Quadruped robot grounding detection method, system and device and storable medium

Abstract

The invention discloses a quadruped robot ground touch detection method, system and device and a storable medium, through designing a generalized momentum disturbance observer, a quadruped robot can estimate self generalized momentum in real time, sensitively perceive external environment change, especially momentum change caused by external force, and improve the detection accuracy of the quadruped robot. According to the technical scheme, the robot can accurately judge the leg grounding condition without an additional sensor, the adaptability to complex terrains is improved, the walking strategy can be adjusted according to the motion state and the external environment regardless of flat or rugged terrains, the stability is kept, and by comparing the actual track difference with the expected track difference, the stability of the robot is improved. The robot can adjust walking postures and steps in time, it is ensured that the robot walks stably and efficiently and arrives at a destination quickly and accurately, in addition, falling or damage risks are reduced through accurate ground touching judgment, and maintenance cost and use risks are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of robot ground contact detection, and in particular to a quadruped robot ground contact detection method, system, device and storable medium. Background Art

[0002] Quadruped robots, as advanced technological products that imitate animal walking, have shown great application potential in many fields such as disaster relief, exploration, and logistics distribution. However, in order to effectively walk and work in these complex environments, quadruped robots need to have precise ground sensing capabilities, especially the accuracy of foot touchdown detection, which is directly related to the robot's work efficiency and safety.

[0003] Traditional ground contact detection methods, such as hydraulic quadruped robot ground contact detection, usually rely on direct detection by pressure sensors or contact sensors, or indirect detection by force sensors and displacement sensors. Although these methods are effective, they may encounter problems such as difficult sensor wiring and limited detection accuracy in some complex terrains or step terrains. Summary of the invention

[0004] In view of this, the present invention proposes a quadruped robot ground contact detection method, system, device and storable medium, which can effectively solve the defects of the prior art such as difficult sensor wiring and limited detection accuracy.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A method for detecting ground contact of a quadruped robot, comprising:

[0007] Establish a dynamic model of a quadruped robot;

[0008] Based on the dynamics model of the quadruped robot, a disturbance observer of generalized momentum is designed, wherein the disturbance observer of generalized momentum can estimate the generalized momentum of the quadruped robot in real time and can detect the change of the generalized momentum caused by external force;

[0009] Obtain the actual torque value and leg position information at the leg joints of the quadruped robot;

[0010] Based on the disturbance observer and dynamic model of the quadruped robot, the expected trajectory of the legs under given control input is calculated;

[0011] The actual observed joint torque and leg position information are converted into actual trajectory using discrete time integration formula, and the difference between the actual trajectory and the expected trajectory is compared;

[0012] Whether the legs touch the ground is detected based on the difference results.

[0013] As a further optional solution of the quadruped robot ground contact detection method, the discrete time integration formula is used to convert the actually observed joint torque and leg position information into the actual trajectory, which specifically includes:

[0014] Obtaining discrete time points of observed joint torque and leg position information;

[0015] For each discrete time point, the state update at that time point is calculated using the discrete time integration formula, and the state update at each discrete time point is accumulated;

[0016] Connect the state updates at discrete time points to form a continuous actual trajectory.

[0017] As a further optional solution of the quadruped robot ground contact detection method, the method also includes using a Kalman filter to fuse the leg state expectations based on the gait scheduler with the probability of the ground height model.

[0018] As a further optional solution of the quadruped robot ground contact detection method, the ground height model models the average vertical force of the foot at the start of contact according to a Gaussian normal distribution, and infers the probability of the foot contacting the ground based on this model.

[0019] As a further optional solution of the quadruped robot ground contact detection method, the ground height model models the average foot vertical force at the start of contact according to Gaussian normal distribution, and infers the probability of foot contact with the ground according to this model, specifically including:

[0020] Vertical force data is collected when the foot is in contact with the ground;

[0021] The collected data were calculated to calculate the average vertical foot force at the start of each contact;

[0022] The mean and standard deviation of the average vertical foot force were calculated;

[0023] Construct a Gaussian distribution function using the mean and standard deviation;

[0024] Determine exposure thresholds;

[0025] The probability that the vertical force of the foot is greater than or equal to the contact threshold is calculated using a Gaussian distribution function;

[0026] Based on the calculated contact probability, the probability of the foot contacting the ground can be inferred.

[0027] As a further optional solution of the quadruped robot ground contact detection method, the method further includes:

[0028] Modify the gait control action of the quadruped robot according to the result of the ground contact detection;

[0029] When the legs touch the ground, the control strategy of the legs is adjusted according to the time and position of the touchdown.

[0030] A quadruped robot ground contact detection system, comprising:

[0031] The dynamic model building module is used to build a dynamic model that describes the dynamic behavior of the quadruped robot in various motion states;

[0032] A generalized momentum disturbance observer module, designed based on the dynamic model, capable of estimating the generalized momentum of the quadruped robot in real time and detecting changes in the generalized momentum caused by external forces;

[0033] A data acquisition module is used to obtain the actual torque value and leg position information at the leg joints of the quadruped robot;

[0034] An expected trajectory calculation module calculates the expected trajectory result of the leg under a given control input according to the generalized momentum disturbance observer and the dynamics model;

[0035] The actual trajectory generation and comparison module uses the discrete time integration formula to convert the actually observed joint torque and leg position information into the actual trajectory, and compares the actual trajectory with the expected trajectory to detect whether the leg touches the ground;

[0036] The ground touchdown detection result output module is used to output the ground touchdown detection result.

[0037] As a further optional solution of the quadruped robot ground contact detection system, the system further includes:

[0038] A gait control action modification module is used to modify the gait control action of the quadruped robot according to the result of the ground contact detection;

[0039] The leg control strategy adjustment module is used to adjust the leg control strategy according to the time and position of the leg contact when the leg contact is detected.

[0040] A computing device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the steps of the above-mentioned quadruped robot ground contact detection method when executing the computer program.

[0041] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for detecting ground contact of a quadruped robot are implemented.

[0042] The beneficial effects of the present invention are as follows: by designing a disturbance observer of generalized momentum, the generalized momentum of the quadruped robot can be estimated in real time, which helps the robot to understand its own motion state more accurately. The disturbance observer can not only estimate the generalized momentum, but also detect the change of the generalized momentum caused by external forces (such as ground reaction force), which enables the robot to perceive the changes in the external environment more keenly, so as to more accurately judge whether the legs touch the ground without sensors. Since the technical solution can estimate the generalized momentum and detect the change of external forces in real time, the robot can better adapt to complex terrain. Whether it is flat ground or rugged mountain roads, the robot can adjust the walking strategy according to its own motion state and the change of the external environment, so as to maintain stability. By comparing the difference between the actual trajectory and the expected trajectory, the robot can timely adjust the walking posture and pace to ensure the smooth and efficient walking, which helps the robot to reach the destination more quickly and accurately when performing tasks. Since the robot can more accurately judge whether the legs touch the ground, it can reduce the occurrence of accidents such as falling or damage caused by misjudgment, which helps to reduce the maintenance cost and use risk of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of a flow chart of a method for detecting ground contact of a quadruped robot according to the present invention;

[0045] Figure 2 A schematic diagram of the composition of a quadruped robot ground contact detection system of the present invention;

[0046] Figure 3 The figure is a schematic diagram of the composition of a computing device according to the present invention. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] refer to Figures 1 to 3 , a quadruped robot touchdown detection method, comprising:

[0049] Establish a dynamic model of a quadruped robot;

[0050] Based on the dynamics model of the quadruped robot, a disturbance observer of generalized momentum is designed, wherein the disturbance observer of generalized momentum can estimate the generalized momentum of the quadruped robot in real time and can detect the change of the generalized momentum caused by external force;

[0051] Obtain the actual torque value and leg position information at the leg joints of the quadruped robot;

[0052] Based on the disturbance observer and dynamic model of the quadruped robot, the expected trajectory of the legs under given control input is calculated;

[0053] The actual observed joint torque and leg position information are converted into actual trajectory using discrete time integration formula, and the difference between the actual trajectory and the expected trajectory is compared;

[0054] Whether the legs touch the ground is detected based on the difference results.

[0055] In this embodiment, by designing a disturbance observer of generalized momentum, the generalized momentum of the quadruped robot can be estimated in real time, which helps the robot to understand its own motion state more accurately. The disturbance observer can not only estimate the generalized momentum, but also detect the change of the generalized momentum caused by external forces (such as ground reaction force), which enables the robot to perceive the changes in the external environment more keenly, so as to more accurately judge whether the legs touch the ground without sensors. Since the technical solution can estimate the generalized momentum and detect the change of external forces in real time, the robot can better adapt to complex terrain. Whether it is flat ground or rugged mountain roads, the robot can adjust the walking strategy according to its own motion state and the changes in the external environment to maintain stability. By comparing the difference between the actual trajectory and the expected trajectory, the robot can adjust the walking posture and pace in time to ensure smooth and efficient walking, which helps the robot to reach the destination more quickly and accurately when performing tasks. Since the robot can more accurately judge whether the legs touch the ground, it can reduce the occurrence of accidents such as falls or damage caused by misjudgment, which helps to reduce the maintenance cost and use risk of the robot.

[0056] Preferably, the discrete time integration formula is used to convert the actually observed joint torque and leg position information into the actual trajectory, specifically including:

[0057] Obtaining discrete time points of observed joint torque and leg position information;

[0058] For each discrete time point, the state update at that time point is calculated using the discrete time integration formula, and the state update at each discrete time point is accumulated;

[0059] Connect the state updates at discrete time points to form a continuous actual trajectory.

[0060] In this embodiment, by obtaining discrete time points for observing joint torque and leg position information, it is possible to ensure that the data is representative in time. These discrete time points are selected based on the sampling frequency of the system or specific requirements, so that key motion information can be captured. For each discrete time point, a discrete time integral formula is used to calculate the state update of the time point. This method can accurately calculate the state of the current time point based on the state information of the previous time point and the observed data of the current time point. By accumulating the state update of each discrete time point, the state change in the entire motion process can be gradually constructed. The state updates of discrete time points are connected to form a continuous actual trajectory. This continuous trajectory not only reflects the motion state of the robot at each discrete time point, but also makes the trajectory more continuous and smooth in time through interpolation or smoothing. Using the discrete time integral formula for state update can realize real-time processing of the observed data, which means that the robot can calculate and update immediately after receiving new observation data, thereby realizing real-time tracking and monitoring of the motion state. The discrete time integral formula is usually easier to implement and calculate than the continuous time integral formula. This is because the discrete time integral formula only needs to consider a limited number of discrete time points without integrating the entire time domain. This greatly reduces the amount of calculation and improves the calculation efficiency. The discrete time integral formula has a certain anti-interference ability for the noise and interference in the observation data. Since it only considers the data at discrete time points, it can filter out some high-frequency noise and random interference, thereby improving the reliability and stability of the trajectory. This technical solution can adapt to different types of observation data and motion states. Whether it is joint torque or leg position information, as long as they can be processed by the discrete time integral formula, they can be used to construct the actual trajectory, which makes the technical solution highly adaptable and flexible.

[0061] It should be noted that the specific steps are:

[0062] Select the integration method: According to the characteristics and requirements of the data, select the appropriate discrete time integration method, including forward Euler method, backward Euler method and trapezoidal method;

[0063] Apply the integration formula: For each time point, use the discrete time integration formula to calculate the state update at that time point. For example, when using the forward Euler method, the state update formula is: y(k) = y(k-1) + t*u(k-1), where y(k) represents the state at the current time point, y(k-1) represents the state at the previous time point, t represents the sampling period, and u(k-1) represents the input at the previous time point (here, some form of joint torque or leg position information);

[0064] Cumulative state updates: By continuously applying the integral formula, the state updates at each time point are accumulated to obtain the actual trajectory of the entire time series;

[0065] Trajectory reconstruction: Connect the state updates at discrete time points to form a continuous actual trajectory. The trajectory may need to be smoothed to eliminate fluctuations or jumps caused by discrete integration.

[0066] Preferably, the method further comprises using a Kalman filter to fuse the leg state expectations based on the gait scheduler with the probability of the ground height model.

[0067] In this embodiment, the Kalman filter can fuse the leg state expectations from the gait scheduler and the probability information of the ground height model. This fusion strategy utilizes data from two different sources to make the prediction results more comprehensive and accurate. The gait scheduler provides leg state expectations based on robot motion planning and gait strategy, while the ground height model provides the probability distribution of ground height based on environmental perception and terrain information. Through the iterative update process of the Kalman filter, the prediction of leg state can be continuously optimized. The filter continuously adjusts the weight and uncertainty of the prediction results based on new observation data and prior information, thereby obtaining a more accurate estimate of the leg state. After the introduction of the Kalman filter, the robot can better adapt to complex terrain. Whether it is flat ground or rugged mountain roads, the robot can flexibly adjust its walking strategy based on its own motion state, the expectations of the gait scheduler, and the probability information of the ground height model. Through accurate leg state prediction and terrain perception, the robot can more effectively plan walking paths and gait strategies, which helps to reduce unnecessary energy consumption and wear and improve walking efficiency and stability. The prediction results that integrate multiple information sources are usually more reliable than those from a single source. The Kalman filter improves the reliability of leg state prediction by integrating the expectations of the gait scheduler and the probability information of the ground height model.

[0068] It should be noted that the specific steps are:

[0069] Define the system state: The system state x may include parameters such as the position and velocity of the legs. These parameters are defined based on the expectations of the gait scheduler. For example, the state x may be a vector containing the position and velocity information of the legs in three-dimensional space.

[0070] Define observations: The observation z comes from the probability distribution of the ground height model, which can be expressed as the height information when the leg actually touches the ground. The observation z may contain noise, so a probability distribution is needed to describe its uncertainty.

[0071] Initialize Kalman filter parameters: including state transfer matrix F, observation matrix H, process noise variance matrix Q, observation noise variance matrix R, initial state vector x0 and initial error covariance matrix P0, the state transfer matrix F is used to describe how the system state is transferred from the previous moment to the current moment, the observation matrix H is used to describe how the system state is mapped to the observation space, the process noise variance matrix Q is used to describe the uncertainty in the state transfer process, the observation noise variance matrix R is used to describe the uncertainty in the observation process, the initial state vector x0 and the initial error covariance matrix P0 are set according to the initial conditions of the gait scheduler;

[0072] Prediction step: Use the state transfer matrix F and the state estimate x_(k-1) at the previous moment to predict the current state x_k_pred, and update the error covariance matrix P_k_pred to reflect the uncertainty of the prediction;

[0073] Update step: Calculate the Kalman gain K, which represents the degree of influence of the observation on the state estimate. Use the observation matrix H, observation z and prediction error covariance matrix P_k_pred to calculate K, update the state estimate x_k, take the weighted sum of the predicted value x_k_pred and the observed value z as the new state estimate, and update the error covariance matrix P_k to reflect the uncertainty of the updated state estimate;

[0074] Iterative Kalman filtering process: For each time step, the prediction and update steps are repeated. Over time, the Kalman filter will gradually converge to the true state of the system;

[0075] Fusion of the probability of the ground height model: In the update step, the observation z comes from the probability distribution of the ground height model. The Kalman filter fuses the probability of the ground height model with the expectations of the gait scheduler by calculating the Kalman gain K. The fusion result is reflected in the updated state estimate x_k and the error covariance matrix P_k.

[0076] Preferably, the ground height model models the average vertical force of the foot at the start of contact according to a Gaussian normal distribution, and infers the probability of the foot contacting the ground based on this model.

[0077] Preferably, the ground height model models the average vertical force of the foot at the start of contact according to Gaussian normal distribution, and infers the probability of the foot contacting the ground according to the model, specifically including:

[0078] Vertical force data is collected when the foot is in contact with the ground;

[0079] The collected data were calculated to calculate the average vertical foot force at the start of each contact;

[0080] The mean and standard deviation of the average vertical foot force were calculated;

[0081] Construct a Gaussian distribution function using the mean and standard deviation;

[0082] Determine exposure thresholds;

[0083] The probability that the vertical force of the foot is greater than or equal to the contact threshold is calculated using a Gaussian distribution function;

[0084] Based on the calculated contact probability, the probability of the foot contacting the ground can be inferred.

[0085] In this embodiment, when the foot contacts the ground, the vertical force data of the foot is collected in real time, and the vertical force data collected at the beginning of each contact is calculated to obtain the average foot vertical force, which helps to eliminate the randomness and uncertainty of a single measurement; the average foot vertical force of multiple contacts is statistically analyzed to calculate its mean and standard deviation, which are the core parameters of the Gaussian distribution function and determine the shape and position of the distribution. The calculated mean and standard deviation are used to construct a Gaussian distribution function, which can describe the probability distribution of the foot vertical force; according to actual needs, a reasonable contact threshold is determined, which is used to determine whether the foot is in contact with the ground. The Gaussian distribution function is used to calculate the probability that the foot vertical force is greater than or equal to the contact threshold. This probability value reflects the possibility of the foot in contact with the ground. According to the calculated contact probability, combined with other relevant information (such as the expectations of the gait scheduler, the ground height model, etc.), the probability of the foot in contact with the ground is comprehensively inferred.

[0086] It should be noted that through Gaussian distribution modeling and probability calculation, it is possible to more accurately determine whether the foot is in contact with the ground. This method takes into account the randomness and uncertainty of the vertical force and improves the accuracy of detection; the ground height model is combined with the Gaussian distribution function, so that the robot can better adapt to different terrains. Whether it is flat ground or rugged mountain roads, the robot can make judgments and adjustments based on the distribution of the vertical force of the foot; accurate contact detection helps the robot maintain a stable walking state. During walking, the robot can adjust its gait and posture in time according to the contact between the foot and the ground to avoid falling or damage; through precise contact detection, the robot can allocate energy and plan walking paths more reasonably, which helps to reduce energy consumption and improve walking efficiency.

[0087] Preferably, the method further comprises:

[0088] Modify the gait control action of the quadruped robot according to the result of the ground contact detection;

[0089] When the legs touch the ground, the control strategy of the legs is adjusted according to the time and position of the touchdown.

[0090] In this embodiment, through real-time ground contact detection, the robot can accurately determine when and where each leg touches the ground. This enables the robot to adjust its gait and posture in a timely manner according to the ground contact information, thereby maintaining a more stable walking state; based on the results of the ground contact detection, the robot can optimize its gait control actions. For example, if it detects that a certain leg touches the ground in advance, the robot can adjust the pace or strength of other legs to maintain the coordination and balance of the overall gait. This optimization helps to reduce energy consumption during walking and improve walking efficiency; the robot can better adapt to different terrains based on the information of ground contact detection. For rugged terrain, the robot can avoid falling or damage by adjusting the control strategy of the legs. In addition, for ground of different materials and hardness, the robot can also adjust the contact strength and gait pattern of the feet to maintain the best walking effect; during walking, the robot Various obstacles may be encountered. Through ground contact detection, the robot can sense the existence of obstacles in advance and take corresponding obstacle avoidance measures, which helps to reduce collisions with obstacles and protect the safety and integrity of the robot. This technical solution enables the robot to make intelligent decisions based on real-time ground contact detection information. This intelligence is not only reflected in gait control and obstacle avoidance capabilities, but also in the perception and adaptation of the environment. The robot can continuously learn and adjust its walking strategies to cope with different challenges and scenarios. For users of quadruped robots, stable walking and efficient gait control are one of the key factors to improve user experience. By implementing this technical solution, the robot can walk more intelligently and stably, thereby providing users with a better user experience.

[0091] A quadruped robot ground contact detection system, comprising:

[0092] The dynamic model building module is used to build a dynamic model that describes the dynamic behavior of the quadruped robot in various motion states;

[0093] A generalized momentum disturbance observer module, designed based on the dynamic model, capable of estimating the generalized momentum of the quadruped robot in real time and detecting changes in the generalized momentum caused by external forces;

[0094] A data acquisition module is used to obtain the actual torque value and leg position information at the leg joints of the quadruped robot;

[0095] An expected trajectory calculation module calculates the expected trajectory result of the leg under a given control input according to the generalized momentum disturbance observer and the dynamics model;

[0096] The actual trajectory generation and comparison module uses the discrete time integration formula to convert the actually observed joint torque and leg position information into the actual trajectory, and compares the actual trajectory with the expected trajectory to detect whether the leg touches the ground;

[0097] The ground touchdown detection result output module is used to output the ground touchdown detection result.

[0098] Preferably, the system further comprises:

[0099] A gait control action modification module is used to modify the gait control action of the quadruped robot according to the result of the ground contact detection;

[0100] The leg control strategy adjustment module is used to adjust the leg control strategy according to the time and position of the leg contact when the leg contact is detected.

[0101] A computing device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the steps of the above-mentioned quadruped robot ground contact detection method when executing the computer program.

[0102] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for detecting ground contact of a quadruped robot are implemented.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting a quadruped robot touching the ground, characterized in that: include: Establish a dynamic model of a quadruped robot; Based on the dynamics model of the quadruped robot, a disturbance observer of generalized momentum is designed, wherein the disturbance observer of generalized momentum can estimate the generalized momentum of the quadruped robot in real time and can detect the change of the generalized momentum caused by external force; Obtain the actual torque value and leg position information at the leg joints of the quadruped robot; Based on the disturbance observer and dynamic model of the quadruped robot, the expected trajectory of the legs under given control input is calculated; The actual observed joint torque and leg position information are converted into actual trajectory using discrete time integration formula, and the difference between the actual trajectory and the expected trajectory is compared; Whether the legs touch the ground is detected based on the difference results.

2. A method for detecting a quadruped robot touching the ground according to claim 1, characterized in that: The discrete time integration formula is used to convert the actually observed joint torque and leg position information into the actual trajectory, specifically including: Obtaining discrete time points of observed joint torque and leg position information; For each discrete time point, the state update at that time point is calculated using the discrete time integration formula, and the state update at each discrete time point is accumulated; Connect the state updates at discrete time points to form a continuous actual trajectory.

3. A method for detecting a quadruped robot touching the ground according to claim 2, characterized in that: The method also includes using a Kalman filter to fuse the leg state expectations based on the gait scheduler with the probability of the ground height model.

4. A method for detecting a quadruped robot touching the ground according to claim 3, characterized in that: The ground height model models the average vertical force of the foot at the onset of contact according to a Gaussian normal distribution and infers the probability of the foot contacting the ground based on this model.

5. A method for detecting a quadruped robot touching the ground according to claim 4, characterized in that: The ground height model models the average vertical force of the foot at the start of contact according to a Gaussian normal distribution, and infers the probability of the foot contacting the ground based on this model, specifically including: Vertical force data is collected when the foot is in contact with the ground; The collected data were calculated to calculate the average vertical foot force at the start of each contact; The mean and standard deviation of the average vertical foot force were calculated; Construct a Gaussian distribution function using the mean and standard deviation; Determine exposure thresholds; The probability that the vertical force of the foot is greater than or equal to the contact threshold is calculated using a Gaussian distribution function; Based on the calculated contact probability, the probability of the foot contacting the ground can be inferred.

6. A method for detecting a quadruped robot touching the ground according to claim 5, characterized in that: The method further comprises: Modify the gait control action of the quadruped robot according to the result of the ground contact detection; When the legs touch the ground, the control strategy of the legs is adjusted according to the time and position of the touchdown.

7. A quadruped robot ground contact detection system, characterized in that: include: The dynamic model building module is used to build a dynamic model that describes the dynamic behavior of the quadruped robot in various motion states; A generalized momentum disturbance observer module, designed based on the dynamic model, capable of estimating the generalized momentum of the quadruped robot in real time and detecting changes in the generalized momentum caused by external forces; A data acquisition module is used to obtain the actual torque value and leg position information at the leg joints of the quadruped robot; An expected trajectory calculation module calculates the expected trajectory result of the leg under a given control input according to the generalized momentum disturbance observer and the dynamics model; The actual trajectory generation and comparison module uses the discrete time integration formula to convert the actually observed joint torque and leg position information into the actual trajectory, and compares the actual trajectory with the expected trajectory to detect whether the leg touches the ground; The ground touchdown detection result output module is used to output the ground touchdown detection result.

8. A quadruped robot ground contact detection system according to claim 7, characterized in that: The system further comprises: A gait control action modification module is used to modify the gait control action of the quadruped robot according to the result of the ground contact detection; The leg control strategy adjustment module is used to adjust the leg control strategy according to the time and position of the leg contact when the leg contact is detected.

9. A computing device, characterized in that The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the quadruped robot ground contact detection method according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the quadruped robot ground contact detection method according to any one of claims 1 to 6.

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