Traffic emergency robot with independent walking and autonomous driving dual functions and method

Abstract

The application relates to a traffic emergency robot with independent walking and autonomous driving dual functions and a method, and relates to the technical field of traffic management. The power supply system comprises a lithium battery module, a charging system and a voltage conversion module. The perception system perceives the environmental information around the robot through a visual sensor, a millimeter wave radar, a laser radar and an RFID. The communication system is responsible for communication between the robot and external equipment. The data collected by the perception system is transmitted to the control system through the communication system, and the robot is controlled and data storage is completed through a motor controller, a servo driver and an electronic chip, so that the robot body is driven. The robot can not only independently walk and complete traffic management tasks, but also is different from traditional unmanned driving, realizes road condition perception and autonomously drives ordinary special vehicles through multi-sensor fusion technology and robot personification operation.

Description

Technical Field

[0001] This invention relates to the field of traffic management technology, and in particular to a traffic emergency robot and method with dual functions of independent walking and autonomous driving. Background Technology

[0002] Currently, conventional special vehicles do not possess intelligent driving capabilities. Rescue and special operations in dangerous environments are extremely dangerous for personnel. In situations such as flash floods, blasting, nuclear leaks, and epidemics, traffic is easily blocked, and rescue personnel face a huge risk of injury or death, significantly hindering the operation. Traffic emergency management is therefore of paramount importance. If existing unmanned vehicles with intelligent driving capabilities are used, although it can prevent rescue personnel from being injured, they cannot enter the rescue site when the road is congested or blocked. In the case of traffic paralysis caused by a sudden accident, a traffic emergency robot with the following functions is urgently needed: (1) Independent walking function: a small and flexible intelligent robot that can identify vehicles and drivable areas around the road, enter the accident center, and transmit sensor data to the traffic command center for traffic control to alleviate traffic congestion; (2) Autonomous driving function: an anthropomorphic driving of accident vehicles or rescue vehicles to perform the evacuation or rescue tasks of ordinary conventional vehicles. Therefore, a traffic emergency robot with both independent walking and autonomous driving functions is urgently needed to realize the tasks of traffic emergency management, vehicle evacuation, or rescue operations in the event of traffic congestion caused by dangerous environments.

[0003] However, existing emergency robots cannot perform the above functions simultaneously, nor can they perform path planning, thus failing to effectively complete emergency tasks. Summary of the Invention

[0004] In response to the above, this invention provides a traffic emergency robot and method with dual functions of independent walking and autonomous driving. It can be installed in the driver's seat of a vehicle and, in conjunction with special vehicles, achieves human-like intelligent driving and vehicle-road coordination, enabling safe and rapid rescue missions and facilitating the evacuation of congested traffic. Simultaneously, multi-sensor fusion technology enables high-precision and high-efficiency road condition perception, allowing for efficient operation even in environments with obstructed vision, poor signal, or high burial density. Furthermore, it possesses independent walking capabilities, allowing the traffic emergency robot to independently navigate when congested vehicles cannot enter, enabling environmental surveying and real-time display and reporting of current road conditions. This facilitates traffic monitoring, patrolling, and command, promoting the smooth conduct of rescue missions, traffic command, and supervision. It offers advantages such as convenient operation, high efficiency, low cost, and portability.

[0005] The technical solution adopted in this invention is a traffic emergency robot with dual functions of independent walking and autonomous driving. It includes a power system, a communication system, a perception system, a control system, and a robot body. The communication system includes a wireless communication module, a wired communication interface, and an encoder. The wireless communication module is installed near the main control unit of the robot body, the wired communication interface is installed on the robot body, and the encoder is installed on the robot body and connected to the moving parts of the robot body. The perception system includes a vision sensor module, a millimeter-wave radar module, a lidar module, and an RFID module. The vision sensor module, millimeter-wave radar module, and lidar module are all installed on sensor brackets on the robot body. The RFID module includes RFID smart tags and RFID tag readers. The RFID smart tags are placed at the target intersection, and the RFID tag readers are placed on the sensor bracket of the robot body. The control system includes a motor controller, motor sensors, motors, servo drivers, a microcontroller, and a communication expansion unit. The motor controller, servo drivers, and communication expansion unit are all mounted on the microcontroller. The motors include DC servo electric cylinders, DC articulated motors, DC high-torque geared motors, and DC hub motors. The motor sensors are mounted on the motors. The servo drivers are respectively arranged on two servo motor cylinders. The communication expansion unit connects to a vision sensor module, a millimeter-wave radar module, and a lidar module.

[0006] The robot body includes a robot frame, a three-jaw chuck, a robotic arm, a sensor bracket, a folding plate, a rear wing, a front wheel frame, a rear wheel frame, and drive wheels. The three-jaw chuck is located at the top of the robotic arm and connected to a DC high-torque geared motor. The three-jaw chuck can be locked onto the outside of a vehicle steering wheel. The robotic arm includes a first link and a second link. The first end of the first link is fixedly connected to the three-jaw chuck, and the second end of the first link and the first end of the second link are rotatably connected. The second end of the second link is fixedly connected to the robot frame. The sensor bracket is slidably connected to the first link, and a sensor is mounted on the sensor bracket. The sensing system includes a folding plate rotatably connected to the front wheel frame, with two servo motor cylinders fixed on the folding plate. The rear wing is rotatably connected to the rear wheel frame. The drive wheels include two front drive wheels and two rear drive wheels. The front drive wheels are connected to the DC joint motors, and the rear drive wheels are connected to the DC hub motors. Both the folding plate and the drive wheels have upper and lower limits. When the vehicle is autonomously driven, the folding plate flips to the lower limit to control the throttle and brake, and the drive wheels fold to the upper limit. When the vehicle is moving independently, the folding plate flips to the upper limit, the drive wheels fold to the lower limit, and the vehicle moves independently.

[0007] When driving an autonomous vehicle, the current drivable area is obtained through a road condition perception algorithm, and the centerline of the current drivable area is calculated. Based on the angle between the centerline of the drivable area and the vehicle's direction of travel, the robot's next movement is determined. The steering wheel angle and throttle depth are used as control variables to make the car's steering angle follow the angle change and maintain a constant distance from the side of the vehicle to the centerline of the drivable area.

[0008] When independently clearing roads, the system determines its current location based on radar information. If it has reached the designated location, it uses the intercom voice monitoring system to direct road clearing. After clearing the road, it returns on a drivable dedicated lane, thus ending the current road clearing operation. If there are still evacuation points, it moves to the next road clearing location. The system uses a road condition perception algorithm to determine whether it can proceed. If not, it uses the intercom voice monitoring system to guide vehicles on both sides to make way for drivable areas. Based on the road condition label results predicted by the main control chip, it determines whether to proceed, turn left, or turn right, and then controls the speed and direction according to the set thresholds.

[0009] Preferably, the vision sensor module includes two monocular cameras, one rear-view camera, and four surround-view cameras.

[0010] Preferably, the millimeter-wave radar module includes four lateral radars and two forward radars.

[0011] Preferably, the lidar module includes a multi-threaded velocity-measuring lidar and a multi-threaded ranging lidar.

[0012] Preferably, the length of the first link is equal to the length of the second link.

[0013] Preferably, the sensor bracket and the fixed end of the first connecting rod are provided with a movable buckle.

[0014] Preferably, the power system includes a lithium battery module, and the lithium battery module includes a transformer module.

[0015] Another aspect of the present invention provides an autonomous driving method for a traffic emergency robot, which includes the following steps:

[0016] S1. Obtain the current drivable area through a road condition perception algorithm;

[0017] S2. Calculate the centerline of the current drivable area, and determine the robot's next movement based on the angle between the centerline of the drivable area and the direction of the vehicle's travel.

[0018] S3. Using the steering wheel angle and throttle depth as control variables, the controller outputs execution commands to the actuator based on the PID algorithm to control the motor's movement so as to maintain the car's steering angle following the angle change and to maintain a constant distance between the vehicle side and the centerline of the driving area.

[0019] Another aspect of the present invention provides a method for an independent walking method for a traffic emergency robot, which includes the following steps:

[0020] S1. Determine whether it is possible to proceed based on the road condition perception algorithm. If not, guide vehicles on both sides to give way to the driving area through the intercom voice monitoring system.

[0021] S2. Based on the road condition label results predicted by the main control chip, determine whether to move forward, turn left or right, and then use the PID algorithm to control the speed and direction according to the set threshold.

[0022] S3. Determine the current location based on radar information. If the designated location has been reached, use the intercom voice monitoring system to direct traffic. After clearing the road, return through the drivable area. This mission ends. If there are still locations that need to be evacuated, proceed to the next evacuation location.

[0023] Preferably, the specific steps of the road condition perception algorithm are as follows:

[0024] S1. Plan the motion trajectory with time involved, the specific formula is:

[0025]

[0026] Where u(t) is the time-dependent trajectory, and k p T is the proportionality coefficient. i Let T be the integration time constant. d The differential time constant;

[0027] S2. Use the output of the lateral controller as the steering wheel angle control quantity for lateral control, including the following sub-steps:

[0028] S21. Analysis of the error of the lateral controller:

[0029] Calculate the robot's relative position in the Frenet coordinate system:

[0030] dχ=χ-χ des

[0031] dy = yy des

[0032] The χ² value at time t is given by the trajectory route function. r With y r The value of, i.e.:

[0033] χ r (t)=χ(t)

[0034] y r (t)=y(t)

[0035] θ r (t)=arctan{y′[χ(t)]}

[0036]

[0037] The vehicle's coordinates in the Frenet coordinate system are obtained using the rotation formula for a two-dimensional coordinate system:

[0038]

[0039] e = d

[0040]

[0041] Where e is the lateral error, and θ is the heading angle error. For the rate of change of lateral error, The rate of change of heading angle error, The heading angle of the robot's current position. v is the heading angle of the path reference point, and v is the robot's current velocity. Let k be the robot's current yaw rate. des The curvature of the path reference point;

[0042] The state variables of the control system are represented as follows:

[0043]

[0044] S22, LQR lateral feedback control, specifically includes the following sub-steps:

[0045] S221. Establish the state-space model:

[0046] Assuming the state vector is x and the control input vector is u, the state-space model can be represented as:

[0047]

[0048] Here, A and B are constant matrices, representing the system's state transition and input matrices.

[0049] S222. Define the cost function:

[0050]

[0051] Here, Q and R are positive definite matrices, representing the weighting coefficients of the state and the input.

[0052] S23. Calculate the optimal controller:

[0053] The optimal controller is represented as a linear state feedback controller:

[0054] u = -Kx

[0055] Where K is the state feedback matrix, substituting the above controller into the state-space model, we get:

[0056]

[0057] Substituting into the cost function, we get:

[0058]

[0059] By differentiating the cost function, the analytical expression for the state feedback matrix K is obtained:

[0060] K = (R + B) T PB) -1 B T PA

[0061] Where P is a positive definite matrix satisfying the algebraic Riccati equation:

[0062] A T P+PA-PBR -1 B T P+Q=0

[0063] The Riccati equations are obtained through iterative solutions.

[0064] S24. Solve for the control variables:

[0065] Based on the state feedback matrix K, the optimal control input is calculated to achieve optimal lateral control.

[0066] δ=u=-Kx

[0067] Where δ is the calculated rotation angle;

[0068] S3. Use a longitudinal position-speed PID controller to calculate throttle depth for longitudinal control:

[0069] The longitudinal position error is calculated as follows:

[0070]

[0071] The speed error is:

[0072]

[0073] The acceleration is:

[0074]

[0075] Longitudinal control utilizes dual PID control for speed, i.e., using e sAs inputs to the position PID, the speed difference between the desired speed and the current speed, along with the output of the position PID, are used as inputs to the speed PID. The output of the speed PID, together with the acceleration, is used as the acceleration input.

[0076] The features and beneficial effects of this invention are:

[0077] (1) The present invention provides a traffic emergency robot with independent walking and autonomous driving functions. It can be installed in the driver's seat of a vehicle to realize human-like intelligent driving and vehicle-road coordination functions. It can not only evacuate vehicles in traffic jams, but also operate rescue vehicles to perform human-like operations, ensuring the safe and rapid execution of rescue missions in dangerous environments.

[0078] (2) The present invention provides a traffic emergency robot with independent walking and autonomous driving functions. With the help of multi-sensor fusion technology, it can achieve high-precision and high-efficiency road condition perception and can still perform efficient operations in environments with obstructed vision, poor signal, and high burial degree.

[0079] (3) The present invention provides a traffic emergency robot with dual functions of independent walking and autonomous driving. It has the function of independent walking. When traffic jams make it difficult for vehicles to enter, it can walk independently, freely shuttle through the gaps in the space of congested vehicles, guide traffic, clear traffic jams, and flexibly act as an emergency traffic police.

[0080] (4) The present invention provides a traffic emergency robot with independent walking and autonomous driving functions. It can display and broadcast the current road conditions in real time, realize traffic supervision, patrol and command, which is conducive to the smooth implementation of rescue missions, traffic command and supervision, etc. It has the advantages of convenient operation, high efficiency, low cost and portability.

[0081] (5) The traffic emergency robot of the present invention has the dual functions of independent walking and autonomous driving. It has the function of autonomous driving lane keeping. The robot's steering wheel angle and throttle depth are determined by the distance from the side of the car to the center line of the driving area, the angle between the car's direction of travel and the tangent direction of the center line of the driving area. The robot automatically controls the steering wheel angle and vehicle speed based on the PID control algorithm to ensure the accuracy of the control. Attached Figure Description

[0082] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0083] Figure 2 This invention relates to an intercom voice monitoring system;

[0084] Figure 3 This is a schematic diagram of the autonomous driving posture of the present invention;

[0085] Figure 4This is a schematic diagram of the initial independent walking posture of the present invention;

[0086] Figure 5 This is a schematic diagram of the posture after independent walking adjustment according to the present invention;

[0087] Figure 6 This is a schematic diagram of the topology of the present invention;

[0088] Figure 7 This is a diagram showing the arrangement of the sensor in the sensor holder according to the present invention;

[0089] Figure 8 This is a schematic diagram of the direction-speed joint control autonomous driving strategy of the present invention;

[0090] Figure 9 This is a diagram of the direction-speed joint control autonomous driving control scheme of the present invention;

[0091] Figure 10 This is a block diagram of the PID-based path planning and driving control algorithm of the present invention;

[0092] Figure 11 This is a block diagram of the PID closed-loop control system of the present invention;

[0093] Figure 12 This is a schematic diagram of the PID longitudinal control position error of the present invention;

[0094] Figure 13 This is a schematic diagram of the longitudinal control of the present invention. Detailed Implementation

[0095] To fully describe the technical content, structural features, objectives, and effects of this invention, a detailed description will be provided below in conjunction with the accompanying drawings.

[0096] This invention provides a traffic emergency robot with both independent walking and autonomous driving functions. Figure 1This is a schematic diagram of the overall structure of the present invention, which includes a power system, a communication system, a sensing system, a control system, and a robot body. The communication system includes a wireless communication module, a wired communication interface, and an encoder. The wireless communication module is installed near the main control unit of the robot body, the wired communication interface is installed on the robot body, and the encoder is installed on the robot body and connected to the moving parts of the robot body. The sensing system includes a vision sensor module, a millimeter-wave radar module, a lidar module, and an RFID module. The vision sensor module, millimeter-wave radar module, and lidar module are all installed on the sensor bracket of the robot body. The RFID module includes RFID smart tags and an RFID tag reader / writer. The RFID smart tags are placed at the target intersection, and the RFID tag reader / writer is placed on the sensor bracket of the robot body. The control system includes a motor controller, motor sensors, motors, servo drivers, a microcontroller, and a communication expansion unit. The motor controller, servo drivers, and communication expansion unit are all mounted on the microcontroller. The motors include DC servo electric cylinders, DC articulated motors, DC high-torque geared motors, and DC hub motors. The motor sensors are mounted on the motors, and the servo drivers are respectively arranged on two servo motor cylinders. The communication expansion unit connects the vision sensor module, the millimeter-wave radar module, and the lidar module.

[0097] In this embodiment, the power system used is a lithium battery module A. The communication and control systems are both installed inside the robot, acquiring current road information through a traffic network. The communication system includes, for example: Figure 2 The illustrated intercom voice monitoring system allows the monitoring center to direct traffic in real time. Information is processed by the control system and fed back to the mechanical structure for vehicle-road coordination, directing current traffic. The perception system's internal visual sensors, millimeter-wave radar, lidar, and RFID tag readers are all mounted on sensor bracket 1. Visual sensors are the primary means of identification, supplemented by millimeter-wave radar and lidar. This ensures the reliability of target detection and road recognition even when visibility is obstructed or the environment is dim. Upon entering the operational environment, the road condition perception algorithm acquires sensor information in real time, identifying vehicles and drivable areas. If no drivable area is available ahead, the system provides real-time feedback to the monitoring center, which then uses the intercom voice monitoring system to issue warnings, instructing vehicles on both sides of the road to clear the drivable area. RFID tags are placed at accident-prone intersections. When vehicles pass through intersections, the RFID tag readers mounted on sensor bracket 1 read the tag information, quickly obtaining current road information and shortening rescue time.

[0098] like Figure 1As shown, the robot body includes a robot frame, a three-jaw chuck 2, a robotic arm 3, a sensor bracket 1, a folding plate 4, a rear wing 5, a drive wheel 6, a front wheel frame, and a rear wheel frame. The three-jaw chuck 2 is located at the top of the robotic arm 3 and is connected to a DC high-torque geared motor a, which enables 360-degree rotation. In autonomous driving, the three-jaw chuck 2 holds the steering wheel, and the control system controls the DC high-torque geared motor to drive the three-jaw chuck 2 to control the steering wheel.

[0099] Figure 2 The intercom voice monitoring system of the present invention can be used for voice broadcasting and reminders. Figure 3 This is a schematic diagram of the autonomous driving posture of the present invention, such as... Figure 3 As shown, at this time, the rear wing rotates to its upper limit, and sensor bracket 1 senses the road conditions in a forward orientation. Figure 4 This is a schematic diagram of the initial independent walking posture of the present invention. At this time, the three-jaw chuck 2 is used to fix the display screen and display the current road information in real time according to the traffic network to direct traffic. Figure 5 This is a schematic diagram of the posture after independent walking adjustment according to the present invention. By adjusting the sensor bracket 1, the three-jaw chuck 2, and the robotic arm 3, the posture can be adjusted from... Figures 3 to 4 The adjustment is as follows: In autonomous driving mode, the three-jaw chuck 2 is used to control the steering wheel rotation, with a torque T = 35 Nm. In independent walking mode, it is used to fix the display screen showing the current road conditions and direct traffic. The robotic arm 3 includes a first link 31 and a second link 32. The second end of the first link 31 and the first end of the second link 32 are rotatably connected. The first link 31 and the second link 32 have the same length. The first end of the first link 31 is fixedly connected to the three-jaw chuck 2, and the second end of the second link 32 is fixedly connected to the robot frame. The height of the robotic arm can be adjusted and locked according to the work space. The sensor bracket 1 is slidably connected to the first link 31. The sensor bracket 1 is equipped with a vision sensor module, a millimeter-wave radar module, and a lidar module. The sensor bracket 1 can slide and adjust on the first link 31 to meet different working methods. The folding plate 4 is rotatably connected to the front wheel frame, and two servo motor cylinders 101 and 102 are fixed on it. When the vehicle is autonomously driven, the folding plate 4 flips to the lower limit position to control the accelerator and brake. Pressure sensors are installed on the accelerator and brake pedals, providing high precision and speed. The thrust F = 350N, and the full stroke of the accelerator and brake can be achieved within 0.8s. When traveling independently, the folding plate 4 flips to the upper limit position, the drive wheels fold to the lower limit position, and the vehicle travels independently. The rear wing 5 is rotatably connected to the rear wheel frame. The drive wheels 6 include two front drive wheels 61 and 62 and two rear drive wheels 63 and 64. The front drive wheels are connected to DC joint motors 103 and 104 for synchronous steering control. The rear drive wheels are connected to DC hub motors 105 and 106. The maximum speed is 10km / h.

[0100] Figure 6 The diagram shown illustrates the topology of this invention. The topology consists of a main control chip that transmits and receives various signals and protocols, including CAN signals from DC joint motors 103 and 104, and DC high-torque geared motor a; CAN signals from the drivers of DC hub motors 105 and 106; and CAN signals from the drivers of servo motor cylinders 101 and 102. The data prediction stage runs online on the main control chip. After reading and saving the weight file obtained from the data training module, it acquires the current road condition image of the robot's location in real time from the vision sensor. It then calls the prediction program function to use the weight file for real-time calculation, predicting and classifying the road conditions. After obtaining the road condition label results, it determines the robot's direction of travel, which is then further processed by the controller algorithm. The signal receiver receives SBUS signals, and the communication expansion unit has a UART protocol. The communication expansion unit receives data from radar and cameras. The information from the main control chip is output to the control system to control the mechanical structure.

[0101] Figure 7 The diagram shows the sensor arrangement in the sensor bracket of this invention. To ensure safety, each area requires two or more sensors for mutual verification. The vision sensors are installed with four surround-view cameras around the perimeter, two monocular cameras for front-view, and one rear-view camera. The millimeter-wave radar includes two front and rear forward-facing millimeter-wave radars and four lateral millimeter-wave radars. The two front lateral millimeter-wave radars are arranged at a 45-degree angle to the robot's direction of travel, and the two rear lateral millimeter-wave radars are arranged at a 30-degree angle to the robot's direction of travel. A ranging lidar and a lateral lidar are also included. When encountering environments with obstructed visibility, the lidar can assist in environmental perception to adapt to extreme environments. A reasonable sensor arrangement ensures the accuracy of target recognition and distance measurement.

[0102] Figure 8 This is a schematic diagram of the direction-speed joint control strategy in autonomous driving of a robot. Figure 9 This diagram illustrates the joint direction-speed control scheme for autonomous driving of a robot. The current drivable area is obtained through a road condition perception algorithm, and the centerline of the current drivable area is calculated. Based on the angle between the centerline of the drivable area and the vehicle's direction of travel, the robot's next movement is determined. The steering wheel angle and throttle depth are used as control variables. A PID controller is designed to maintain the vehicle's steering angle following the angle change and to keep the distance from the side of the vehicle to the centerline of the drivable area constant.

[0103] like Figure 10The diagram shows the path planning and driving control algorithm based on PID of this invention. Based on radar information, the current position is determined. If the designated location has been reached, the road is directed via a voice monitoring system to clear the obstruction. After clearing the obstruction, the vehicle returns on a drivable dedicated lane, and the task ends. If there are still evacuation points, the vehicle proceeds to the next evacuation location. A road condition perception algorithm determines whether the vehicle can proceed. If not, the voice monitoring system guides vehicles on both sides to clear the drivable area. Based on the road condition label results predicted by the main control chip, the system determines whether to proceed forward or turn left or right. Then, based on set thresholds, a PID controller is used to control speed and direction.

[0104] Figure 11 The diagram shows a block diagram of a PID closed-loop control system, where u(t) represents the controller output, i.e., the signal quantity between the controller and the actuator. The parameter e represents the error, which is also E in the control system diagram (equal to the desired output minus the actual output).

[0105] Figure 12 The relative position of the robot in the Frenet coordinate system is shown. Figure 13 The diagram illustrates PID longitudinal control of position error. The longitudinal control utilizes dual PID controllers, using the longitudinal position error as the input to the position PID controller, and the speed difference and the output of the position PID controller as the input to the speed PID controller. The output of the speed PID controller and the actual acceleration are combined to form the acceleration used for longitudinal control of the vehicle.

[0106] The specific operation steps of this invention are as follows:

[0107] like Figures 1-12 As shown, the specific operating steps of the traffic emergency robot with independent walking and autonomous driving functions of the present invention are as follows:

[0108] After installing and arranging the robot's communication, perception, and control systems according to the topology diagram and sensor layout diagram, the robot's power system is turned on. This invention communicates with external devices through the communication system and accurately perceives road conditions, identifies targets on the road, extracts lane lines, and segments the drivable area through the perception system. By adjusting the sensor bracket 1, three-jaw chuck 2, robotic arm 3, folding plate 4, and rear wing 5, the invention can switch between autonomous driving and independent walking postures. Multiple sensors are arranged on the sensor bracket 1; adjusting the angle of the sensor bracket 1 leverages the advantages of multiple sensors to achieve accurate road condition perception. The three-jaw chuck 2 engages the vehicle's steering wheel during autonomous driving, controlling the steering wheel through a high-torque DC geared motor a. During autonomous driving, it can also fix the display screen to provide real-time road condition updates. The folding plate 4 is rotatably connected to the front wheel frame, and servo motor cylinders 101 and servo motor cylinders 102 are fixed on it. When the vehicle is autonomously driven, the folding plate 4 flips to the lower limit position to control the throttle and brake. It has high precision and speed, with a thrust F = 350N, and can achieve the full stroke of the throttle and brake within 0.8s. When walking independently, the folding plate 4 flips to the upper limit position. The rear wing 5 is rotatably connected to the rear wheel frame. The drive wheels 6 include two front drive wheels 61 and 62 and two rear drive wheels 63 and 64. The two front drive wheels 61 and 62 are respectively connected to DC joint motors 103 and 104 to synchronously control the steering. The two rear drive wheels 63 and 64 are respectively connected to DC hub motors 105 and 106. The maximum speed is 10km / h.

[0109] The specific steps of the road condition perception algorithm are as follows:

[0110] First, PID control is used to control the longitudinal speed and automatically match the acceleration. Specifically, the difference between the desired speed and the current speed is used as the acceleration signal input. Speed ​​- Speed ​​= Acceleration (ignoring dimensions), which can be viewed as one number minus one number. The goal is to achieve a large acceleration initially when far from the target, gradually decreasing the acceleration as the car approaches the target, and finally zero acceleration upon reaching the target. The planned speed is 10 km / h, so the desired speed is 10 km / h. The initial acceleration is zero, indicating a large initial acceleration. The acceleration gradually decreases during acceleration, reaching zero acceleration at 10 km / h. Before reaching 10 km / h, there is continuous acceleration, thus performing acceleration matching. The formula is:

[0111]

[0112] Where, k p T is the proportionality coefficient. i Let T be the integration time constant. dLet x be the differential time constant. Plan the trajectory with time, assuming initial conditions x(0), y(0), and termination condition x(T), y(x end ), y'(x end ),y"(x end Using these boundary conditions, we can calculate the fifth-degree polynomial (six unknowns) x(t) and y(x) (y(x) represents a requirement for the slope of the curve). By differentiating the intermediate variables, we obtain:

[0113] y(t)=y(x(t))

[0114] y'(t)=y'(x(t))·x'(t)

[0115]

[0116] Then y(t) can be obtained. The planning module calculates a reference trajectory. The robot's actual driving trajectory needs to fit the reference trajectory as closely as possible. For this purpose, the control module needs to calculate the control information of the robot's throttle / brake and turning angle. The output of the lateral LQR controller is the turning angle, and the longitudinal position-speed PID controller is used to calculate the throttle / brake.

[0117] Lateral control:

[0118] S1. Analyze the error of the lateral controller.

[0119] In the Frenet coordinate system, such as Figure 12 As shown, calculate the robot's relative position:

[0120] dx=χ-x des

[0121] dy = yy des

[0122] The x value at time t is given by the trajectory route function. r With y r The value of, i.e.:

[0123] x r (t)=x(t)

[0124] y r (t)=y(t)

[0125] θ r (t)=arctan{y'[x(t)]}

[0126]

[0127] The vehicle's coordinates in the Frenet coordinate system can be obtained using the rotation formula in a two-dimensional coordinate system:

[0128]

[0129] e = d

[0130]

[0131] Where e is the lateral error, and θ is the heading angle error. For the rate of change of lateral error, The rate of change of heading angle error. The heading angle of the robot's current position. Here, v is the heading angle of the path reference point, and v is the robot's current velocity. Let k be the robot's current yaw rate. des The curvature of the path reference point.

[0132] The state variables of a control system can be expressed as:

[0133]

[0134] S2, LQR Lateral Feedback Control

[0135] (1) Establish a state-space model.

[0136] Assuming the state vector is x and the control input vector is u, the state-space model can be represented as:

[0137]

[0138] Here, A and B are constant matrices, representing the system's state transition and input matrices.

[0139] (2) Define the cost function.

[0140]

[0141] Here, Q and R are positive definite matrices, representing the weighting coefficients of the state and the input.

[0142] S3. Calculate the optimal controller.

[0143] The goal of the LQR algorithm is to find an optimal controller that minimizes the cost function. The optimal controller can be represented as a linear state feedback controller:

[0144] u = -Kx

[0145] Where K is the state feedback matrix, which needs to be calculated. Substituting the above controller into the state-space model, we can obtain:

[0146]

[0147] Substituting this into the cost function, we get:

[0148]

[0149] By differentiating the cost function, we can obtain the analytical expression for the state feedback matrix K:

[0150] K = (R + B) T PB) -1 B T PA

[0151] Where P is a positive definite matrix satisfying the algebraic Riccati equation:

[0152] A T P+PA-PBR -1 B T P+Q=0

[0153] The Riccati equation can be obtained through iterative solutions.

[0154] S4. Solving for control variables.

[0155] Based on the state feedback matrix K, the optimal control input can be calculated to achieve optimal lateral control.

[0156] δ=u=-Kx

[0157] Where δ is the calculated rotation angle.

[0158] Vertical control:

[0159] like Figure 13 As shown, the longitudinal position error is calculated as follows:

[0160]

[0161] The speed error is:

[0162]

[0163] The acceleration is:

[0164]

[0165] Vertical control uses dual PID controllers to control speed, i.e., using e s As input to the position PID controller, the speed difference and the output of the position PID controller are used as inputs to the speed PID controller. The output of the speed PID controller and the acceleration are used together as the acceleration to feed the car, thus realizing the speed control of the robot.

[0166] The steering angle obtained above can be used for lateral control, and the acceleration can be used for longitudinal control.

[0167] This invention provides a traffic emergency robot with dual functions of independent walking and autonomous driving. It can be installed in the driver's seat of a vehicle and, in conjunction with special vehicles, realizes functions such as intelligent driving and vehicle-road coordination, making rescue missions safe and rapid. At the same time, multi-sensor fusion technology can achieve high-precision and high-efficiency road condition perception, enabling efficient operation even in environments with obstructed vision, poor signal, or high burial density. In addition, it has the function of independent walking, which can be carried out independently when vehicles cannot enter, to conduct environmental surveys, and can display and broadcast the current road conditions in real time, realizing traffic monitoring, patrol, and command. This is conducive to the smooth conduct of rescue missions, traffic command and supervision, and has the advantages of convenient operation, high efficiency, low cost, and portability.

[0168] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A traffic emergency robot with dual functions of independent walking and autonomous driving, characterized in that: It includes a power system, a communication system, a sensing system, a control system, and a robot body. The communication system includes a wireless communication module, a wired communication interface, and an encoder. The wireless communication module is installed near the main control unit of the robot body, the wired communication interface is installed on the robot body, and the encoder is installed on the robot body and connected to the moving parts of the robot body. The sensing system includes a vision sensor module, a millimeter-wave radar module, a lidar module, and an RFID module. The vision sensor module, millimeter-wave radar module, and lidar module are all installed on the sensor bracket of the robot body. The RFID module includes RFID smart tags and RFID tag readers. The RFID smart tags are placed at the target intersection, and the RFID tag readers are placed on the sensor bracket of the robot body. The control system includes a motor controller, motor sensors, motors, servo drivers, a microcontroller, and a communication expansion unit. The motor controller, servo drivers, and communication expansion unit are all mounted on the microcontroller. The motors include DC servo electric cylinders, DC articulated motors, DC high-torque geared motors, and DC hub motors. The motor sensors are mounted on the motors, and the servo drivers are respectively arranged on two servo motor cylinders. The communication expansion unit connects the vision sensor module, the millimeter-wave radar module, and the lidar module. The robot body includes a robot frame, a three-jaw chuck, a robotic arm, a sensor bracket, a folding plate, a rear wing, a front wheel frame, a rear wheel frame, and drive wheels. The three-jaw chuck is located at the top of the robotic arm and connected to a DC high-torque geared motor. The three-jaw chuck can be locked onto the outside of a vehicle steering wheel. The robotic arm includes a first link and a second link. The first end of the first link is fixedly connected to the three-jaw chuck, and the second end of the first link and the first end of the second link are rotatably connected. The second end of the second link is fixedly connected to the robot frame. The sensor bracket is slidably connected to the first link, and a sensor is mounted on the sensor bracket. The sensing system includes a folding plate rotatably connected to the front wheel frame, with two servo motor cylinders fixed on the folding plate. The rear wing is rotatably connected to the rear wheel frame. The drive wheels include two front drive wheels and two rear drive wheels. The front drive wheels are connected to the DC joint motors, and the rear drive wheels are connected to the DC hub motors. Both the folding plate and the drive wheels have upper and lower limits. When the vehicle is autonomously driven, the folding plate flips to the lower limit to control the throttle and brake, and the drive wheels fold to the upper limit. When the vehicle is moving independently, the folding plate flips to the upper limit, the drive wheels fold to the lower limit, and the vehicle moves independently. When driving an autonomous vehicle, the current drivable area is obtained through a road condition perception algorithm, and the centerline of the current drivable area is calculated. Based on the angle between the centerline of the drivable area and the vehicle's direction of travel, the robot's next movement is determined. The steering wheel angle and throttle depth are used as control variables to make the car's steering angle follow the angle change and maintain a constant distance from the side of the vehicle to the centerline of the drivable area. When independently clearing roads, the system determines its current location based on radar information. If it has reached the designated location, it uses the intercom voice monitoring system to direct road clearing. After clearing the road, it returns on a drivable dedicated lane, thus ending the current road clearing operation. If there are still evacuation points, it moves to the next road clearing location. The system uses a road condition perception algorithm to determine whether it can proceed. If not, it uses the intercom voice monitoring system to guide vehicles on both sides to make way for drivable areas. Based on the road condition label results predicted by the main control chip, it determines whether to proceed, turn left, or turn right, and then controls the speed and direction according to the set thresholds.

2. The traffic emergency robot with independent walking and autonomous driving functions according to claim 1, characterized in that: The vision sensor module includes two monocular cameras, one rear-view camera, and four surround-view cameras.

3. The traffic emergency robot with independent walking and autonomous driving functions according to claim 1, characterized in that: The millimeter-wave radar module includes four lateral radars and two forward radars.

4. The traffic emergency robot with independent walking and autonomous driving functions according to claim 1, characterized in that: The lidar module includes a multi-threaded velocity-measuring lidar and a multi-threaded ranging lidar.

5. The traffic emergency robot with independent walking and autonomous driving functions according to claim 1, characterized in that: The length of the first link is equal to the length of the second link.

6. The traffic emergency robot with independent walking and autonomous driving functions according to claim 1, characterized in that: The sensor bracket and the fixed end of the first connecting rod are provided with a movable buckle.

7. The traffic emergency robot with independent walking and autonomous driving functions according to claim 1, characterized in that: The power system includes a lithium battery module, and the lithium battery module includes a transformer module.

8. An autonomous driving method for a traffic emergency robot with independent walking and autonomous driving functions as described in claim 1, characterized in that: It includes the following steps: S1. Obtain the current drivable area through a road condition perception algorithm; S2. Calculate the centerline of the current drivable area, and determine the robot's next movement based on the angle between the centerline of the drivable area and the direction of the vehicle's travel. S3. Using the steering wheel angle and throttle depth as control variables, the controller outputs execution commands to the actuator based on the PID algorithm to control the motor's movement so as to maintain the car's steering angle following the angle change and to maintain a constant distance between the vehicle side and the centerline of the driving area.

9. An independent walking method for a traffic emergency robot with independent walking and autonomous driving functions as described in claim 1, characterized in that: S1. Determine whether it is possible to proceed based on the road condition perception algorithm. If not, guide vehicles on both sides to give way to the driving area through the intercom voice monitoring system. S2. Based on the road condition label results obtained by the main control chip from the data prediction, determine whether to move forward, turn left or right, and then use the PID algorithm to control the speed and direction according to the set threshold. S3. Determine the current location based on radar information. If the designated location has been reached, use the intercom voice monitoring system to direct traffic. After clearing the road, return through the drivable area. This mission ends. If there are still locations that need to be evacuated, proceed to the next evacuation location.

10. The autonomous driving method for a traffic emergency robot according to claim 8, characterized in that: The specific steps of the road condition perception algorithm are as follows: S1. Plan the motion trajectory with time involved, the specific formula is: Where u(t) is the trajectory of motion with time, and k p T is the proportionality coefficient. i Let T be the integration time constant. d The differential time constant; S2. Use the output of the lateral controller as the steering wheel angle control quantity for lateral control, including the following sub-steps: S21. Analysis of the error of the lateral controller: Calculate the robot's relative position in the Frenet coordinate system: dx=x-x des dy=yy des The x value at time t is given by the trajectory route function. r With y r The value of, that is: x r (t)=x(t) y r (t)=y(t) θ r (t)=arctan{y'[x(t)]} The vehicle's coordinates in the Frenet coordinate system are obtained using the rotation formula for a two-dimensional coordinate system: e = d Where e is the lateral error, and θ is the heading angle error. For the rate of change of lateral error, The rate of change of heading angle error. The heading angle of the robot's current position. Here, v is the heading angle of the path reference point, and v is the robot's current velocity. Let k be the robot's current yaw rate. des The curvature of the path reference point; The state variables of the control system are represented as follows: S22, LQR lateral feedback control, specifically includes the following sub-steps: S221. Establish the state-space model: Assuming the state vector is x and the control input vector is u, the state-space model can be represented as: Where A and B are constant matrices, representing the system's state transition and input matrices; S222. Define the cost function: J=∫0 ∞ (x T Qx+u T Ru)dt Where Q and R are positive definite matrices, representing the weighting coefficients of the state and the input; S23. Calculate the optimal controller: The optimal controller is represented as a linear state feedback controller: u = -Kx Where K is the state feedback matrix, substituting the above controller into the state-space model, we get: Substituting into the cost function, we get: J=∫0 ∞ (x T Qx+x T K T RKx)dt By differentiating the cost function, the analytical expression for the state feedback matrix K is obtained: K=(R+B T PB) -1 B T PA Where P is a positive definite matrix satisfying the algebraic Riccati equation: A T P+PA-PBR -1 B T P+Q=0 The Riccati equations are obtained through iterative solutions. S24. Solve for the control variables: Based on the state feedback matrix K, the optimal control input is calculated to achieve optimal lateral control. δ=u=-Kx Where δ is the calculated rotation angle; S3. Use a longitudinal position-speed PID controller to calculate throttle depth for longitudinal control: The longitudinal position error is calculated as follows: The speed error is: The acceleration is: Longitudinal control utilizes dual PID control for speed, i.e., using e s As inputs to the position PID, the speed difference between the desired speed and the current speed, along with the output of the position PID, are used as inputs to the speed PID. The output of the speed PID, together with the acceleration, is used as the acceleration input.

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