Low-speed intelligent unmanned vehicle all-wheel steering-driving cooperative control system and method

Abstract

The invention relates to a low-speed intelligent unmanned vehicle all-wheel steering-driving cooperative control system and method, and relates to the field of intelligent unmanned vehicle control. According to the technical scheme, the pain point that torque distribution and steering angles of a traditional four-wheel-drive unmanned vehicle are disjointed is effectively solved, the MPC is controlled through model prediction to construct the multi-target optimization model, steering precision and energy consumption are considered, tire eccentric wear and invalid energy consumption are reduced, the service life and endurance of parts are prolonged, and the operation cost is remarkably reduced. In addition, a steering strategy is dynamically adjusted based on the running speed, rear wheels reversely steer at low speed, the turning radius is greatly reduced, and the obstacle avoidance flexibility of a narrow roadway is improved. At a high speed, the rear wheels steer in the same direction to enhance the stability, optimize the emergency lane changing control precision and adapt to complex working conditions, the control period is only 10-50 ms, the response is rapid, working condition changes can be adapted in real time, the practicability is extremely high in various low-speed scenes, and the control performance of the unmanned vehicle is comprehensively optimized.

Description

Technical Field

[0001] This invention relates to the field of intelligent unmanned vehicle control technology, and in particular to a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system and method. Background Technology

[0002] Low-speed intelligent unmanned vehicles have been widely applied in various fields such as park logistics, warehousing and transportation, closed alleyway cleaning, and street patrol. Their typical driving environments often include complex conditions such as navigating narrow passages, frequent obstacle avoidance, and emergency lane changes. This places more stringent comprehensive requirements on the vehicle's steering flexibility, driving stability, and energy economy. However, current mainstream control schemes have many shortcomings and are difficult to meet the usage requirements under the above-mentioned complex conditions. Therefore, there is an urgent need for a technical solution that can achieve coordinated control of all-wheel steering and drive, dynamically adjust the steering radius, and take into account multi-objective optimization to solve the control bottlenecks of existing low-speed intelligent unmanned vehicles.

[0003] Specifically, existing low-speed autonomous vehicles mostly adopt a traditional mid-mounted motor rear-wheel drive architecture. Although this architecture can achieve basic power output by distributing driving force to each wheel, the torque distribution strategy only focuses on driving force requirements and does not dynamically link with the steering angle in real time, resulting in poor coordination between the steering and drive systems. In terms of steering control, traditional solutions mostly use front-wheel steering alone or simple rear-wheel assisted steering modes, which cannot dynamically adjust the steering radius according to the driving speed. In low-speed scenarios, the turning radius is too large, making it difficult to adapt to obstacle avoidance operations in narrow alleys; in high-speed and straight-line scenarios, steering stability is insufficient, and the risk of sideslip is prone to occur during emergency lane changes. In addition, the existing control logic does not incorporate steering accuracy, tire wear, and energy consumption into a unified multi-objective optimization framework. Long-term operation can easily lead to excessive tire wear and significantly increased energy consumption, thereby affecting the service life and operating economy of the autonomous vehicle. Summary of the Invention

[0004] The purpose of this invention is to provide a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system and method to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system, comprising: The drive unit includes four independently controlled hub motors and corresponding hub motor controllers, which correspond to the left front wheel, right front wheel, left rear wheel and right rear wheel of the unmanned vehicle, respectively, and are used to receive torque control commands to achieve individual drive; The steering unit includes front and rear dual Ackermann steering modules and corresponding steering controllers. The front and rear dual Ackermann steering modules include a front wheel steering mechanism and a rear wheel steering mechanism, which are used to receive steering angle control commands to adjust the steering angles of the front and rear wheels. The sensing unit is used to collect driving state parameters of the autonomous vehicle, including driving speed, real-time steering angle, tire force, obstacle distance, and road width; and The collaborative control unit is communicatively connected to the drive unit, steering unit, and sensing unit. It is used to construct a multi-objective optimization model based on model predictive control (MPC), and dynamically output torque distribution commands to the drive unit and steering angle adjustment commands to the steering unit according to the parameters collected by the sensing unit. It can also adaptively adjust the steering strategy according to the driving speed.

[0006] In some implementations, the multi-objective optimization model uses steering accuracy, tire wear, and energy consumption as optimization objectives, and its objective function is: min J = ω1·Δθ + ω2·W + ω3·E; Wherein, Δθ is the deviation between the actual steering angle and the target steering angle; W is the tire wear coefficient; E is the vehicle energy consumption value; ω1, ω2, and ω3 are weighting coefficients respectively, and satisfy ω1+ω2+ω3=1. The weighting coefficients can be dynamically adjusted according to the driving scenario. The specific method by which the cooperative control unit dynamically adjusts the steering radius based on the driving speed is as follows: When the driving speed is less than the preset low speed threshold, the rear wheels are controlled to steer in the opposite direction to the front wheels; When the driving speed is greater than or equal to the preset high-speed threshold, the rear wheels are controlled to steer in the same direction as the front wheels.

[0007] In some implementations, the preset low-speed threshold ranges from 0.1 km / h to vehicle speed < 10 km / h, and the preset high-speed threshold ranges from 10 km / h to vehicle speed ≤ 25 km / h. The collaborative control unit supports custom configuration of the specific values ​​of the preset low-speed threshold and the preset high-speed threshold according to the application scenario.

[0008] In some embodiments, the sensing unit includes: The speed sensor is used to collect the driving speed of the unmanned vehicle, with a measurement range of 0-30 km / h and an accuracy of ±0.1 km / h; Steering angle sensor, which is used to collect the real-time steering angle of the front and rear wheels, with an accuracy of ±0.1°; LiDAR is used to collect distances to obstacles, with a detection range of 0.1-100m. A camera, used to capture road boundary information, operates at 30fps; and A pressure sensor is used to collect tire contact pressure.

[0009] In some implementations, the control cycle of the cooperative control unit is 10-50ms, and it can receive data transmitted by the sensing unit in real time and update torque distribution and steering angle adjustment commands.

[0010] In some embodiments, the hub motor in the drive unit has a rated power of 500W, supports continuously adjustable torque from 0 to 150 N·m, and a response time of ≤10ms; In the steering unit, the front wheel steering angle adjustment range is -35° to 35°, the rear wheel steering angle adjustment range is -15° to 15°, and the steering response time is ≤20ms.

[0011] Secondly, the present invention provides a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control method, the method being applied to the aforementioned low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system, the method comprising: Initialize, set the preset low-speed threshold, preset high-speed threshold, and weight coefficients ω1, ω2, ω3 of the multi-objective optimization model, and start the sensing unit, driving unit, and steering unit; Status acquisition: The sensing unit collects the unmanned vehicle's driving speed v, target steering angle θ_target, current front and rear wheel steering angles θ_f and θ_r, tire force F, obstacle distance d, and road width L in real time, and transmits the collected parameters to the cooperative control unit; Speed ​​determination: The cooperative control unit determines the scene type based on the driving speed v; if v < preset low speed threshold, it is determined to be a low speed scene; if v ≥ preset high speed threshold, it is determined to be a high speed and straight-line scene. Multi-objective optimization calculation: Based on model predictive control (MPC), a multi-objective optimization model is constructed. The collected state parameters are substituted into the model. The deviation between the actual steering angle and the target steering angle Δθ=|θ_target-θ_actual|, the tire wear coefficient W, and the vehicle energy consumption value E are used as optimization objectives to calculate the target torque T1-T4 of each wheel hub motor and the target steering angles θ_f_target and θ_r_target of the front and rear wheels. In low-speed scenarios, the steering strategy is executed by controlling the target steering angle θ_r_target of the rear wheels to be opposite to the target steering angle θ_f_target of the front wheels to reduce the turning radius; in high-speed and straight-line scenarios, θ_r_target is controlled to be in the same direction as θ_f_target to improve driving stability. Command output: The cooperative control unit sends the target torque T1-T4 to the corresponding wheel hub motor controller, and sends the target steering angles θ_f_target and θ_r_target to the front and rear dual Ackerman steering modules to complete one round of cooperative control; The system continuously updates and repeats the above steps of collecting status data and outputting commands according to a preset control cycle, dynamically adjusting torque distribution and steering angle in real time.

[0012] In some implementations, the multi-objective optimization calculation step includes: The tire wear coefficient W is calculated based on the tire force F, the rate of change of steering angle Δθ / Δt, and the driving speed v. The specific calculation formula is as follows: W=k1·F + k2·(Δθ / Δt) + k3·v; Where k1, k2, and k3 are proportionality coefficients; The overall vehicle energy consumption value E is the sum of the energy consumption of the four wheel hub motors, and the specific calculation formula is as follows: E=Σ(T_i·n_i / η_i); Where T_i is the torque of the i-th motor; n_i is the speed of the i-th motor; η_i is the efficiency of the i-th motor; and the value of i is 1-4. When the autonomous vehicle is in a low-speed scenario and the perception unit collects the tire force F=100N, the collaborative control unit calculates the torque distribution of the four hub motors through model predictive control (MPC) as follows: left front wheel T1=36N·m, right front wheel T2=35N·m, left rear wheel T3=32N·m, and right rear wheel T4=31N·m.

[0013] In some implementations, the initialization step includes: The weighting coefficients are specifically set as ω1=0.4, ω2=0.3, and ω3=0.3, the preset low-speed threshold is set to 10km / h, and the preset high-speed threshold is set to 20km / h.

[0014] In some implementations, the steps of executing the steering strategy include: In low-speed scenarios, when the driving speed v=8km / h is collected by the sensing unit, the cooperative control unit calculates the front wheel target steering angle θ_f_target=30° and the rear wheel target steering angle θ_r_target=-10°. The front and rear dual Ackerman steering modules adjust the front wheel steering angle to 30° and the rear wheel steering angle to -10° to achieve small-radius steering to adapt to the obstacle avoidance requirements of narrow passages. In high-speed and straight-line scenarios, when the driving speed v collected by the sensing unit is greater than or equal to 10 km / h and the target steering angle θ_target = 10°, the cooperative control unit controls the rear wheels to steer in the same direction as the front wheels, sets the target steering angle of the rear wheels to θ_r_target = 5°, and optimizes the distribution of torque among the four wheel hub motors to improve lane change stability.

[0015] The beneficial effects of the technical solution provided by this invention include at least the following: This technical solution effectively addresses the pain point of torque distribution and steering angle disconnect in traditional four-wheel-drive autonomous vehicles. It constructs a multi-objective optimization model through Model Predictive Control (MPC), balancing steering accuracy and energy consumption, reducing tire wear and ineffective energy consumption, extending component lifespan and range, and significantly lowering operating costs. Furthermore, based on a dynamic speed-adjusted steering strategy, at low speeds, the rear wheels steer in the opposite direction, significantly reducing the turning radius and improving obstacle avoidance flexibility in narrow alleyways; at high speeds, the rear wheels steer in the same direction, enhancing stability and optimizing emergency lane-changing control accuracy, adapting to complex operating conditions. With a control cycle of only 10-50ms, it offers rapid response and real-time adaptation to changing conditions, making it highly practical in various low-speed scenarios and comprehensively optimizing the control performance of autonomous vehicles. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0017] Figure 1 The diagram illustrates a structural block diagram of a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system provided by an exemplary embodiment of the present invention.

[0018] Figure 2 The diagram illustrates a logic block diagram of an all-wheel steering-drive cooperative control method for a low-speed intelligent unmanned vehicle, provided by an exemplary embodiment of the present invention.

[0019] Figure 3 The diagram illustrates a control schematic of a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control method provided by an exemplary embodiment of the present invention in a low-speed scenario.

[0020] Figure 4 The diagram illustrates a control schematic of a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control method provided by an exemplary embodiment of the present invention in a high-speed scenario.

[0021] Figure 5 The diagram illustrates a control schematic of a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control method provided by an exemplary embodiment of the present invention in a straight-line scenario. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] First, a brief introduction to some terms used in the embodiments of this application will be given: The dual Ackerman steering module is a new type of steering control mechanism applied to low-speed intelligent unmanned vehicles. Its core is to add an independent rear-wheel steering system to the traditional front-wheel Ackerman steering, forming a modular structure in which both the front and rear wheels can achieve precise steering according to the Ackerman geometry principle. It usually includes a front-wheel steering mechanism, a rear-wheel steering mechanism and a corresponding steering controller. It can receive steering angle commands from the cooperative control unit and dynamically adjust the steering angle and steering direction (in the same or opposite direction) of the front and rear wheels. In low-speed scenarios, it reduces the turning radius of the vehicle to adapt to narrow space operations, and in high-speed scenarios, it improves driving stability to cope with emergency lane changes and other working conditions.

[0024] Model Predictive Control (MPC) is an advanced control algorithm based on the concept of rolling optimization. Its core principle is to establish a mathematical model of the controlled object (such as a low-speed intelligent unmanned vehicle), combine it with real-time collected system state parameters (such as driving speed, steering angle, tire force, etc.), predict the output state of the system in the future, and then use preset multi-objective optimization indicators (such as steering accuracy, tire wear, energy consumption, etc.) as constraints to solve for the optimal control quantity through optimization algorithms. It only executes the control command at the current moment, and then repeats the prediction-optimization-execution process in the next control cycle. It has strong adaptability to dynamic operating conditions and multi-objective balancing capabilities.

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] Figure 1 This diagram illustrates a structural block diagram of a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system provided by an exemplary embodiment of the present invention. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system includes: The drive unit includes four independently controlled hub motors and corresponding hub motor controllers, which correspond to the left front wheel, right front wheel, left rear wheel and right rear wheel of the unmanned vehicle, respectively, and are used to receive torque control commands to achieve individual drive; The steering unit includes front and rear dual Ackermann steering modules and corresponding steering controllers. The front and rear dual Ackermann steering modules include a front wheel steering mechanism and a rear wheel steering mechanism, which are used to receive steering angle control commands to adjust the steering angles of the front and rear wheels. The sensing unit is used to collect the autonomous vehicle's driving status parameters, including driving speed, real-time steering angle, tire force, obstacle distance, and road width; and The collaborative control unit communicates with the drive unit, steering unit, and sensing unit. It is used to build a multi-objective optimization model based on model predictive control (MPC), and dynamically output torque distribution commands to the drive unit and steering angle adjustment commands to the steering unit according to the parameters collected by the sensing unit. It can also adaptively adjust the steering strategy according to the driving speed.

[0027] In this embodiment, the sensing unit serves as a data input source, utilizing multiple sensors to collect vehicle status and environmental information, thus addressing the control deviation problem caused by incomplete perception in traditional systems. The collaborative control unit constructs a multi-objective optimization model based on the MPC algorithm, transforming the perceived data into quantified control parameters. By dynamically balancing steering accuracy and energy consumption requirements through weighting, it breaks down the control barriers between drive and steering. The four independent wheel hub motors of the drive unit and the front and rear dual Ackermann structures of the steering unit form the execution layer. Both receive torque and steering angle commands respectively, responding to refined control needs through independent control characteristics. In this configuration, the system, through real-time communication between the units, enables dynamic adaptation of steering and drive to changes in driving speed and environment, ultimately achieving precise and efficient control under complex operating conditions.

[0028] In some embodiments, the multi-objective optimization model takes steering accuracy, tire wear, and energy consumption as optimization objectives, and its objective function is: min J = ω1·Δθ + ω2·W + ω3·E; Where Δθ is the deviation between the actual steering angle and the target steering angle (steering accuracy); W is the tire wear coefficient; E is the vehicle energy consumption value; ω1, ω2, and ω3 are weighting coefficients respectively, and satisfy ω1+ω2+ω3=1. The weighting coefficients can be dynamically adjusted according to the driving scenario.

[0029] In this embodiment, steering accuracy, tire wear, and energy consumption are used as optimization dimensions. Abstract requirements are transformed into calculable objectives through three quantitative indicators: Δθ, W, and E, ensuring the accuracy of steering command execution. W and E are associated with operating costs, with losses quantified through parameters such as tire force, steering change rate, and motor torque. The objective function min J integrates the three objectives through linear combination. In narrow passages, ω1 is increased to prioritize steering; during long-distance travel, ω2 and ω3 are increased to control costs. A flexible balance between handling performance and operational economy is achieved through dynamic weight allocation.

[0030] In some embodiments, the cooperative control unit dynamically adjusts the steering radius based on the driving speed in the following ways: When the driving speed is less than the preset low speed threshold, the rear wheels are controlled to steer in the opposite direction to the front wheels; When the driving speed is greater than or equal to the preset high-speed threshold, the rear wheels are controlled to steer in the same direction as the front wheels.

[0031] In some embodiments, the range of the preset low speed threshold is 0.1km / h ≤ vehicle speed < 10km / h, and the range of the preset high speed threshold is 10km / h ≤ vehicle speed ≤ 25km / h. The cooperative control unit supports custom configuration of the specific values ​​of the preset low speed threshold and the preset high speed threshold according to the application scenario.

[0032] In this embodiment, at low speeds, autonomous vehicles are often in narrow alleys or making close-range U-turns, requiring a minimized turning radius. The rear wheels turning in opposite directions to the front wheels can generate a crabbing effect through the difference in steering angle between the wheels, significantly reducing the required steering space and meeting the flexibility demands of low-speed conditions. At high speeds, vehicle inertia increases, making lateral stability crucial. The rear wheels turning in the same direction as the front wheels can reduce wheel track deviation, lowering the risk of sideslip caused by centrifugal force and adapting to the stability requirements of road driving and emergency lane changes.

[0033] In some embodiments, the sensing unit includes: The speed sensor is used to collect the driving speed of the unmanned vehicle, with a measurement range of 0-30 km / h and an accuracy of ±0.1 km / h; Steering angle sensor, which is used to collect the real-time steering angle of the front and rear wheels, with an accuracy of ±0.1°; LiDAR is used to collect distances to obstacles, with a detection range of 0.1-100m. A camera, used to capture road boundary information, operates at 30fps; and A pressure sensor is used to collect tire contact pressure.

[0034] In some embodiments, the cooperative control unit is an MCU with a main frequency of 1GHz, which supports real-time calculation of the MPC algorithm, has a control cycle of 10-50ms, and can receive data transmitted by the sensing unit in real time and update torque distribution and steering angle adjustment commands.

[0035] In some embodiments, the drive unit has a hub motor with a rated power of 500W, supports continuously adjustable torque from 0 to 150 N·m, and a response time of ≤10ms; the steering unit has a front wheel steering angle adjustment range of -35° to 35°, a rear wheel steering angle adjustment range of -15° to 15°, and a steering response time of ≤20ms.

[0036] It should be noted that the low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above.

[0037] Figure 2The diagram illustrates a logic block diagram of a low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control method according to an exemplary embodiment of the present invention. This method is applied in the aforementioned low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system and includes: Initialize, set the preset low-speed threshold, preset high-speed threshold, and weight coefficients ω1, ω2, ω3 of the multi-objective optimization model, and start the sensing unit, driving unit, and steering unit; The state acquisition and perception unit collects the unmanned vehicle's driving speed v, target steering angle θ_target, current front and rear wheel steering angles θ_f and θ_r, tire force F, obstacle distance d, and road width L in real time, and transmits the collected parameters to the collaborative control unit. Speed ​​determination: The cooperative control unit determines the scene type based on the driving speed v; if v < preset low speed threshold, it is determined to be a low speed scene; if v ≥ preset high speed threshold, it is determined to be a high speed and straight-line scene. Multi-objective optimization calculation: Based on model predictive control (MPC), a multi-objective optimization model is constructed. The collected state parameters are substituted into the model. The deviation between the actual steering angle and the target steering angle Δθ=|θ_target-θ_actual|, the tire wear coefficient W, and the vehicle energy consumption value E are used as optimization objectives to calculate the target torque T1-T4 of each wheel hub motor and the target steering angles θ_f_target and θ_r_target of the front and rear wheels. In low-speed scenarios, the steering strategy is executed by controlling the target steering angle θ_r_target of the rear wheels to be opposite to the target steering angle θ_f_target of the front wheels to reduce the turning radius; in high-speed and straight-line scenarios, θ_r_target is controlled to be in the same direction as θ_f_target to improve driving stability. Upon command output, the collaborative control unit sends the target torques T1-T4 to the corresponding wheel hub motor controllers and sends the target steering angles θ_f_target and θ_r_target to the front and rear dual Ackermann steering modules, thus completing one round of collaborative control. The system continuously updates and repeats the above steps of collecting status data and outputting commands according to a preset control cycle, dynamically adjusting torque distribution and steering angle in real time.

[0038] In this embodiment, state acquisition captures vehicle and environmental states through multi-dimensional data, constructing a data input layer for control decisions and addressing control deviations caused by information gaps. Speed ​​judgment serves as a scene switching hub, classifying operating conditions based on speed to match needs with strategies. The MPC algorithm transforms data into quantified control parameters, balancing steering accuracy and economy. Steering strategy execution and command output form a decision-making and execution link, outputting adaptive actions according to the scene.

[0039] In some embodiments, during the multi-objective optimization calculation steps: The tire wear coefficient W is calculated based on the tire force F, the rate of change of steering angle Δθ / Δt, and the driving speed v. The specific calculation formula is as follows: W=k1·F + k2·(Δθ / Δt) + k3·v; Where k1, k2, and k3 are proportionality coefficients.

[0040] The overall vehicle energy consumption value E is the sum of the energy consumption of the four wheel hub motors, and the specific calculation formula is as follows: E=Σ(T_i·n_i / η_i); Where T_i is the torque of the i-th motor; n_i is the speed of the i-th motor; η_i is the efficiency of the i-th motor; and the value of i is 1-4.

[0041] When the autonomous vehicle is in a low-speed scenario and the perception unit collects the tire force F=100N, the collaborative control unit calculates the torque distribution of the four hub motors through model predictive control (MPC) as follows: left front wheel T1=36N·m, right front wheel T2=35N·m, left rear wheel T3=32N·m, and right rear wheel T4=31N·m.

[0042] In this embodiment, the tire wear coefficient W is based on the mechanical characteristic that wear is positively correlated with force, frequency of action, and driving load. F directly reflects the tire contact pressure and is the cause of wear; Δθ / Δt reflects the frequency of steering actions, and high-frequency steering easily aggravates local wear; v is related to the friction duration, and the higher the speed, the faster the wear accumulates; k1-k3 are adapted to the characteristics of different tire materials through proportional coefficients. The summation formula of energy consumption E calculates the energy consumption of a single motor through the torque T_i, speed n_i, and efficiency η_i of each motor, avoiding the error of overall estimation and ensuring that energy consumption optimization can be implemented at the specific wheel end. The torque distribution case in the low-speed scenario is a concrete result of MPC combined with W and E calculation. When F=100N, the front wheel torque is slightly higher to adapt to the steering-dominant demand, while the wear and energy consumption of each wheel are balanced through slight differences.

[0043] In some embodiments, during the initialization step: The weighting coefficients are set as follows: ω1=0.4, ω2=0.3, ω3=0.3. The preset low-speed threshold is set to 10km / h, and the preset high-speed threshold is set to 20km / h.

[0044] In some embodiments, during the step of executing the redirection policy: See Figure 3In low-speed scenarios, when the driving speed v=8km / h is collected by the sensing unit, the cooperative control unit calculates the front wheel target steering angle θ_f_target=30° and the rear wheel target steering angle θ_r_target=-10°. The front and rear dual Ackerman steering modules adjust the front wheel steering angle to 30° and the rear wheel steering angle to -10° to achieve small-radius steering to adapt to the obstacle avoidance requirements of narrow passages. See Figure 4 and Figure 5 In high-speed and straight-line scenarios, when the driving speed v collected by the sensing unit is greater than or equal to 10 km / h and the target steering angle θ_target = 10°, the cooperative control unit controls the rear wheels to steer in the same direction as the front wheels, sets the target steering angle of the rear wheels to θ_r_target = 5°, and optimizes the distribution of torque of the four wheel hub motors to improve lane change stability.

[0045] In this embodiment, at low speeds, obstacle avoidance in narrow passages requires minimizing the turning radius. A large 30° steering angle on the front wheels provides the basic steering torque, while a -10° reverse steering angle on the rear wheels generates a coordinated steering torque through the difference in steering angles between the wheels. The Ackermann steering geometry is used to reduce the diameter of the steering trajectory, solving the problem of maneuvering in confined spaces. At high speeds, increased vehicle inertia leads to higher demands for lateral stability. A target steering angle of 10° corresponds to emergency lane changes, and a 5° unidirectional steering angle on the rear wheels reduces wheel track offset, offsetting the impact of centrifugal force on the vehicle's attitude. Simultaneously, optimized torque distribution balances the load difference between wheels during steering by adjusting the power output of each wheel, preventing overload and slippage on one side of the tires. Ultimately, through the dual control of quantified steering angle parameters and coordinated torque, the on-demand switching between flexibility and stability in different speed scenarios is achieved.

[0046] In summary, this technical solution effectively addresses the pain point of torque distribution and steering angle disconnect in traditional four-wheel-drive autonomous vehicles. It constructs a multi-objective optimization model through Model Predictive Control (MPC), balancing steering accuracy and energy consumption, reducing tire wear and ineffective energy consumption, extending component lifespan and range, and significantly lowering operating costs. Furthermore, based on a dynamic speed-adjusted steering strategy, the rear wheels steer in the opposite direction at low speeds, significantly reducing the turning radius and improving obstacle avoidance flexibility in narrow alleyways; at high speeds, the rear wheels steer in the same direction, enhancing stability and optimizing emergency lane-changing control accuracy, adapting to complex operating conditions. With a control cycle of only 10-50ms, it offers rapid response and real-time adaptation to changing conditions, making it highly practical in various low-speed scenarios and comprehensively optimizing the control performance of autonomous vehicles.

[0047] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand this disclosure, and are not intended to limit the scope of the invention.

[0048] It is understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this disclosure.

[0049] It is understood that the various implementation methods described in this specification can be implemented individually or in combination, and this disclosure does not limit them.

[0050] Unless otherwise stated, all technical and scientific terms used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this specification. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0051] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0052] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the aforementioned method implementation, and will not be repeated here.

[0053] In the several embodiments provided in this specification, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments of the systems described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0054] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0055] In addition, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0056] The above description is merely a specific embodiment of this specification, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system, characterized in that, include: The drive unit includes four independently controlled hub motors and corresponding hub motor controllers, which correspond to the left front wheel, right front wheel, left rear wheel and right rear wheel of the unmanned vehicle, respectively, and are used to receive torque control commands to achieve individual drive; The steering unit includes front and rear dual Ackermann steering modules and corresponding steering controllers. The front and rear dual Ackermann steering modules include a front wheel steering mechanism and a rear wheel steering mechanism, which are used to receive steering angle control commands to adjust the steering angles of the front and rear wheels. The sensing unit is used to collect the driving status parameters of the unmanned vehicle, including driving speed, real-time steering angle, tire force, obstacle distance, and road width. as well as The collaborative control unit is communicatively connected to the drive unit, steering unit, and sensing unit. It is used to construct a multi-objective optimization model based on model predictive control (MPC), and dynamically output torque distribution commands to the drive unit and steering angle adjustment commands to the steering unit according to the parameters collected by the sensing unit. It can also adaptively adjust the steering strategy according to the driving speed.

2. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system according to claim 1, characterized in that, The multi-objective optimization model takes steering accuracy, tire wear, and energy consumption as optimization objectives, and its objective function is: min J = ω1·Δθ + ω2·W + ω3·E; Wherein, Δθ is the deviation between the actual steering angle and the target steering angle; W is the tire wear coefficient; E is the vehicle energy consumption value; ω1, ω2, and ω3 are weighting coefficients respectively, and satisfy ω1+ω2+ω3=1. The weighting coefficients can be dynamically adjusted according to the driving scenario. The specific method by which the cooperative control unit dynamically adjusts the steering radius based on the driving speed is as follows: When the driving speed is less than the preset low speed threshold, the rear wheels are controlled to steer in the opposite direction to the front wheels; When the driving speed is greater than or equal to the preset high-speed threshold, the rear wheels are controlled to steer in the same direction as the front wheels.

3. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system according to claim 2, characterized in that, The preset low-speed threshold ranges from 0.1 km / h to vehicle speed < 10 km / h, and the preset high-speed threshold ranges from 10 km / h to vehicle speed ≤ 25 km / h. The collaborative control unit supports custom configuration of the specific values ​​of the preset low-speed threshold and the preset high-speed threshold according to the application scenario.

4. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system according to claim 1, characterized in that, The sensing unit includes: The speed sensor is used to collect the driving speed of the unmanned vehicle, with a measurement range of 0-30 km / h and an accuracy of ±0.1 km / h; Steering angle sensor, which is used to collect the real-time steering angle of the front and rear wheels, with an accuracy of ±0.1°; LiDAR is used to collect distances to obstacles, with a detection range of 0.1-100m. A camera, used to capture road boundary information, operates at 30fps; and A pressure sensor is used to collect tire contact pressure.

5. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system according to claim 1, characterized in that, The control cycle of the cooperative control unit is 10-50ms, and it can receive data transmitted by the sensing unit in real time and update torque distribution and steering angle adjustment commands.

6. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system according to claim 1, characterized in that, In the drive unit, the hub motor has a rated power of 500W, supports continuously adjustable torque from 0 to 150 N.m, and a response time of ≤10ms; In the steering unit, the front wheel steering angle adjustment range is -35° to 35°, the rear wheel steering angle adjustment range is -15° to 15°, and the steering response time is ≤20ms.

7. A method for all-wheel steering-drive coordinated control of a low-speed intelligent unmanned vehicle, characterized in that, The method is applied to the low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control system as described in any one of claims 1-6, and the method includes: Initialize, set the preset low-speed threshold, preset high-speed threshold, and weight coefficients ω1, ω2, ω3 of the multi-objective optimization model, and start the sensing unit, driving unit, and steering unit; Status acquisition: The sensing unit collects the unmanned vehicle's driving speed v, target steering angle θ_target, current front and rear wheel steering angles θ_f and θ_r, tire force F, obstacle distance d, and road width L in real time, and transmits the collected parameters to the cooperative control unit; Speed ​​determination: The cooperative control unit determines the scene type based on the driving speed v; if v < preset low speed threshold, it is determined to be a low speed scene; if v ≥ preset high speed threshold, it is determined to be a high speed and straight-line scene. Multi-objective optimization calculation: Based on model predictive control (MPC), a multi-objective optimization model is constructed. The collected state parameters are substituted into the model. The deviation between the actual steering angle and the target steering angle Δθ=|θ_target-θ_actual|, the tire wear coefficient W, and the vehicle energy consumption value E are used as optimization objectives to calculate the target torque T1-T4 of each wheel hub motor and the target steering angles θ_f_target and θ_r_target of the front and rear wheels. In low-speed scenarios, the steering strategy is executed by controlling the target steering angle θ_r_target of the rear wheels to be opposite to the target steering angle θ_f_target of the front wheels to reduce the turning radius; in high-speed and straight-line scenarios, θ_r_target is controlled to be in the same direction as θ_f_target to improve driving stability. Command output: The cooperative control unit sends the target torque T1-T4 to the corresponding wheel hub motor controller, and sends the target steering angles θ_f_target and θ_r_target to the front and rear dual Ackerman steering modules to complete one round of cooperative control; The system continuously updates and repeats the above steps of collecting status data and outputting commands according to a preset control cycle, dynamically adjusting torque distribution and steering angle in real time.

8. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control method according to claim 7, characterized in that, In the steps of the multi-objective optimization calculation: The tire wear coefficient W is calculated based on the tire force F, the rate of change of steering angle Δθ / Δt, and the driving speed v. The specific calculation formula is as follows: W=k1·F + k2·(Δθ / Δt) + k3·v; Where k1, k2, and k3 are proportionality coefficients; The overall vehicle energy consumption value E is the sum of the energy consumption of the four wheel hub motors, and the specific calculation formula is as follows: E=Σ(T_i·n_i / η_i); Where T_i is the torque of the i-th motor; n_i is the speed of the i-th motor; η_i is the efficiency of the i-th motor; and the value of i is 1-4. When the autonomous vehicle is in a low-speed scenario and the perception unit collects the tire force F=100N, the collaborative control unit calculates the torque distribution of the four hub motors through model predictive control (MPC) as follows: left front wheel T1=36N·m, right front wheel T2=35N·m, left rear wheel T3=32N·m, and right rear wheel T4=31N·m.

9. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control method according to claim 7, characterized in that, In the initialization step: The weighting coefficients are specifically set as ω1=0.4, ω2=0.3, and ω3=0.3, the preset low-speed threshold is set to 10km / h, and the preset high-speed threshold is set to 20km / h.

10. The low-speed intelligent unmanned vehicle all-wheel steering-drive cooperative control method according to claim 7, characterized in that, In the steps of executing the steering strategy: In low-speed scenarios, when the driving speed v=8km / h is collected by the sensing unit, the cooperative control unit calculates the front wheel target steering angle θ_f_target=30° and the rear wheel target steering angle θ_r_target=-10°. The front and rear dual Ackerman steering modules adjust the front wheel steering angle to 30° and the rear wheel steering angle to -10° to achieve small-radius steering to adapt to the obstacle avoidance requirements of narrow passages. In high-speed and straight-line scenarios, when the driving speed v collected by the sensing unit is greater than or equal to 10 km / h and the target steering angle θ_target = 10°, the cooperative control unit controls the rear wheels to steer in the same direction as the front wheels, sets the target steering angle of the rear wheels to θ_r_target = 5°, and optimizes the distribution of torque among the four wheel hub motors to improve lane change stability.

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