitable for aerospace, building maintenance and other scenes.

The invention relates to the technical field of specialized robots, in particular to a method for detecting the inner wall of a small-diameter pipeline of a bionic robot snake based on a visual system, and the method comprises the following steps: 1, designing the structure of the bionic robot snake; 2, designing an intelligent gait planning algorithm, and performing multi-modal adaptive motion control; 3, designing a multi-modal detection system, wherein the system integrates vision, thermal imaging and inertial navigation; and 4, on the basis of the bionic robot snake, an intelligent gait planning algorithm and a multi-modal detection system, detecting the complex working condition of the inner wall of the small-diameter pipeline. According to the invention, the inner wall of the small-diameter pipeline can be well detected.

The invention discloses a small robot hierarchical reinforcement learning gait adaptive generation method and system, and the method comprises the steps: collecting environment perception data and robot body state data through a perception module, and extracting environment features and robot state information; generating a gait plan instruction through a high-level planning module based on the environment characteristics and the robot state information; based on the gait plan instruction and the robot state information, a joint control instruction is generated through a low-layer control module in combination with a multi-body dynamics model; the joint control instruction is executed, and robot joints are controlled to move so as to achieve self-adaptive gait generation. By utilizing the embodiment of the invention, the problem of self-adaptive generation of gaits in a complex environment can be solved, and the motion intelligence, the control accuracy and the quick adaptability to unknown terrains of the robot are improved.

The invention relates to the field of robot control, and particularly discloses a PID control and gait planning method of a wheel-foot composite structure and electronic equipment, and the method comprises the steps: building a PID control model with a coupling compensation item; angle errors of all joints are calculated, motion interference of adjacent joints is compensated according to a PID control model with a coupling compensation item, in a wheel type mode, an outer ring takes a second threshold value as a target to control the speed, an inner ring corrects the posture according to IMU feedback, and the posture control weight is enhanced when the inclination angle exceeds a third threshold value; in the foot mode, PID parameters are adjusted in advance according to laser radar prediction, and the complex terrain adaptability is improved; terrain, gait, control precision and energy consumption data are collected in real time, PID parameters are automatically adjusted based on a PPO algorithm, and the control precision of the robot in the unknown terrain is improved; according to the invention, through a dynamic coupling compensation mechanism, the motion interference between the joints is accurately solved, so that the joints keep high coordination in motion, and the joints can stably keep accurate motion postures no matter what complex terrains face.

The invention relates to a biped robot gait planning method. The invention relates to the technical field of robot gait planning, and the method comprises the steps: taking a kinetic model of a robot as input, so as to achieve the neural network reasoning of simulation; describing the robot by using a URDF file, and enabling the robot to be equivalent to a kinematic chain formed by connecting rigid bodies in series; through accurate description, a robot consistent with that in reality is established in a simulation environment, the robot is enabled to carry out gait learning in a simulation physical environment, and supervised random divergence trial and error are carried out in a mode of setting initial conditions, termination conditions and reward and punishment conditions so as to realize gait learning. According to the invention, through repeated modification of parameters and structural adjustment of adaptability, a stable, low-energy-consumption and high-robustness control strategy is finally obtained through training.

The application discloses a kind of small celestial body weak gravity condition under detection robot movement control method and device, the method of the present application includes first to detection robot is carried out steady gait planning, at the end time of the foot end trajectory obtained, add the active attachment bias Δr for ensuring that detection robot is attached to the clamping of small celestial body star table;Then the foot end trajectory is as leg desired position, inverse kinematics is solved to obtain the desired joint angle of each joint, obtains the current joint angle of each joint of detection robot, and calculates the deviation between the current joint angle and the desired joint angle of each joint, and the deviation is input into the movement controller to obtain the joint output control torque, the corresponding joint is controlled by joint output control torque, until the deviation is less than set value.The present application can realize the stable attachment and movement of detection robot on the surface of small celestial body under the condition of weak gravity of small celestial body, create prerequisite for the smooth development of detection task.

The application discloses a lower limb rehabilitation training exoskeleton control method, an electronic device and a storage medium. The method comprises the following steps: collecting standard gait information in the process of human walking; generating a hip joint angle change curve and a knee joint angle change curve according to the standard gait information; performing smoothing processing on the hip / knee joint angle change curve; generating a variable step frequency step length hip joint curve according to the hip joint angle change curve and a hip joint amplitude parameter and a step frequency parameter; generating a variable step frequency step length knee joint curve according to the knee joint angle change curve and a knee joint amplitude parameter and a step frequency parameter; and driving a hip / knee joint motor according to the variable step frequency step length hip / knee joint curve, and driving a human lower limb through an exoskeleton. The method is based on normal motion data of the human body for gait planning, is closer to the human motion mode, has strong adaptability, requires a small amount of data, has strong practicability, and solves the problem of speed mutation caused by non-smooth adjacent period curves.

This invention discloses a hierarchical reinforcement learning method and system for adaptive gait generation in small robots. The method includes: collecting environmental perception data and robot body state data through a perception module, and extracting environmental features and robot state information; generating gait planning instructions through a high-level planning module based on the environmental features and robot state information; generating joint control instructions through a low-level control module combined with a multibody dynamics model based on the gait planning instructions and robot state information; and executing the joint control instructions to control the robot joint movement to achieve adaptive gait generation. Using this invention, the problem of adaptive gait generation in complex environments can be solved, improving the robot's motion intelligence, control accuracy, and rapid adaptation to unknown terrain.

The application relates to a complex terrain adaptive gait planning method for a biped robot and the biped robot, and belongs to the technical field of robots, and comprises the following steps: a fractional order PID controller is constructed, the fractional order PID controller comprises a multi-sensor network module, an adaptive module and a fractional order calculus control module; a reinforcement learning model for gait planning of the biped robot is constructed, a state, an action, a decision and a reinforcement learning reward function of the reinforcement learning are set, a policy gradient method is used to continuously iterate and optimally adjust the reinforcement learning model, policy parameters are updated, and control parameters of the biped robot are output; and the control parameters output by the fractional order PID controller and the reinforcement learning model are decisionally fused through a comprehensive loss function, so that globally optimal control parameters of the biped robot are obtained. The application can provide more accurate gait planning for the biped robot in complex terrain, and better walking flexibility and adaptability are achieved.

The application relates to the technical field of robot simulation, in particular to a robot ground verification platform and a robot vibration simulation method. The robot ground verification platform is used for verifying the vibration response of a space robot walking on a flexible structure and performing gait planning, and comprises a platform main body, a flexible structure beam, a fixed base, an electromagnet, an acceleration sensor, a simulated floating wheel and a robot. The flexible structure beam is arranged on the platform main body through the fixed base. The acceleration sensor and the electromagnet are arranged on the flexible structure beam. The robot moves on the platform main body through the simulated floating wheel. The robot vibration simulation method is that the robot moves on the flexible structure beam through the on-off state of the electromagnet, and the vibration of the flexible structure beam is detected through the acceleration sensor. The application can simulate the action of the robot in a space state, can monitor the vibration of the flexible structure beam in real time, so that the vibration parameters of the robot can be obtained, and the application of the experiment is facilitated.

The invention provides a wheel step type rotary drilling rig intelligent gait planning and cooperative control method and system, and the method comprises the steps: collecting the torque value of a drill rod, the rotation direction of the drill rod, the contact force deflection angle of each supporting leg steel claw, the leg vibration frequency, and the feeding speed and rotation speed of drilling operation; fourier transform is carried out on the torque value of the drill rod to obtain the torque fluctuation frequency of the drill rod; evaluating a phase angle between the rotation direction of the drill rod and the circumferential layout of the supporting legs, screening out leg positions with the phase angle exceeding a preset safety angle threshold value and vibration exceeding a limit, and adjusting the unfolding radius; identifying load concentration orientation distribution of each leg by taking the expansion radius adjustment amount of the supporting leg as an independent variable and the steel claw contact force deflection angle change rate as a dependent variable; gait scheduling arrangement, feeding speed and rotation speed are fused and optimized, a switching time window is divided according to the leg lifting and falling action period of each supporting leg, and feeding propulsion and rotation start-stop takt is matched in an alternating gap of adjacent supporting legs to form a cooperative control signal.

The application relates to an exoskeleton robot gait planning method, device, system and storage medium. The method comprises the following steps: acquiring angle data of a human body wearing an exoskeleton robot in a current walking cycle; the angle data is output by a motor encoder at a joint position of the exoskeleton robot; performing time domain to frequency domain conversion on the angle data to obtain frequency domain data; determining a gait planning function based on a gait fitting function, gait fitting parameters and the frequency domain data, and outputting the gait planning function; and the gait planning function is used for gait planning of the exoskeleton robot. The method can effectively improve the matching degree of the motion trajectory of the exoskeleton robot and the motion trajectory of the human body.

The invention discloses a safety constraint optimization gait planning method and device for a lower limb exoskeleton rehabilitation robot, and the method comprises the steps: designing an ACPG (adaptive central pattern generator) network topology for sagittal plane activity space limitation, and achieving the generation of a personalized gait track with a biological rhythm under human-computer interaction; on the basis of interaction torque and joint speed fed back by sensing, energy of human body active movement injected into the exoskeleton is calculated; constructing an intermediate variable according to the energy weighting, designing a gait adjustment model in combination with an admittance model, and outputting a self-adaptive smooth gait frequency and an amplitude modulation factor; based on the gait adjustment model, by using a control barrier function optimization theory, gait frequency and amplitude change are constrained within a safety range, so that the ACPG outputs rhythm gaits and meets safety requirements at the same time. According to the method, the energy change under the human-computer interaction effect is considered, and the personalized gait track with safety protection is generated.

The present invention discloses a structural design, paddling gait planning and testing device of a bionic canine amphibious robot, belonging to the technical field of amphibious robots. The present invention uses the principle of bionics to allow the robot to imitate the gait of mammals in nature to achieve faster and more efficient movement in a water environment. The proposal of this gait is more scientific and reasonable, and does not require additional propulsion devices or deformation devices to achieve movement in land and water environments. The mechanical structure is simple and some sandwich waterproof structures are added. The present invention can directly obtain the numerical value of the force applied to the object under test during underwater movement, can measure the numerical value of the force in two opposite directions, and can measure the force in other directions by only changing the connection direction of the aluminum profile. The degree of integration is good. When the gait of the robot during underwater movement is changed, the force measuring device can still accurately measure the force applied, and has good adaptability.

The application relates to the field of robot control, and particularly discloses a PID control and gait planning method of a wheel-foot composite structure and electronic equipment, which comprises the following steps: a PID control model with a coupling compensation term is established; each joint angle error is calculated; adjacent joint motion interference is compensated according to the PID control model with the coupling compensation term; in the wheel mode, an outer ring controls speed with the second threshold value as the target, and an inner ring corrects the posture according to IMU feedback; when the inclination exceeds the third threshold value, the posture control weight is enhanced; in the foot mode, PID parameters are adjusted in advance according to laser radar prediction, and the adaptability to complex terrains is improved; terrain, gait, control precision and energy consumption data are collected in real time, PID parameters are automatically adjusted based on a PPO algorithm, and the control precision of the robot in unknown terrains is improved; and through a dynamic coupling compensation mechanism, the motion interference between joints is accurately resolved, each joint can keep high coordination in motion, and accurate motion postures can be stably maintained regardless of complex terrains.

The present invention discloses a gait and trajectory planning method for a multi-legged robot based on a hierarchical mutual cooperation strategy, which solves the problem that there are still challenges in the overall algorithm control of existing legged robots. The key technical solution is to divide the control of the robot into a lower-level controller responsible for local gait planning and an upper-level controller for global trajectory planning. For the lower-level controller, a non-linear optimization method is used. According to the local terrain and body information of the robot's current position, a non-linear optimization problem is established to search for the trajectory parameters of the tip of each leg. Through the inverse solution of the obtained tip trajectory of each leg, the control signal of the robot's motor is obtained. For the upper-level controller, a supervised learning method is used to learn the capabilities of the lower-level controller from the motion information samples, and a model-based reinforcement learning algorithm is used for global trajectory planning. The gait and trajectory planning method for a multi-legged robot based on a hierarchical mutual cooperation strategy of the present invention has universality, simple ideas, and is more convenient and effective.

The invention provides an underwater hexapod robot multi-wall surface continuous motion control method based on reinforcement learning, and the method comprises the steps: obtaining a forward distance from the mass center of an underwater hexapod robot to a to-be-climbed wall surface through distance measurement equipment, and obtaining a distance from the mass center of the underwater hexapod robot to the current wall surface of the underwater hexapod robot through the distance measurement equipment; the method comprises the following steps: setting a state space, an action space and a reward function for stable crossing and constant-speed directional climbing tasks by analyzing the constant-speed directional climbing and wall surface crossing tasks; a deep deterministic strategy gradient reinforcement learning method is utilized to train a gait of stably crossing between wall surfaces with different dip angles. According to the method, the problems that a traditional gait planning method is complex in modeling and unstable in gait switching are solved, the underwater hexapod robot can cross over wall faces with different dip angles and conduct directional and constant-speed climbing along the wall faces with the different dip angles, meanwhile, the thruster thrust adaptive to the torque of the blade point leg can be solved, and the aim that the robot can climb on the wall faces with the different dip angles is achieved. And the motion stability of the underwater hexapod robot is ensured.

The invention relates to a self-adaptive gait planning method and system based on whole body motion collaborative optimization, and belongs to the field of robot control. The method comprises the following steps: obtaining virtual terrain impedance through multi-modal sensing fusion; dynamic CPG modulation is carried out based on the virtual terrain impedance, an adaptive gait rhythm is obtained, a basic gait mode is generated, and the adaptive gait rhythm comprises a leg phase, gait amplitude and gait frequency; the basic gait mode is corrected based on whole body movement collaborative optimization, and an ideal foothold and an expected mass center acceleration vector are obtained. According to the method, virtual terrain impedance perception, dynamic CPG modulation and whole-body motion collaborative optimization are fused, unified processing of terrain static attributes and dynamic disturbance is achieved, and the adaptive capacity and robustness of the robot in an unknown environment are remarkably improved.

The invention discloses a nerve-mechanics coupling software multi-mode motion control method based on a pre-training language model. Comprising a mass point-spring-based soft robot body structure module, a rhythm oscillation signal generation and parameter control module modulated based on a pre-training language model, a muscle driving and mechanical calculation module based on nerve-mechanical mapping, and a simulation and debugging module based on physical simulation. The method is oriented to gait planning and intelligent control of the bionic soft robot in an unstructured environment. In order to solve the problem that gait switching depends on artificial parameters and is difficult to quickly adjust along with instructions, a pre-training language model is introduced, and high-level semantic instructions are mapped into neural controller characteristic parameters. The robot can smoothly switch wriggling, steering and rolling behaviors under the working conditions of a hard plane, viscous fluid and the like through a natural language without remodeling, and a control and analysis method for effectively improving the autonomous environment adaptability of a software system is provided.

The application discloses a disturbance estimation and humanoid robot whole-body disturbance rejection control method based on DCM residual, and relates to the technical field of humanoid robot motion control. A disturbance observer based on DCM residual is designed, external force disturbance acting on the robot centroid can be estimated in real time without the need of a foot force sensor, and the disturbance observer has stronger robustness to model errors and sensor noise; secondly, a hierarchical online motion planner is proposed; a foothold planner optimizes the foothold position and gait cycle in real time through quadratic programming; a gait generator adopts a model predictive controller based on three-dimensional DCM, generates a robust centroid trajectory and gait according to the planned foothold; further, a whole-body disturbance rejection controller is developed, the estimated disturbance is embedded into the whole-body dynamics constraint, active compensation of the disturbance and direct control of the joint torque are realized. The application is used to solve the problems of insufficient robustness, low disturbance estimation accuracy and coupling of gait planning and whole-body control existing in the prior art.

The present invention discloses a method for controlling the motion of a quadruped robot in a complex environment based on a pulse neural network. First, a single-leg model of the quadruped robot and a conversion model of its leg joint angle state and foot end position coordinates are constructed. Subsequently, a hybrid neural network is constructed and trained. A terrain classification result is obtained using the trained hybrid neural network. Finally, the quadruped robot gait planning and foot end trajectory planning are completed based on the terrain classification result. The leg joint angle is output from the foot end target position to complete the motion control of the quadruped robot. The technical solution of the present invention utilizes image recognition based on a pulse neural network to classify the robot's environment. The recognition accuracy is high and the hardware requirements are low. In addition, appropriate gait planning and motion control decisions are made for the quadruped robot according to different environment types, so that the quadruped robot has stronger adaptability and passability in complex terrains with different characteristics, and has certain promotion value.