Efficient and energy-saving robot light material device

Abstract

The invention discloses an efficient and energy-saving robot light material device, and particularly relates to the technical field of control systems, the efficient and energy-saving robot light material device comprises a data acquisition module which comprises a gyroscope, an accelerometer, an IMU (Inertial Measurement Unit) and a pressure sensor and is used for acquiring robot real-time motion data including acceleration, angular velocity, attitude angle and ground pressure; the data sampling frequency is set to be 50 Hz, synchronous work of the sensors is ensured, the data source is reliable, and the precision meets the control requirement. The modeling module performs modeling on mechanical behaviors of the robot through a Lagrange equation, calculates dynamic response of the robot in actual operation by using a numerical integration method, monitors the motion state of the robot in real time, detects external disturbance in real time, and performs disturbance compensation on the robot through a disturbance compensation algorithm. Compensation torque is calculated based on external force input and a feedback mechanism of an internal distributed control system, so that the stability of the robot is kept.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and in particular to a high-efficiency and energy-saving robot lightweight material device. Background Art

[0002] The use of lightweight materials has become a key trend in the development of modern robotics. Lightweighting not only effectively reduces a robot's weight and improves its efficiency, but also extends battery life, reduces energy consumption, and enhances its overall performance. However, the use of lightweight materials presents a series of new challenges in robot design, particularly instability during motion and actuation, which significantly impacts the robot's precise operation and efficient task execution.

[0003] Lightweight materials, typically including aluminum alloys, titanium alloys, carbon fiber composites, and other advanced polymer and metal-based composites, are widely used in aerospace, automotive, electronics, robotics, and other fields due to their high strength-to-weight ratio and excellent mechanical properties. In robotic design, the introduction of lightweight materials can significantly reduce the overall weight of robotic arms or mobile robots, enabling robots to respond quickly to commands, reduce energy consumption, and thus improve work efficiency. For example, in application scenarios such as remote operation and working in hazardous environments, lightweight materials can help robots complete complex tasks with greater flexibility and maneuverability.

[0004] However, despite the many advantages of lightweight materials, their application also raises some instability issues, especially during motion and actuation. Lightweight materials generally have lower rigidity and poor anti-torsion properties, which makes the robot prone to vibration or posture deviation when moving at high speed or when disturbed by external forces. For example, when the robot performs complex operations, the lightweight robotic arm may bear a large load or encounter a large external impact force, resulting in an unstable motion trajectory and affecting the working accuracy. At the same time, due to the low elastic modulus of these materials, the robot may become unbalanced when accelerating, decelerating or changing direction, which may lead to unnecessary vibration or loss of control. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a high-efficiency and energy-saving robot lightweight material device.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A highly efficient and energy-saving robot lightweight material device, comprising: Data acquisition module, which includes a gyroscope, accelerometer, IMU, and pressure sensor to collect real-time motion data of the robot, including acceleration, angular velocity, attitude angle, and ground pressure. The data sampling frequency is set to 50Hz; A data calculation module, comprising a Kalman filter and an adaptive PID controller. The Kalman filter fuses multi-sensor data, recursively estimates a state based on the current state and observed data according to prediction and update steps, updates the signal weight coefficient by calculating an error covariance matrix, performs denoising, and outputs high-precision attitude and position data. The attitude data output by the Kalman filter is compared with the target trajectory, the attitude error is calculated, and the error is adjusted in real time using the adaptive PID controller. A model building module is used to model the mechanical behavior of the robot using the Lagrange equation, calculate the dynamic response of the robot in actual operation using a numerical integration method, and monitor the motion state of the robot in real time; The motion adjustment module detects external disturbances in real time during the robot's motion and calculates compensation torque based on external force input and the feedback mechanism of the internal distributed control system through a disturbance compensation algorithm.

[0007] As a preferred embodiment, the prediction algorithm in the Kalman filter data fusion is: ; in : No. The predicted state value at the moment, : state transition moment, : No. The estimated state value at time t, : control input matrix, : control input; The update algorithm in the Kalman filter data fusion is: ; in: : prediction covariance matrix, : observation matrix, : observation noise covariance matrix; : Kalman gain.

[0008] As a preferred embodiment, the output algorithm of the PID controller is: ; in: Control output, :error; , , : PID gain; : Integral term of error; : Integral term of error.

[0009] As a preferred embodiment, it also includes: Power output module: The power output module includes: a high-precision brushless motor, an active vibration control system, an environmental perception sensor and an algorithm processing perception transmission module.

[0010] Optimized path planning module: Dynamically generates an obstacle avoidance path based on the robot's current position, target position, and environmental information. Taking into account the smoothness, shortest distance, and time consumption of the path, the module determines the appropriate path by calculating the optimal cost function between nodes. Through the feedback control algorithm, the module obtains the current posture and speed data during the robot's movement, dynamically adjusts the path and speed, ensures that the robot moves along the predetermined trajectory, and promptly corrects path deviations.

[0011] As a preferred embodiment, the high-precision brushless motor collects motor speed, torque and current signals in real time, adjusts the motor output using a closed-loop feedback control algorithm, and maintains the stability of speed and torque. The active vibration control system uses an inertial sensor to monitor the robot's vibration signal in real time, combines the reverse control algorithm to generate a compensation signal in real time, uses the motor drive system to adjust the vibration phase, and ensures real-time vibration suppression through the control algorithm. The environmental perception sensor obtains ground height difference, obstacle position and dynamic change information of obstacles by scanning environmental data in real time, and transmits it to the control system for processing. The algorithm processing perception transmission module uses a deep learning algorithm to process perception sensor data, trains a neural network to identify obstacles, path changes, ground unevenness and other information, extracts feature data through a convolutional neural network, and calculates the relative position of the obstacle and the robot.

[0012] As a preferred embodiment, the optimized path planning module adjusts the input torque of the drive system for corresponding compensation, combines the mass distribution and motion state of the robot, calculates and evaluates the inertia torque of the robot in real time, and compares it with the external disturbance torque, and optimizes the speed and torque of each motor by adjusting the power output of the drive system.

[0013] As a preferred embodiment, the output of the neural network updates the weights via back propagation: ; in: : No. The weights in the iterations, : learning rate, : error function.

[0014] As a preferred embodiment, dynamic trajectory adjustment: ; in: : The adjusted trajectory, : expected trajectory, : Correction amount based on feedback.

[0015] As a preferred embodiment, the PID controller is regulated by adjusting the proportional, integral and differential gain coefficients to dynamically adjust the motor speed and driving torque to ensure accurate tracking of the control target.

[0016] The beneficial effects of the present invention are: The model module constructed in the present invention models the mechanical behavior of the robot through the Lagrange equation, uses the numerical integration method to calculate the dynamic response of the robot in actual operation, monitors the robot's motion state in real time, detects external disturbances in real time, and calculates the compensation torque based on the external force input and the feedback mechanism of the internal distributed control system through the disturbance compensation algorithm to maintain the stability of the robot.

[0017] The present invention dynamically generates an obstacle avoidance path based on the robot's current position, target position, and environmental information. It takes into account the smoothness, shortest distance, and time consumption of the path, and determines the appropriate path by calculating the optimal cost function between nodes. Through the feedback control algorithm, the current posture and speed data are obtained during the robot's movement, and the path and speed are dynamically adjusted to ensure that the robot moves along the predetermined trajectory and correct path deviations in a timely manner.

[0018] The present invention combines the robot's mass distribution and motion state to calculate and evaluate the robot's inertia torque in real time, and compares it with the external disturbance torque. By adjusting the power output of the drive system, the speed and torque of each motor are optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the system flow in the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] like Figure 1As shown, a high-efficiency and energy-saving robot lightweight material device includes: a data acquisition module, which includes a gyroscope, an accelerometer, an IMU, and a pressure sensor to collect real-time motion data of the robot, including acceleration, angular velocity, attitude angle, and ground pressure. The data sampling frequency is set to 50Hz, and it is ensured that each sensor works synchronously, and its data source is reliable and the accuracy meets the control requirements; A data calculation module, comprising a Kalman filter and an adaptive PID controller. The Kalman filter fuses multi-sensor data, recursively estimates state values ​​based on the current state and observed data in accordance with prediction and update steps, updates signal weight coefficients by calculating an error covariance matrix, dynamically adjusts motor speed and drive torque to ensure accurate tracking of the control target, performs denoising, and outputs high-precision attitude and position data. The attitude data output by the Kalman filter is compared with the target trajectory, the attitude error is calculated, and the error is adjusted in real time using the adaptive PID controller to ensure accurate tracking of the control target. A model building module is used to model the mechanical behavior of the robot using the Lagrange equation, calculate the dynamic response of the robot in actual operation using a numerical integration method, and monitor the motion state of the robot in real time; A motion adjustment module detects external disturbances in real time during robot motion and calculates compensation torque based on external force input and feedback from the internal distributed control system using a disturbance compensation algorithm to maintain robot stability. During robot motion, external disturbances (such as collisions, changes in thrust, and friction) can cause deviations or instability in the robot's trajectory. The Motion Adjustment Module effectively minimizes the impact of these disturbances on the robot by detecting these external disturbances in real time and compensating for them promptly. The compensation torque precisely calculates and adjusts the input torque of the robot's drive system, enabling the robot to quickly return to the desired trajectory and maintain stability.

[0022] Dynamic stability: When the robot moves in a complex environment, the disturbance compensation system can ensure that the robot responds to external disturbances in a timely manner, avoiding unnecessary vibrations or posture tilt, thereby improving the dynamic stability of the system.

[0023] Smooth motion: By adjusting the compensation torque in real time, a smooth transition of the robot's motion can be achieved, avoiding drastic changes caused by sudden disturbances and improving the stability of the overall motion.

[0024] External disturbances can cause deviations when a robot performs delicate tasks (such as grasping objects or moving), affecting the quality of the task. By compensating for these disturbances in real time, the Motion Adjustment Module significantly improves the robot's motion accuracy.

[0025] Position accuracy: Disturbance compensation can effectively avoid the position error of the robot caused by the influence of external forces, ensuring that the robot performs tasks according to the predetermined trajectory.

[0026] Speed ​​and acceleration control: When external disturbances cause changes in speed or acceleration, the compensation algorithm can quickly adjust the input torque of the drive system so that the robot operates stably within the expected speed and acceleration range, thereby improving motion control accuracy.

[0027] Power output module: The power output module includes: a high-precision brushless motor, an active vibration control system, an environmental perception sensor and an algorithm processing perception transmission module.

[0028] Optimized path planning module: Dynamically generates an obstacle avoidance path based on the robot's current position, target position, and environmental information. It considers the path's smoothness, minimum distance, and time consumption, and determines the appropriate path by calculating the optimal cost function between nodes. Through a feedback control algorithm, it obtains the robot's current posture and speed data during its movement, dynamically adjusts the path and speed, ensures the robot moves along the predetermined trajectory, and promptly corrects path deviations. The high-precision brushless motor collects motor speed, torque and current signals in real time, adjusts the motor output using a closed-loop feedback control algorithm, and maintains the stability of speed and torque. The active vibration control system uses an inertial sensor to monitor the robot's vibration signal in real time, combines it with an inverse control algorithm to generate a compensation signal in real time, uses the motor drive system to adjust the vibration phase, and ensures real-time vibration suppression through a control algorithm. The environmental perception sensor obtains ground height difference, obstacle position and dynamic change information of obstacles by scanning environmental data in real time, and transmits it to the control system for processing. The algorithm processing perception transmission module uses a deep learning algorithm to process perception sensor data, trains a neural network to identify obstacles, path changes, ground unevenness and other information, extracts feature data through a convolutional neural network, and calculates the relative position of the obstacle and the robot; The optimized path planning module adjusts the input torque of the drive system to make corresponding compensation. In combination with the robot's mass distribution and motion state, it calculates and evaluates the robot's inertia torque in real time and compares it with the external disturbance torque. By adjusting the power output of the drive system, the speed and torque of each motor are optimized.

[0029] The prediction algorithm in the Kalman filter data fusion: ; in : No. The state prediction value at the moment, : state transition moment, : No. The estimated state value at time t, : control input matrix, : control input; The update algorithm in the Kalman filter data fusion is: ; in: : prediction covariance matrix, : observation matrix, : observation noise covariance matrix; : Kalman gain.

[0030] The output algorithm of the PID controller is: ; in: Control output, :error; , , : PID gain; : Integral term of error; : Integral term of error.

[0031] The output of the neural network is passed through back propagation to update the weights: ; in: : No. The weights in the iterations, : learning rate, : error function.

[0032] Dynamic trajectory adjustment: ; in: : The adjusted trajectory, : expected trajectory, : Correction amount based on feedback.

[0033] In addition, a low center of gravity structure is designed. By calculating parameters such as the center of mass position and center of gravity height, the layout of key components (such as batteries, processors, drive units, etc.) is replanned. The finite element analysis method is used to simulate the impact of the robot's center of gravity position on motion stability to reduce the potential risk of the robot overturning.

[0034] Shock absorbers or rubber pads are installed on the robot's chassis and at the connections between its components. Appropriate elastic coefficients and damping coefficients are selected. The vibration propagation path under different ground conditions is calculated through numerical simulation methods. The shock absorption system is dynamically adjusted to reduce the impact of vibration on the robot's stability.

[0035] Shock absorbers are typically used to absorb large mechanical shocks. They have high damping capacity and can effectively reduce the propagation of large vibrations. Rubber pads, on the other hand, are more suitable for cushioning smaller vibrations, offering superior elasticity and flexibility. Choosing the appropriate spring and damping coefficients is crucial during design. The spring coefficient influences the shock absorber's recovery capacity, while the damping coefficient determines its ability to attenuate vibrations. Both must be optimally selected based on the robot's operating environment and operational characteristics.

[0036] To ensure that shock absorbers and rubber pads effectively reduce the impact of vibration on the robot's stability during motion, numerical simulation methods are essential for analyzing the vibration propagation path. Numerical simulation methods can help designers analyze vibration propagation and its impact on the robot under different floor conditions before actual construction. Common simulation methods include finite element analysis (FEA) and multibody dynamics (MBD).

[0037] Installing shock absorbers or rubber pads on the robot's chassis and at the joints between its components, and calculating vibration propagation paths through numerical simulation, are important methods for improving robot stability. By designing appropriate elastic and damping coefficients, combined with a dynamic adjustment system, the impact of vibration on the robot's motion stability can be significantly reduced. This not only ensures the robot's efficient execution in complex environments but also extends its service life, improving its reliability and performance.

[0038] Conduct multiple rounds of field tests based on different environmental conditions (such as slope, obstacle density, ground conditions, etc.), record the robot's posture, acceleration, torque and other data during movement, and calibrate and optimize the model based on actual operation data.

[0039] Based on the test results, the control algorithm and hardware structure are adjusted through a closed-loop optimization process. The backpropagation method is used to analyze the source of errors, accurately adjust the control parameters, analyze the response characteristics of the robot in various test scenarios, and improve the robot's stability and control accuracy through iterative improvements.

[0040] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A highly efficient and energy-saving robot lightweight material device, characterized in that :include: Data acquisition module, which includes a gyroscope, accelerometer, IMU, and pressure sensor to collect real-time motion data of the robot, including acceleration, angular velocity, attitude angle, and ground pressure. The data sampling frequency is set to 50Hz; A data calculation module, comprising a Kalman filter and an adaptive PID controller. The Kalman filter fuses multi-sensor data, recursively estimates a state based on the current state and observed data according to prediction and update steps, updates the signal weight coefficient by calculating an error covariance matrix, performs denoising, and outputs high-precision attitude and position data. The attitude data output by the Kalman filter is compared with the target trajectory, the attitude error is calculated, and the error is adjusted in real time using the adaptive PID controller. A model building module is used to model the mechanical behavior of the robot using the Lagrange equation, calculate the dynamic response of the robot in actual operation using a numerical integration method, and monitor the motion state of the robot in real time; The motion adjustment module detects external disturbances in real time during the robot's motion and calculates compensation torque based on external force input and the feedback mechanism of the internal distributed control system through a disturbance compensation algorithm.

2. The high-efficiency and energy-saving robot lightweight material device according to claim 1 is characterized in that :Prediction algorithm in the Kalman filter data fusion: ； in : No. The predicted state value at the moment, : state transition moment, : No. The estimated state value at time t, : control input matrix, : control input; The update algorithm in the Kalman filter data fusion is: ； in: : prediction covariance matrix, : observation matrix, : observation noise covariance matrix; : Kalman gain.

3. The energy-efficient robot lightweight material device according to claim 1, characterized in that The output algorithm of the PID controller is: ； in: Control output, :error; , , : PID gain; : Integral term of error; : Integral term of error.

4. The energy-efficient robot lightweight material device according to claim 1, characterized in that Also includes: Power output module: The power output module includes: a high-precision brushless motor, an active vibration control system, an environmental perception sensor, and an algorithm processing perception transmission module; Optimized path planning module: Dynamically generates an obstacle avoidance path based on the robot's current position, target position, and environmental information. Taking into account the smoothness, shortest distance, and time consumption of the path, the module determines the appropriate path by calculating the optimal cost function between nodes. Through the feedback control algorithm, the module obtains the current posture and speed data during the robot's movement, dynamically adjusts the path and speed, ensures that the robot moves along the predetermined trajectory, and promptly corrects path deviations.

5. The high-efficiency and energy-saving robot lightweight material device according to claim 4 is characterized in that : The high-precision brushless motor collects motor speed, torque and current signals in real time, adjusts the motor output using a closed-loop feedback control algorithm, and maintains the stability of speed and torque. The active vibration control system uses an inertial sensor to monitor the robot's vibration signal in real time, combines the reverse control algorithm to generate a compensation signal in real time, uses the motor drive system to adjust the vibration phase, and ensures real-time vibration suppression through the control algorithm. The environmental perception sensor obtains ground height difference, obstacle position and dynamic change information of obstacles by scanning environmental data in real time, and transmits it to the control system for processing. The algorithm processing perception transmission module uses a deep learning algorithm to process perception sensor data, trains a neural network to identify obstacles, path changes and ground unevenness information, extracts feature data through a convolutional neural network, and calculates the relative position of the obstacle and the robot.

6. The high-efficiency and energy-saving robot lightweight material device according to claim 4 is characterized in that The optimized path planning module adjusts the input torque of the drive system to make corresponding compensation. It calculates and evaluates the robot's inertia torque in real time based on the robot's mass distribution and motion state, and compares it with the external disturbance torque. It optimizes the speed and torque of each motor by adjusting the power output of the drive system.

7. The high-efficiency and energy-saving robot lightweight material device according to claim 5, characterized in that : The output of the neural network updates the weights through back propagation: ； in: : No. The weights in the iterations, : learning rate, : error function.

8. The high-efficiency and energy-saving robot lightweight material device according to claim 5, characterized in that :Dynamic trajectory adjustment: ； in: : The adjusted trajectory, : expected trajectory, : Correction amount based on feedback.

9. The high-efficiency and energy-saving robot lightweight material device according to claim 1, characterized in that PID controller adjustment specifically adjusts the proportional, integral, and differential gain coefficients to dynamically adjust the motor speed and drive torque to ensure accurate tracking of the control target.

Images