An omnidirectional wheel chassis motion control system for robots

Abstract

This invention relates to the field of robot motion control technology, specifically disclosing an omnidirectional wheel chassis motion control system for robots. Based on a 4-wheel Mecanum wheel chassis architecture, the four Mecanum wheels are rectangularly distributed under the robot chassis, divided into two diagonal groups. The Mecanum wheel rollers in each group have opposite tilt directions, and each Mecanum wheel is equipped with an independent drive motor and reducer. Based on the motion characteristics of the 4-wheel Mecanum wheels, this invention constructs a precise kinematic model, uses Jacobian matrices to achieve forward and inverse kinematic solutions, and eliminates control deviations caused by assembly errors and wheel diameter errors through explicit mathematical formulas.

Description

Technical Field

[0001] This invention relates to the field of robot motion control technology, and in particular to an omnidirectional wheel chassis motion control system for robots. Background Technology

[0002] With the widespread application of robotics technology in industrial logistics, service sectors, and special operations, increasingly higher demands are being placed on the mobility, control precision, and stability of robot chassis. Mecanum wheels, a special wheel system capable of omnidirectional movement, decompose the motor's driving force into components in different directions through rollers tilted at 45° on the wheel body. Utilizing the coordinated operation of multiple wheels, robots can achieve translation, rotation in place, and composite movements along arbitrary trajectories within a plane, eliminating the need for traditional steering mechanisms. This significantly improves the robot's mobility in confined spaces and complex working conditions, and is therefore widely used in various omnidirectional mobile robot chassis.

[0003] Currently, motion control systems for robot chassis based on four Mecanum wheels generally suffer from the following technical defects: First, the motion control algorithms mostly adopt traditional kinematic modeling, failing to fully consider non-ideal factors such as Mecanum wheel roller wear, assembly deviations, and changes in ground friction. This results in significant deviations between the theoretical and actual motion trajectories, leading to insufficient control accuracy. Especially during high-speed motion or load changes, problems such as trajectory drift and motion stuttering are prone to occur. Second, existing systems mostly employ a single feedback control method, relying solely on the speed information fed back from the motor encoder for adjustment, and cannot perceive the chassis attitude in real time. Key parameters such as wheel-to-ground contact status are weak in anti-interference capability, and under complex working conditions such as uneven ground or collisions with obstacles, motion instability or even loss of control is likely to occur. Third, the drive motors of each Mecanum wheel are mostly independently controlled, lacking a coordinated linkage mechanism. When a single motor has a speed deviation or malfunction, it cannot be compensated and adjusted in time, resulting in a decrease in the coordination of chassis motion, or even the inability to achieve normal omnidirectional motion. Fourth, the existing control system has poor adaptability, making it difficult to flexibly adjust control parameters according to different loads and working conditions. It lacks versatility, and the debugging process is complicated, which is not conducive to mass production and widespread application.

[0004] For example, Chinese patent application CN105479433B discloses a Mecanum wheel omnidirectional mobile transport robot. Although it uses Mecanum wheels to achieve omnidirectional movement and sets up an independent suspension module to improve operational stability, the control logic of this invention mainly focuses on the coordinated control of the manipulator and the chassis. It does not design a high-precision coordinated control algorithm for the motion characteristics of the Mecanum wheel, nor does it set up a multi-source feedback fusion mechanism. The control accuracy and anti-interference ability under complex working conditions are still insufficient. Although the anti-interference control scheme related to the aforementioned Mecanum wheel omnidirectional mobile transport robot introduces reinforcement learning and multi-source data fusion, it is complex in structure and high in cost, making it difficult to adapt to the application requirements of small and medium-sized robot chassis. Therefore, in order to solve such problems, an omnidirectional wheel chassis motion control system for robots is proposed. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an omnidirectional wheel chassis motion control system for robots.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] An omnidirectional wheel chassis motion control system for robots is based on a 4-wheel Mecanum wheel chassis architecture. The 4 Mecanum wheels are rectangularly distributed under the robot chassis, divided into two diagonal groups. The Mecanum wheel rollers of each diagonal group have opposite tilt directions. Each Mecanum wheel is equipped with an independent drive motor and reducer. The control system is characterized by including: a sensing module, a main control module, a drive module, a cooperative control module, and a human-machine interaction module. The modules communicate bidirectionally via a CAN bus to form a closed-loop control.

[0008] The perception module is used to collect the robot chassis's motion state, attitude information, wheel-to-ground contact state, and external environmental parameters. It preprocesses the collected raw data before sending it to the main control module. The perception module includes an encoder, an IMU (Inertial Measurement Unit), pressure sensors, a laser rangefinder, and Hall effect sensors. The encoder is coaxially connected to each drive motor to collect motor speed and steering data. The IMU is mounted at the center of the chassis to collect the chassis's angular velocity, angular acceleration, linear acceleration, and attitude angles (pitch, roll, and yaw). Pressure sensors are mounted on the wheel frame or independent suspension module of each Mecanum wheel to collect wheel-to-ground contact pressure and determine the wheel-to-ground contact state. Laser rangefinders are mounted around the chassis to collect distances to surrounding obstacles to assist obstacle avoidance. Hall effect sensors are mounted at the drive motors to detect motor operating current and determine motor operating status.

[0009] Main control module: As the core of the control system, it receives motion commands from the human-machine interface module, combines them with real-time data from the sensing module, and generates target control commands for each Mecanum wheel drive motor through a preset kinematic model and control algorithm, and sends them to the drive module. Simultaneously, it receives motor operating status data from the drive module in real time, performs anomaly detection and fault diagnosis, and immediately triggers a protection mechanism if an anomaly is detected. The main control module uses an embedded microprocessor with a built-in kinematics calculation unit, data fusion unit, anomaly detection unit, and parameter storage unit. The data fusion unit uses a Kalman filter algorithm.

[0010] The drive module receives the target control commands sent by the main control module, converts them into drive signals (PWM signals) for the drive motors, and drives each Mecanum wheel drive motor to run at the target speed and direction. At the same time, it collects the actual operating data (speed, current, temperature) of the drive motors and feeds it back to the main control module and the cooperative control module. The drive module includes four independent motor drivers, each corresponding to a Mecanum wheel drive motor, and supports closed-loop speed control and current protection functions.

[0011] The collaborative control module is used to achieve coordinated control of the four Mecanum wheel drive motors. Based on the target control commands sent by the main control module and the wheel speed and attitude data fed back by the sensing module, the collaborative control algorithm dynamically adjusts the control parameters of each motor, compensates for the speed deviation and wheel-to-ground contact deviation of individual motors, and ensures the coordinated movement of the four Mecanum wheels to achieve precise omnidirectional movement of the robot. The collaborative control module has a built-in PID adjustment unit and deviation compensation unit, and the collaborative control algorithm also includes an adaptive parameter adjustment strategy.

[0012] Human-machine interaction module: Used to enable interaction between the user and the control system, including motion command input, control parameter setting, operation status display and fault alarm; The human-machine interaction module can be a touch screen, wireless remote control or host computer software, and supports switching between manual control and automatic control modes. In manual mode, the user can directly input motion commands such as translation and rotation, while in automatic mode, the motion trajectory can be preset to realize the robot's autonomous movement.

[0013] Kinematic model and solution formula

[0014] The kinematic model in the main control module is constructed based on the motion characteristics of four Mecanum wheels. The forward and inverse kinematic solutions are achieved using the Jacobian matrix. Precise mathematical modeling eliminates control deviations caused by assembly errors and wheel diameter errors, as detailed below:

[0015] Coordinate system definition: A two-dimensional rectangular coordinate system O-XY is established with the geometric center of the robot chassis as the origin O. The X-axis is the forward direction of the robot, the Y-axis is the lateral direction of the robot, the Z-axis is perpendicular to the chassis plane (upward is positive), and θ is the robot's heading angle (rotation angle around the Z-axis, clockwise is positive).

[0016] Inverse kinematics: Based on the user-inputted robot target motion parameters (linear velocity in the X-axis direction) linear velocity in the Y-axis direction Angular velocity around the Z-axis The target rotational speeds of the four Mecanum wheels were obtained by solving the Jacobian matrix. , , , The solution formula is:

[0017]

[0018] in, The radius of the Mecanum wheel (unit: m). This is the distance between the front and rear wheels of the chassis (longitudinal track, unit: m). This is the distance between the left and right wheels of the chassis (lateral wheel track, unit: m). The target rotational speed of the front left Mecanum wheel (unit: rad / s). The target rotational speed of the front right Mecanum wheel (unit: rad / s). The target rotational speed of the rear right Mecanum wheel (unit: rad / s). The target rotational speed of the rear left Mecanum wheel (unit: rad / s); when the speed is positive, the motor rotates clockwise, and when the speed is negative, the motor rotates counterclockwise.

[0019] Correct kinematic solution: The actual rotational speeds of the four Mecanum wheels are collected via an encoder. , , , By combining the inverse of the Jacobian matrix mentioned above, the actual motion parameters of the robot can be calculated. , , This is used to compare with the target motion parameters, generate a deviation signal, and achieve closed-loop control. The forward formula is:

[0020]

[0021] Multi-source data fusion algorithm

[0022] The data fusion unit of the main control module adopts the Kalman filter algorithm in existing technology to fuse multi-source data collected by the sensing module, eliminate data noise and measurement errors from single sensors, improve data reliability, and provide accurate support for control decisions. Through the Kalman filter algorithm, the motor speed data collected by the encoder is fused with the linear acceleration and angular velocity data collected by the IMU inertial measurement unit to improve the detection accuracy of robot motion speed and attitude angle. The wheel-to-ground contact pressure data collected by the pressure sensor is fused with the obstacle distance data collected by the laser rangefinder to determine the chassis operating condition (flat ground, uneven ground, near obstacles), providing a basis for the collaborative control module and parameter adjustment.

[0023] Cooperative control algorithm

[0024] The collaborative control module employs existing PID regulation and deviation compensation algorithms, combined with an adaptive parameter adjustment strategy, to achieve precise collaborative control of the four Mecanum wheels.

[0025] Furthermore, the omnidirectional wheel chassis motion control system for robots described in this application can also incorporate the existing model predictive control (MPC) algorithm. Based on the kinematic model and state-space equation of the 4-wheel Mecanum wheel chassis, it performs rolling optimization and prediction of the future multi-step motion state of the chassis, outputs the optimal control sequence in real time, and realizes rapid and accurate positioning and trajectory tracking of the chassis.

[0026] The MPC algorithm can run efficiently on the STM32H743VIT6 embedded microprocessor. By reasonably reducing the prediction time domain and optimizing the dimension, it meets the real-time control requirements of the microcontroller.

[0027] Meanwhile, a target feedforward and disturbance feedforward compensation mechanism is introduced into the PID control algorithm, which directly connects the target motion command, load change, wheel-ground contact state and other feedforward quantities into the control loop, thereby accelerating the system response speed and error convergence, and suppressing dynamic disturbances and control delay.

[0028] By combining Kalman filter data fusion, Jacobian matrix kinematics calculation, MPC predictive control and feedforward compensation, the positioning accuracy, response speed and operation stability of the chassis under high-speed motion, variable load and complex working conditions are further improved.

[0029] Furthermore, the anomaly detection unit of the main control module is used to detect the operating status of each module of the control system in real time, including: abnormal motor operation (excessive current, excessive temperature, speed deviation exceeding the preset range), sensor failure (no data feedback, abnormal data fluctuation), and communication anomaly (interruption of data transmission between modules). When an anomaly is detected, the protection mechanism is immediately triggered, including: stopping the drive motor, cutting off the motor power supply, issuing a fault alarm (audible and visual alarm) through the human-machine interaction module, and storing the fault information in the parameter storage unit for later troubleshooting and maintenance.

[0030] Furthermore, the control system also includes a power module to provide a stable power supply for each module. The power module uses a lithium battery pack and is equipped with a power management unit, which can monitor the battery voltage and current in real time, realize overcharge, over-discharge, and overcurrent protection, and extend the battery life. At the same time, the power management unit can feed back battery status data to the main control module. When the battery power is lower than the preset threshold, a low battery alarm is issued through the human-machine interaction module, prompting the user to charge in time.

[0031] Furthermore, each Mecanum wheel is connected to the robot chassis via an independent suspension module. The independent suspension module includes two parallel cantilever arms, a connecting plate, and a shock absorber. One end of the shock absorber is connected to the connecting plate, and the other end is connected to the chassis. One end of the cantilever arm is connected to the drive shaft of the Mecanum wheel via a bearing with a mounting seat, and the other end is fixed to the optical shaft. Both ends of the optical shaft are connected to the chassis. The pressure sensor of the sensing module is installed on the connecting plate to collect the wheel-ground contact pressure and assist in judging the wheel-ground contact state. At the same time, the independent suspension module can absorb the vibration generated by ground bumps, reduce the impact of vibration on the control system and chassis movement, and improve the stability of operation.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. This invention constructs a precise kinematic model based on the motion characteristics of a four-wheeled Mecanum wheel. It employs the Jacobian matrix to achieve forward and inverse kinematic solutions, eliminating control deviations caused by assembly errors and wheel diameter errors through explicit mathematical formulas. Simultaneously, a Kalman filter algorithm is introduced for multi-source data fusion to eliminate data noise and improve the accuracy of motion parameter detection. The collaborative control module dynamically adjusts the control parameters of each motor through PID regulation and deviation compensation algorithms, combined with an adaptive parameter adjustment strategy, to compensate for speed deviations and wheel-to-ground slippage deviations, ensuring that the robot's motion trajectory is highly consistent with the target trajectory. This effectively solves the problems of trajectory drift and insufficient control accuracy in existing systems.

[0034] 2. This invention incorporates a multi-source sensing module that integrates various sensors such as an encoder, an IMU (Inertial Measurement Unit), and a pressure sensor. This module can collect real-time data on motor operation status, chassis attitude, wheel-ground contact status, and external environmental parameters. By using a Kalman filter algorithm, it achieves multi-source data fusion and operating condition identification. Combined with a collaborative control algorithm and adaptive parameter adjustment, it can effectively cope with interference from complex operating conditions such as uneven ground, load changes, and wheel-ground slippage, ensuring stable chassis movement and preventing loss of control. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the structure of an omnidirectional wheel chassis motion control system for robots proposed in this invention;

[0036] Figure 2 This is a schematic diagram of the structure of an omnidirectional wheel chassis motion control system for robots proposed in this invention;

[0037] Figure 3 This is a schematic diagram of the structure of an omnidirectional wheel chassis motion control system for robots proposed in this invention. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0039] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0040] Reference Figure 1-3 An omnidirectional wheel chassis motion control system for robots is disclosed. Based on a 4-wheel Mecanum wheel chassis architecture, the 4 Mecanum wheels are rectangularly distributed under the robot chassis and divided into two diagonal groups. The Mecanum wheel rollers of each diagonal group have opposite tilt directions. Each Mecanum wheel is equipped with an independent drive motor and reducer. The control system is characterized by including a sensing module, a main control module, a drive module, a cooperative control module, and a human-machine interaction module. The modules communicate bidirectionally via a CAN bus to form a closed-loop control.

[0041] The sensing module is used to collect the motion state, attitude information, wheel-ground contact state and external environmental parameters of the robot chassis, and sends the collected raw data to the main control module after preprocessing. The sensing module includes an encoder, an IMU inertial measurement unit, a pressure sensor, a laser rangefinder and a Hall sensor.

[0042] As the core of the control system, the main control module receives motion commands sent by the human-machine interaction module, combines them with real-time data fed back by the sensing module, generates target control commands for each Mecanum wheel drive motor through a preset kinematic model and control algorithm, and sends them to the drive module. At the same time, it receives motor operating status data fed back by the drive module in real time, performs anomaly detection and fault diagnosis, and immediately triggers the protection mechanism when an anomaly is detected. The main control module has a built-in kinematic calculation unit, data fusion unit, anomaly detection unit and parameter storage unit. The data fusion unit adopts the Kalman filter algorithm.

[0043] The drive module is used to receive the target control command sent by the main control module, convert it into drive signal for the drive motor, drive each Mecanum wheel drive motor to run according to the target speed and direction, and at the same time collect the actual running data of the drive motor and feed it back to the main control module and the cooperative control module.

[0044] The collaborative control module is used to realize the coordinated linkage control of four Mecanum wheel drive motors. Based on the target control command sent by the main control module and the wheel speed and attitude data fed back by the sensing module, the control parameters of each motor are dynamically adjusted through the collaborative control algorithm to compensate for the speed deviation and wheel-to-ground contact deviation of a single motor. The collaborative control algorithm includes PID adjustment algorithm and deviation compensation algorithm.

[0045] The human-machine interface module is used to enable interaction between the user and the control system, including motion command input, control parameter setting, operation status display, and fault alarm.

[0046] As a technical optimization of the present invention, in the four Mecanum wheels, the front left and rear right wheels are Class A rollers and tilted clockwise at 45°, the front right and rear left wheels are Class B rollers and tilted counterclockwise at 45°, and the reducer is a worm gear reducer, used to achieve speed reduction and torque increase.

[0047] As a technical optimization scheme of the present invention, the kinematic model in the main control module is constructed based on the motion characteristics of the four Mecanum wheels. The forward and inverse kinematic solutions are realized using the Jacobian matrix. A two-dimensional rectangular coordinate system is established with the geometric center of the robot chassis as the origin. The X-axis is the forward direction of the robot, the Y-axis is the lateral movement direction of the robot, the Z-axis is perpendicular to the chassis plane and is positive upward, and θ is the heading angle of the robot around the Z-axis and is positive clockwise. The inverse kinematic solution is used to calculate the target rotation speed of each Mecanum wheel according to the target motion parameters of the robot, and the forward kinematic solution is used to calculate the actual motion parameters of the robot according to the actual rotation speed of each Mecanum wheel, so as to realize closed-loop control.

[0048] As a technical optimization of the present invention, the installation and functions of each component of the sensing module are as follows:

[0049] The encoder is coaxially connected to each drive motor to collect motor speed and direction data;

[0050] The IMU (Inertial Measurement Unit) is installed at the geometric center of the chassis to collect the chassis's angular velocity, angular acceleration, linear acceleration, pitch angle, roll angle, and yaw angle.

[0051] Pressure sensors are installed on the wheel frame or independent suspension module of each Mecanum wheel to collect wheel-ground contact pressure to determine the wheel-ground contact status;

[0052] Laser rangefinders are installed around the chassis to collect distances to surrounding obstacles to assist in obstacle avoidance;

[0053] The Hall sensor is installed at the drive motor terminal to detect the motor's operating current and determine the motor's operating status.

[0054] As a technical optimization of the present invention, the main control module adopts an STM32H743VIT6 embedded microprocessor; the data fusion unit performs fusion processing on the multi-source data collected by the sensing module through the Kalman filter algorithm to eliminate data noise and measurement error of a single sensor.

[0055] The anomaly detection unit can detect abnormal motor operation, sensor failure, and communication anomalies. Upon detection of an anomaly, it immediately triggers a protection mechanism that stops motor operation, cuts off power, triggers a fault alarm, and stores fault information. The parameter storage unit uses an EEPROM storage chip to store control parameters under different loads and operating conditions, and supports users to call, modify, and save them through the human-machine interaction module.

[0056] As a technical optimization of the present invention, the drive module includes four independent BLDC motor drivers, each corresponding to a Mecanum wheel drive motor, supporting PWM signal input, speed closed-loop control and current protection functions, and can convert the target control command sent by the main control module into the drive signal of the drive motor, and collect the actual running data of the motor and feed it back to the main control module and the cooperative control module.

[0057] As a technical optimization of the present invention, the collaborative control module and the main control module are integrated in the same microprocessor, with a built-in PID adjustment unit and a deviation compensation unit. The collaborative control algorithm also includes an adaptive parameter adjustment strategy. The PID adjustment unit is used to dynamically adjust the duty cycle of the PWM signal of the motor driver for the speed deviation of each Mecanum wheel. The deviation compensation unit is used to adjust the target speed of other wheels to compensate for the deviation when wheel speed deviation or wheel slippage is detected. The adaptive parameter adjustment strategy can dynamically adjust the PID parameters according to the chassis motion conditions to improve control accuracy and stability.

[0058] As a technical optimization of the present invention, the human-computer interaction module adopts a 7-inch touch screen, which supports switching between manual control and automatic control modes. In manual mode, users can directly input motion commands such as translation and rotation through virtual joysticks or buttons. In automatic mode, users can preset motion trajectories to realize the robot's autonomous movement. It also supports control parameter setting, operation status display and fault alarm functions.

[0059] As a technical optimization of the present invention, the control system also includes a power module. The power module adopts a lithium battery pack and is equipped with a power management unit. The power management unit can monitor the battery voltage and current in real time, realize overcharge, over-discharge and overcurrent protection, and extend the battery life. The power management unit can also feed back battery status data to the main control module. When the battery power is lower than the preset threshold, a low power alarm is issued through the human-machine interaction module and the user is prompted to charge in time, so as to provide a stable working power for the entire control system.

[0060] As a technical optimization of the present invention, each Mecanum wheel is connected to the robot chassis through an independent suspension module. The independent suspension module can absorb the vibration caused by ground bumps, reduce the impact of vibration on the control system and chassis movement, and improve the stability of operation. In addition, the pressure sensor of the sensing module is installed on the connection plate of the independent suspension module to collect the wheel-ground contact pressure to help determine the wheel-ground contact state.

[0061] The working process of the control system in this invention is as follows:

[0062] Initialization: After the system is powered on, the main control module performs a self-test on each module, checking the operating status of the sensing module, drive module, collaborative control module, human-machine interaction module, and power supply module. If the self-test is normal, it enters standby mode; if an abnormality is detected, an alarm is immediately issued and the system is shut down.

[0063] Command input: The user selects the control mode (manual / automatic) through the human-computer interaction module, inputs motion commands or preset motion trajectories, and the command signal is sent to the main control module;

[0064] Kinematics calculation: The kinematics calculation unit of the main control module obtains the target speed and direction of the four Mecanum wheels according to the received motion command through the inverse Jacobian matrix formula, and sends it to the cooperative control module;

[0065] Collaborative control: The collaborative control module combines real-time data (motor speed, chassis posture, wheel-ground contact pressure, etc.) fed back by the sensing module, and dynamically adjusts the control parameters of each motor through a collaborative control algorithm (PID regulation + deviation compensation + parameter adaptive adjustment) to generate the final control command and send it to the drive module;

[0066] Drive execution: The drive module converts control commands into PWM drive signals, which drive the drive motors of the four Mecanum wheels to run at the target speed and direction, realizing the robot's omnidirectional movement;

[0067] Closed-loop feedback: The perception module collects the robot's actual motion state data in real time, preprocesses it and sends it to the main control module. The main control module obtains the actual motion parameters through the forward kinematics formula, compares them with the target parameters, generates a deviation signal and feeds it back to the collaborative control module to realize closed-loop control, continuously adjust the control parameters and ensure motion accuracy.

[0068] Anomaly Handling: The anomaly detection unit monitors the operating status of each module in real time. If anomalies such as motor overload, sensor failure, or communication interruption are detected, the protection mechanism is immediately triggered to stop the movement, cut off the power, issue an alarm, and store the fault information.

[0069] Task completed: The system stops driving the motor and enters standby mode after the user issues a stop command through the human-computer interaction module or after the preset trajectory is completed in automatic mode; if the battery power is too low, the system will automatically stop and prompt for charging.

[0070] In this invention, through the aforementioned control system, the robot can achieve translation in any direction within a plane (speed range 0-1m / s), rotation in place (angular velocity range 0-1rad / s), and composite motion along any trajectory. The motion trajectory accuracy is ≤±5mm, and the trajectory deviation when the wheels slip can be controlled within ±10mm. It can adapt to various working conditions such as flat ground and slightly uneven ground, has strong anti-interference ability, and is stable and reliable in operation. It can be widely used in industrial handling, service robots, special detection, and other scenarios.

[0071] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An omnidirectional wheel chassis motion control system for robots, based on a 4-wheel Mecanum wheel chassis architecture, wherein the 4 Mecanum wheels are rectangularly distributed under the robot chassis, divided into two diagonal groups of wheels, with the Mecanum wheel rollers of each group of diagonal wheels tilting in opposite directions, and each Mecanum wheel is equipped with an independent drive motor and reducer, characterized in that, The control system includes a sensing module, a main control module, a drive module, a cooperative control module, and a human-machine interaction module. The modules communicate with each other via a CAN bus to form a closed-loop control. The sensing module is used to collect the motion state, attitude information, wheel-ground contact state and external environmental parameters of the robot chassis, and sends the collected raw data to the main control module after preprocessing. The sensing module includes an encoder, an IMU inertial measurement unit, a pressure sensor, a laser rangefinder and a Hall sensor. As the core of the control system, the main control module receives motion commands sent by the human-machine interaction module, combines them with real-time data fed back by the sensing module, generates target control commands for each Mecanum wheel drive motor through a preset kinematic model and control algorithm, and sends them to the drive module. At the same time, it receives motor operating status data fed back by the drive module in real time, performs anomaly detection and fault diagnosis, and immediately triggers the protection mechanism when an anomaly is detected. The main control module has a built-in kinematic calculation unit, data fusion unit, anomaly detection unit and parameter storage unit. The data fusion unit adopts the Kalman filter algorithm. The drive module is used to receive the target control command sent by the main control module, convert it into drive signal for the drive motor, drive each Mecanum wheel drive motor to run according to the target speed and direction, and at the same time collect the actual running data of the drive motor and feed it back to the main control module and the cooperative control module. The collaborative control module is used to realize the coordinated linkage control of four Mecanum wheel drive motors. Based on the target control command sent by the main control module and the wheel speed and attitude data fed back by the sensing module, the control parameters of each motor are dynamically adjusted through the collaborative control algorithm to compensate for the speed deviation and wheel-to-ground contact deviation of a single motor. The collaborative control algorithm includes PID adjustment algorithm and deviation compensation algorithm. The human-machine interface module is used to enable interaction between the user and the control system, including motion command input, control parameter setting, operation status display, and fault alarm.

2. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, In the four Mecanum wheels, the front left and rear right wheels are Class A rollers and tilted 45° clockwise, while the front right and rear left wheels are Class B rollers and tilted 45° counterclockwise. The reducer is a worm gear reducer, used to achieve speed reduction and torque increase.

3. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, The kinematic model in the main control module is constructed based on the motion characteristics of the four Mecanum wheels. The forward and inverse kinematics are implemented using the Jacobian matrix. A two-dimensional Cartesian coordinate system is established with the geometric center of the robot chassis as the origin. The X-axis is the forward direction of the robot, the Y-axis is the lateral direction of the robot, the Z-axis is perpendicular to the chassis plane and is positive upward, and θ is the heading angle of the robot around the Z-axis and is positive clockwise. The inverse kinematics is used to calculate the target rotational speed of each Mecanum wheel based on the target motion parameters of the robot, and the forward kinematics is used to calculate the actual motion parameters of the robot based on the actual rotational speed of each Mecanum wheel, thereby realizing closed-loop control.

4. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, The installation and functions of each component of the sensing module are as follows: The encoder is coaxially connected to each drive motor to collect motor speed and direction data; The IMU (Inertial Measurement Unit) is installed at the geometric center of the chassis to collect the chassis's angular velocity, angular acceleration, linear acceleration, pitch angle, roll angle, and yaw angle. Pressure sensors are installed on the wheel frame or independent suspension module of each Mecanum wheel to collect wheel-ground contact pressure to determine the wheel-ground contact status; Laser rangefinders are installed around the chassis to collect distances to surrounding obstacles to assist in obstacle avoidance; The Hall sensor is installed at the drive motor terminal to detect the motor's operating current and determine the motor's operating status.

5. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, The main control module uses an STM32H743VIT6 embedded microprocessor; the data fusion unit uses a Kalman filter algorithm to fuse multi-source data collected by the sensing module, eliminating data noise and measurement errors from a single sensor. The anomaly detection unit can detect abnormal motor operation, sensor failure, and communication anomalies. Upon detection of an anomaly, it immediately triggers a protection mechanism that stops motor operation, cuts off power, triggers a fault alarm, and stores fault information. The parameter storage unit uses an EEPROM storage chip to store control parameters under different loads and operating conditions, and supports users to call, modify, and save them through the human-machine interaction module.

6. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, The drive module includes four independent BLDC motor drivers, each corresponding to a Mecanum wheel drive motor. It supports PWM signal input, speed closed-loop control, and current protection functions. It can convert the target control commands sent by the main control module into drive signals for the drive motors and collect actual motor operating data to feed back to the main control module and the cooperative control module.

7. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, The collaborative control module is integrated with the main control module in the same microprocessor, with built-in PID adjustment unit and deviation compensation unit. The collaborative control algorithm also includes an adaptive parameter adjustment strategy. The PID adjustment unit is used to dynamically adjust the duty cycle of the PWM signal of the motor driver for the speed deviation of each Mecanum wheel. The deviation compensation unit is used to adjust the target speed of other wheels to compensate for the deviation when wheel speed deviation or wheel slippage is detected. The adaptive parameter adjustment strategy can dynamically adjust the PID parameters according to the chassis motion conditions to improve control accuracy and stability.

8. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, The human-machine interaction module uses a 7-inch touch screen and supports switching between manual and automatic control modes. In manual mode, users can directly input motion commands such as translation and rotation through a virtual joystick or buttons. In automatic mode, users can preset motion trajectories to enable the robot to move autonomously. It also supports setting control parameters, displaying operating status, and fault alarm functions.

9. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, The control system also includes a power module, which uses a lithium battery pack and is equipped with a power management unit. The power management unit can monitor the battery voltage and current in real time, realize overcharge, over-discharge and overcurrent protection, and extend the battery life. The power management unit can also feed back battery status data to the main control module. When the battery power is lower than the preset threshold, it will issue a low power alarm through the human-machine interaction module and prompt the user to charge in time, providing a stable working power for the entire control system.

10. A motion control system for an omnidirectional wheel chassis applied to a robot according to claim 1, characterized in that, Each Mecanum wheel is connected to the robot chassis via an independent suspension module. The independent suspension module can absorb vibrations caused by ground bumps, reduce the impact of vibrations on the control system and chassis movement, and improve operational stability. In addition, the pressure sensor of the sensing module is installed on the connection plate of the independent suspension module to collect wheel-ground contact pressure to help determine the wheel-ground contact status.

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