Intelligent road cone cluster cooperative control method based on beidou positioning

Abstract

This invention discloses a collaborative control method for intelligent traffic cone clusters based on BeiDou positioning. The method includes a host control platform sending a curved formation path to a cluster of multiple intelligent traffic cones; the intelligent traffic cones within the cluster establishing a self-organizing network through communication modules and exchanging their pose and motion intention information using a distributed control architecture; each intelligent traffic cone using a BeiDou positioning module to obtain its real-time position, tracking the curved formation path as the target trajectory, and adjusting its steering based on the lateral deviation between its real-time position and the target trajectory. In this invention, the host platform transforms the road closure task into an executable curved formation path; the traffic cone cluster achieves distributed state sharing through a self-organizing network, performs path tracking using BeiDou positioning, and achieves coupled control of path following and collision avoidance through vector superposition. This invention enables unmanned autonomous placement of traffic cone clusters, eliminating the need for manual handling and reducing the operational risks for personnel in the driving lane.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology, specifically relating to an intelligent road cone cluster collaborative control method based on BeiDou positioning. Background Technology

[0002] Currently, in road construction, accident handling, and temporary traffic control scenarios, traditional traffic cones are typically placed manually along the edge of the planned road closure area to create a closed or warning zone. However, traditional traffic cones lack mobility and sensing capabilities, relying entirely on manual handling and placement. This not only results in low operational efficiency but also poses significant safety risks, especially on high-risk and complex road sections such as highways, curves, and national and provincial highways, where personnel frequently need to cross the driving lanes. In recent years, although some electric intelligent traffic cone products have emerged, most still rely on manual or semi-automatic deployment methods. The cones themselves cannot move autonomously, nor can they achieve coordinated control among clusters or precise tracking of curved paths. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies, which lack a control method that enables multiple intelligent traffic cones to move autonomously, coordinate their placement along a curved formation path, and achieve dynamic obstacle avoidance, resulting in the need for a large amount of manual intervention in road closure operations. This invention provides a BeiDou positioning-based intelligent traffic cone cluster collaborative control method that enables multiple intelligent traffic cones to move autonomously, coordinate their placement along a curved formation path, and achieve dynamic obstacle avoidance.

[0004] This invention is achieved through the following technical solution:

[0005] A BeiDou-based intelligent traffic cone cluster collaborative control method includes:

[0006] S1. The upper-level control platform generates a curved formation path based on the geometric data of the closed road area and sends the path to a cluster of multiple smart cones. Each smart cone has a mobile chassis, a Beidou positioning module, an environmental perception module, a communication module, and a controller.

[0007] S2. Multiple intelligent traffic cones within the cluster establish a self-organizing network through a communication module and exchange their pose and motion intention information using a distributed control architecture.

[0008] S3. After each smart traffic cone receives the curved formation path and binds it to one of the target pose points, it uses the Beidou positioning module to obtain its own real-time position, performs path tracking with the curved formation path as the target trajectory, and adjusts its own steering according to the lateral deviation between the real-time position and the target trajectory.

[0009] S4. Each smart cone independently plans its own motion parameters based on neighborhood information and the curved formation path, so that the cluster as a whole moves synchronously along the curved formation path, and maintains a safe distance between adjacent smart cones without collision.

[0010] S5. During the driving process, each smart cone detects obstacles ahead in real time through the environmental perception module; if an obstacle is detected, it will either avoid it or stop, and broadcast the obstacle information to other smart cones in the cluster.

[0011] S6. Once each smart traffic cone reaches its final parking position on the curved formation path, each smart traffic cone stops moving and activates its warning device, causing the entire cluster to present a curved shape consistent with the curved formation path, thus completing autonomous placement.

[0012] A two-layer architecture is adopted, combining global planning by a higher-level platform with distributed collaborative execution of traffic cones. The higher-level platform transforms the road closure task into an executable curved formation path; the traffic cone cluster achieves distributed state sharing through a self-organizing network, uses BeiDou positioning for path tracking, and achieves coupled control of path following and collision avoidance through vector overlay. This enables unmanned autonomous placement of traffic cone clusters, eliminating the need for manual handling and removing the risks associated with personnel operating within the driving lane.

[0013] Furthermore, step S1 specifically includes:

[0014] S1.1 The upper-level control platform acquires the geometric data of the closed road area;

[0015] Based on geometric data, the S1.2 upper control platform generates a curved formation path that matches the contour of the closed area of ​​the road along the road edge. The curved formation path includes several target pose points.

[0016] The S1.3 upper control platform sends the coordinates of all target pose points to a cluster of multiple intelligent road cones in the form of instructions;

[0017] Within the S1.4 cluster, each smart traffic cone receives the coordinate values ​​of all target pose points through its own communication module and binds its unique identifier to one of the target pose points, enabling each smart traffic cone to obtain its corresponding final parking position.

[0018] Using cubic spline interpolation, discrete geometric parameters, including start point, end point, radius of curvature, and offset, are transformed into a continuous and traceable sequence of pose points. By binding each target pose point with an identity identifier, the global task is decomposed into independently executable subtasks.

[0019] Furthermore, step S2 specifically includes:

[0020] S2.1 Each smart cone in the cluster broadcasts its own identity identifier through its own communication module at a preset channel frequency, and simultaneously listens for the identity identifiers broadcast by other smart cones. The neighbor list in the cluster is determined based on the number of identity identifiers received.

[0021] S2.2 Each smart cone establishes a point-to-point communication link with each neighbor smart cone in the neighbor list, forming a fully distributed self-organizing network;

[0022] S2.3 Each smart traffic cone broadcasts its current pose information and motion intention information to all neighboring smart traffic cones through a self-organizing network according to a preset time period;

[0023] S2.4 Each smart cone simultaneously receives pose information and motion intention information broadcast by neighboring smart cones, and stores the received information in a local cache for subsequent motion planning.

[0024] Based on IEEE 802.15.4 / ZigBee or LoRa, short-range self-organizing networks discover neighbors by periodically broadcasting identity identifiers and establish point-to-point links using TCP three-way handshakes. No central node or infrastructure is required, and the cluster can quickly self-organize. Communication and positioning sampling frequencies are synchronized to ensure the real-time performance of the control loop. The dynamic neighbor discovery and removal mechanism adapts to topology changes.

[0025] Furthermore, step S3 specifically includes:

[0026] S3.1 After receiving the curved formation path, each intelligent road cone uses the complete curved formation path as a reference and searches for the target pose point and its neighboring points that are closest to its current position in real time during the driving process, forming a dynamic local tracking window and forming the local target trajectory of each intelligent road cone.

[0027] S3.2 Each smart traffic cone obtains its current real-time location through its own BeiDou positioning module at a preset frequency;

[0028] S3.3 Each intelligent traffic cone searches for the target pose point closest to its own real-time position in the local target trajectory and uses that target pose point as the current tracking target point;

[0029] S3.4 Each intelligent traffic cone generates a speed control command to move itself toward the current tracking target point based on the positional relationship between its real-time position and the current tracking target point, thereby driving the mobile chassis to travel;

[0030] S3.5 Each smart cone projects its real-time position onto the curved formation path to obtain the projection point, and calculates the distance from the real-time position to the projection point as the lateral deviation.

[0031] S3.6 Each smart cone calculates the difference between its own real-time heading angle and the target heading angle at the projection point, which is used as the heading deviation;

[0032] S3.7 Each intelligent traffic cone inputs its lateral deviation and heading deviation into its own controller, which uses a PID control algorithm to output steering adjustment.

[0033] S3.8 Each intelligent traffic cone controls its own moving chassis to perform steering actions based on the steering adjustment amount.

[0034] A pure tracking algorithm is used to generate basic speed commands, combined with projection-PID for lateral correction. Pure tracking generates linear / angular velocity by calculating the direction angle and distance to the nearest path point; the PID controller takes lateral and heading deviations as inputs and outputs steering adjustment, distributing left and right wheel speeds differentially.

[0035] Furthermore, step S4 specifically includes:

[0036] S4.1 Each smart traffic cone obtains its own status and the status of its neighbors through local caching;

[0037] S4.2 Each intelligent road cone extracts the target pose point corresponding to the current moment and the expected target pose point at the next moment from the local target trajectory, as the reference input for path tracking;

[0038] S4.3 Each intelligent road cone calculates its path tracking speed based on the deviation between its current real-time position and the target pose point corresponding to the current moment, and records it as the first speed. The first speed is used to drive the intelligent road cone to approach the curved formation path.

[0039] S4.4 Each smart cone calculates the current distance between itself and each neighboring smart cone based on the pose information of all neighboring smart cones, and determines whether the current distance is less than the preset safe distance threshold; if so, it calculates the avoidance speed to increase the spacing and records it as the second speed; if not, it sets the second speed to zero.

[0040] S4.5 Each intelligent traffic cone vector superimposes the first velocity and the second velocity to generate its own desired velocity vector, and outputs the desired velocity vector as its motion parameter for the next moment to the mobile chassis for execution.

[0041] Motion planning is decoupled into the superposition of velocity vectors of two orthogonal targets: path tracking velocity (approaching the target point) and avoidance velocity (moving away from overly close neighbors). Path tracking and collision avoidance are decoupled and optimized independently before being superimposed, avoiding the complexity of multi-target optimization; at the same time, it ensures that the cluster moves synchronously along the path and that there are no collisions between individuals.

[0042] Furthermore, step S5 specifically includes:

[0043] S5.1 During the driving process, each smart traffic cone collects detection data within a set angle range ahead through its own configured environmental perception module at a preset detection frequency;

[0044] S5.2 Each smart traffic cone identifies whether there is an obstacle ahead based on the detection data. If there is, it extracts the position coordinates and relative speed of the obstacle.

[0045] S5.3 Each smart traffic cone calculates the collision time between itself and the obstacle based on its current real-time position, real-time speed, and the position coordinates and relative speed of the obstacle.

[0046] S5.4 Each smart traffic cone will have its collision time TTC compared to a preset first threshold. Second threshold Comparison, among which The comparison rule is: if If there is no immediate danger, the current driving state will be maintained; if If a potential collision risk is identified, a deceleration and avoidance maneuver is initiated, and a new local path is planned to bypass the obstacle; if If an emergency collision is detected, braking should be initiated immediately to bring the vehicle to a stop.

[0047] S5.5. When each smart cone detects an obstacle and performs an avoidance or stopping action, it broadcasts the obstacle information to all other smart cones in the cluster through the self-organizing network established in step S3.

[0048] S5.6 After the intelligent traffic cone performs a braking and stopping action, it sends a waiting instruction request to the upper control platform through a self-organizing network. After receiving the waiting instruction request, the upper control platform records the status of the traffic cone. At the same time, the intelligent traffic cone continuously detects obstacles ahead through the environmental perception module.

[0049] A three-level hierarchical decision-making model based on collision time-to-traffic (TTC) corresponds to three response strategies: hold, decelerate and detour, and brake to stop. The dynamic window algorithm samples collision-free trajectories in the velocity space. This avoids the conservative strategy of indiscriminate emergency braking and improves traffic efficiency. The combination of hierarchical response and local path replanning balances safety and continuity.

[0050] Furthermore, step S5.6 specifically includes:

[0051] If the obstacle is detected to have disappeared within the preset waiting time, the intelligent traffic cone will use the target pose point closest to the current real-time position as the recovery point, and the recovery point as the new tracking target. Following the path tracking method in step S3 and the motion parameter planning method in step S4, it will re-plan its own motion parameters, drive itself to the recovery point, and continue to travel along the curved formation path to the subsequent target pose point.

[0052] If the obstacle does not disappear within the preset waiting time, the smart traffic cone will report the anomaly to the upper control platform through the self-organizing network. The anomaly information includes the traffic cone's identification, current location, and the duration of the obstacle's presence. The smart traffic cone will remain stopped and report the anomaly again at intervals until it receives a recovery command or manual intervention command from the upper control platform.

[0053] After braking to a stop, the traffic cone continuously monitors the obstacle status and has a waiting timeout mechanism. Once the obstacle disappears, it is reinstated into the tracking system using the nearest path point as the recovery point. If the obstacle does not disappear within the timeout period, an anomaly is reported requesting manual intervention. This achieves automatic recovery from an abnormal state to normal operation, reducing manual intervention. The anomaly reporting mechanism prevents the traffic cone from waiting indefinitely, ensuring system controllability.

[0054] Furthermore, step S6 specifically includes:

[0055] S6.1 Each smart traffic cone continuously determines whether its current real-time location has reached its bound final parking location during the driving process;

[0056] S6.2 When the intelligent traffic cone determines that it has reached the final parking position, the intelligent traffic cone sends a braking command to its own moving chassis, so that the linear speed drops to zero and the movement stops;

[0057] S6.3 The smart traffic cone broadcasts its own positioning status information to other smart traffic cones in the cluster through the self-organizing network established in step S2.

[0058] S6.4 Each smart traffic cone activates its own warning device after confirming that it is in place and receiving status information that all other smart traffic cones in the cluster are in place.

[0059] A distributed consensus mechanism is used to implement synchronous alerts. Each traffic cone independently determines its own arrival status and broadcasts its arrival information through a self-organizing network; each traffic cone maintains a global arrival array and only initiates an alert when it is in position and has received arrival information from all other traffic cones; a timeout re-query mechanism is set to ensure reliability.

[0060] Furthermore, in step S6.1, when the distance between the current position of a smart road cone and its final parking position is less than a preset arrival threshold and the heading deviation is less than a preset heading threshold, it is determined that the final parking position has been reached.

[0061] A dual-threshold approach, using position and heading, is employed for accuracy. The position threshold ensures spatial positioning, while the heading threshold ensures attitude alignment. Both conditions must be met for a traffic cone to be considered present, preventing it from stopping in the correct position but facing the wrong direction. This dual-threshold approach ensures that the traffic cone not only reaches the correct position but also stops in the correct attitude, guaranteeing that the final curved shape matches the preset path.

[0062] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for collaborative control of intelligent traffic cone clusters based on BeiDou positioning is implemented.

[0063] Compared with the prior art, the beneficial effects of the present invention are:

[0064] This invention employs a two-layer architecture combining global planning from a higher-level platform with distributed collaborative execution of traffic cones. The higher-level platform transforms the road closure task into an executable curved formation path; the traffic cone cluster achieves distributed state sharing through a self-organizing network, uses BeiDou positioning for path tracking, and achieves coupled control of path following and collision avoidance through vector overlay. This invention enables unmanned autonomous placement of traffic cone clusters, eliminating the need for manual handling and removing the risks associated with personnel operating within the driving lane. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the path tracking and anti-deviation process of the present invention;

[0066] Figure 2 This is a schematic diagram of the obstacle detection and hierarchical decision-making process of the present invention;

[0067] Figure 3 This is a schematic diagram of the arrival judgment and synchronous start-up warning process of the present invention. Detailed Implementation

[0068] To make the invention clearer and easier to understand, a further detailed description is provided in conjunction with the accompanying drawings and specific embodiments:

[0069] Example 1

[0070] This invention provides a method for intelligent traffic cone cluster cooperative control based on BeiDou positioning, such as... Figure 1-3 As shown, it includes the following steps:

[0071] S1. The upper control platform generates a curved formation path based on the geometric data of the closed road area and sends the path to a cluster of multiple smart cones. Each smart cone has a mobile chassis, a Beidou positioning module, an environmental perception module, a communication module, and a controller.

[0072] Specifically, it includes:

[0073] S1.1 The upper-level control platform acquires the geometric data of the closed road area;

[0074] This geometric data is input by the operations management personnel based on the actual location of the construction or accident site, and includes at least the starting coordinates and ending coordinates of the closed area, the radius of curvature of the curve, the offset of the road centerline, and the preset cone spacing. The starting and ending coordinates are expressed in latitude and longitude, the radius of curvature of the curve is used to describe the degree of road curvature, and the cone spacing is the arc distance along the path between the target positions of two adjacent cones.

[0075] Based on geometric data, the S1.2 upper-level control platform generates a curved formation path along the road edge that matches the contour of the closed area of ​​the road. The curved formation path includes several target pose points; the specific generation method is as follows:

[0076] The aforementioned starting coordinates are taken as the starting point of the curved formation path, and the aforementioned ending coordinates are taken as the ending point of the curved formation path. Between the starting and ending points, a series of continuous target pose points are generated using a cubic spline interpolation algorithm according to the curvature radius of the curve and the offset of the road centerline. The line connecting all the target pose points forms the curved formation path. The method of generating a series of continuous target pose points using the cubic spline interpolation algorithm is existing technology and will not be described in detail here. Let the total number of target pose points be N, and each target pose point is represented as:

[0077] ;

[0078] in, , Let the coordinates of the target pose point be in the planar coordinate system. The heading angle at that point is obtained by calculating the azimuth angle of the line connecting two adjacent points. The curved formation path is formed by arranging these N target pose points in sequence.

[0079] The S1.3 upper-level control platform sends the curved formation path as instructions to a cluster of multiple intelligent traffic cones. Each intelligent traffic cone is pre-configured with a mobile chassis, a Beidou positioning module, an environmental perception module, and a communication module.

[0080] The mobile chassis is a two-wheel differential drive chassis with a universal wheel at each end, supporting forward, backward, and on-the-spot turning; the Beidou positioning module is a receiver that supports RTK differential positioning, which can achieve centimeter-level positioning; the environmental perception module includes at least one forward-facing lidar or monocular camera; the communication module is a dual-mode communication unit that supports 4G / 5G and local self-organizing networks.

[0081] The upper control platform is connected to each smart traffic cone via a 4G or 5G wireless communication network. The instruction contains the coordinate values ​​of all target pose point sequences on the curved formation path and the number M of smart traffic cones in the cluster, where M equals N, that is, each smart traffic cone is bound to a unique target pose point in the curved formation path as the final parking position of the smart traffic cone.

[0082] Within the S1.4 cluster, each smart traffic cone receives the coordinate values ​​of all target pose points through its own communication module and binds its unique identifier to one of the target pose points, enabling each smart traffic cone to obtain its corresponding final parking position.

[0083] The specific binding method is as follows: When the host control platform sends instructions, it sorts the smart road cones by their identification identifiers from smallest to largest, and sequentially binds the target pose points. Assigned to traffic cone 1, Assigned to traffic cone 2, ..., The target pose point is assigned to each smart traffic cone N. After receiving the instruction, each smart traffic cone extracts the target pose point assigned to it as its final parking position and stores it in its local memory.

[0084] S2. Multiple smart traffic cones within the cluster establish a self-organizing network through communication modules and exchange their pose and motion intention information using a distributed control architecture.

[0085] Each smart traffic cone's communication module supports short-range ad hoc network communication. The communication protocol uses the ZigBee protocol based on the IEEE 802.15.4 standard or LoRa modulation technology, operating at a frequency of 2.4 GHz or 470 MHz. Each smart traffic cone performs the following operations through this communication module:

[0086] S2.1 Each smart traffic cone in the cluster broadcasts its own identity identifier through its own communication module at a preset channel frequency, and simultaneously listens for the identity identifiers broadcast by other smart traffic cones. The neighbor list in the cluster is determined based on the number of identity identifiers received.

[0087] The identification identifier is a unique number burned into the device at the factory, with a length of 4 bytes. The broadcast interval is 0.5 seconds, the broadcast power is 20dBm, and the coverage radius is no less than 50 meters. After listening to 5 consecutive broadcast cycles, each smart cone collects the identification identifiers of other smart cones and determines its neighbor list within the communication range based on the number of received identification identifiers. Let the neighbor list of the i-th smart cone be:

[0088] ;

[0089] S2.2, Each smart traffic cone is based on its neighbor list. It establishes point-to-point communication links with each neighboring smart cone in the list, forming a fully distributed self-organizing network.

[0090] The link establishment process employs the TCP three-way handshake mechanism. For each smart road cone j in the neighbor list, smart road cone i sends a connection request to j. After j confirms, it returns an acknowledgment, and i confirms again, completing the bidirectional link establishment. Once all links are established, all smart road cones within the cluster form a fully distributed self-organizing network. This network has no central node; any two smart road cones capable of communicating with each other have a direct link. For nodes that cannot communicate directly, indirect communication is achieved through multi-hop forwarding via intermediate nodes. The multi-hop routing is maintained using the On-Demand Distance Vector (AODV) protocol.

[0091] S2.3 Each smart traffic cone broadcasts its current pose information and motion intention information to all neighboring smart traffic cones through a self-organizing network according to a preset time period.

[0092] The preset time period is 0.1 seconds, consistent with the sampling frequency of the BeiDou positioning module. Pose information includes: current position coordinates. and current heading angle The coordinates are converted to Cartesian coordinates using the Gauss-Kruger projection. Motion intent information includes: current velocity. and the expected speed at the next moment The desired velocity is given by the motion planning in step S4. The data packet format of the broadcast information is shown in Table 1 below:

[0093] Table 1. Data packet format for broadcast information

[0094]

[0095] S2.4 Each smart cone simultaneously receives pose information and motion intention information broadcast by neighboring smart cones, and stores the received information in a local cache for subsequent motion planning.

[0096] The reception process employs a non-blocking mode. Each smart cone maintains a receive buffer. When a complete data packet is received, the aforementioned fields are parsed and stored in a local cache. The local cache uses a circular queue structure, storing a maximum of the last 10 cycles of neighbor data. The information in the cache is indexed by the source node ID. The data record corresponding to each source node includes: the most recently received position, heading angle, current speed, expected speed, and the corresponding timestamp.

[0097] If a neighbor fails to send any data for three consecutive cycles (0.3 seconds), the communication with that neighbor is considered interrupted. The smart traffic cone removes the neighbor from the neighbor list and triggers the neighbor rediscovery process. When a new neighbor is discovered, the link is established following the same steps.

[0098] Through the above process, each smart cone within the cluster can obtain the pose and motion intention information of all neighboring smart cones within its communication range in real time. This information will be used by each smart cone to independently plan its own motion parameters, thereby achieving synchronized movement and collision-free collaboration of the entire cluster.

[0099] S3. After receiving the curved formation path, each intelligent traffic cone uses its BeiDou positioning module to obtain its real-time position, tracks the curved formation path as the target trajectory, and adjusts its steering based on the lateral deviation between its real-time position and the target trajectory. Figure 1 .

[0100] S3 uses a curved formation path as the target trajectory for path tracking, specifically including:

[0101] S3.1 After receiving the curved formation path, each intelligent traffic cone uses the complete curved formation path as a reference, and each intelligent traffic cone corresponds to a final target pose point. During the journey, it continuously searches for the nearest path point and its neighboring points to its current position, forming a dynamic local tracking window. A window is slid along the complete path with the current position of the intelligent traffic cone as the center for real-time path tracking control, forming the local target trajectory of each intelligent traffic cone.

[0102] The local target trajectory of the intelligent road cone includes a sequence of multiple consecutive target pose points, denoted as:

[0103] ;

[0104] in, Let L represent the j-th target pose point of the i-th smart road cone, and L be the total number of target pose points contained in the local target trajectory of the road cone.

[0105] S3.2 Each smart traffic cone obtains its current real-time location through its own BeiDou positioning module at a preset frequency.

[0106] The preset frequency is set to 10Hz, meaning it acquires data every 0.1 seconds. The real-time location is denoted as:

[0107] ;

[0108] in, , Let be the plane coordinates at time t. Let be the heading angle at time t.

[0109] S3.3 Each intelligent traffic cone searches for the target pose point closest to its own real-time position in the local target trajectory and uses that target pose point as the current tracking target point.

[0110] The distance calculation formula is:

[0111] ;

[0112] Choose to The target pose point corresponding to the smallest j is taken as the current tracking target point, denoted as j. .

[0113] S3.4 Each intelligent traffic cone generates a speed control command to move itself toward the current tracking target point based on the positional relationship between its real-time location and the current tracking target point, thereby driving the mobile chassis to travel.

[0114] The speed control command includes linear velocity v(t) and angular velocity ω(t), which are calculated in the following way:

[0115] Set real-time location Point to the current tracking target point The vector is The distance between the two is The linear velocity command is:

[0116] ;

[0117] in, The maximum speed of the intelligent traffic cone. This is the preset deceleration distance threshold, usually set to 1 meter.

[0118] The angular velocity command is obtained by calculating the difference between the real-time heading angle and the target heading angle. A pure tracking control algorithm is used here, and the formula is:

[0119] ;

[0120] in, This is the angle between the real-time heading angle and the target heading angle. The wheelbase of the moving chassis is specified. Linear velocity and angular velocity commands are sent to the drive motors of the moving chassis, causing the road cone to move towards the currently tracked target point. When... When the distance is less than 0.05 meters, it is determined that the target pose point has been reached. Then, the next target pose point in the local target trajectory is switched as the new current tracking target point, and the above process is repeated.

[0121] In S3, the steering is adjusted based on the lateral deviation between the real-time position and the target trajectory, specifically including:

[0122] S3.5 Each smart cone projects its real-time position onto the curved formation path to obtain the projection point, and calculates the distance from the real-time position to the projection point as the lateral deviation.

[0123] The method for calculating the projection point is as follows: In the continuous sequence of target pose points along the curved formation path, find the two adjacent target pose points closest to the real-time position. Draw a perpendicular line from the real-time position to the line segment defined by these two points; the foot of the perpendicular is the projection point. Let the coordinates of the projection point be ( , The target heading angle at the projection point is... The target heading angle The lateral deviation is obtained by linear interpolation of the heading angles of two adjacent points. The distance from the real-time position to the projected point is calculated as the lateral deviation.

[0124] ;

[0125] The sign of the lateral deviation is determined by whether the real-time position is on the left or right side of the path. It is defined that it is positive when it is on the right side of the path and negative when it is on the left side.

[0126] S3.6 Each smart cone calculates the difference between its own real-time heading angle and the target heading angle at the projection point, which is used as the heading deviation;

[0127] Real-time heading angle Target heading angle at the projection point The difference is taken as the heading deviation:

[0128] ;

[0129] Normalize the heading deviation to Interval.

[0130] S3.7, Each smart traffic cone will have a lateral deviation and heading deviation The input is fed into its pre-configured controller. The controller employs a proportional-integral-derivative (PID) control algorithm. The controller's output is the steering adjustment amount. The calculation formula is as follows:

[0131] ;

[0132] in, , , These are the proportional, integral, and differential coefficients of the heading deviation, respectively. This represents the proportionality coefficient for lateral deviation. The values ​​of each coefficient were obtained through actual vehicle calibration, and the typical range is as follows: =0.5, =0.01, =0.1, =0.3.

[0133] S3.8 Each intelligent traffic cone adjusts according to the steering angle. The chassis controls its own movement to perform steering actions. For a differential drive chassis, the steering adjustment is converted into the speed difference between the left and right wheels. Specifically, let the desired linear velocity be... Then the speed of the left wheel and right wheel speed for:

[0134] ;

[0135] By adjusting the speeds of the left and right wheels, the actual trajectory of the traffic cones is made to continuously approach the curved formation path, ultimately reducing the lateral deviation. and heading deviation Approaching zero, thus achieving anti-deviation control.

[0136] S4. Each smart cone independently plans its own motion parameters based on neighborhood information and the curved formation path, so that the cluster moves synchronously along the curved formation path, and adjacent smart cones maintain a safe distance without collision.

[0137] Specifically, it includes:

[0138] S4.1 Each smart traffic cone obtains its current real-time position from the local cache. Real-time heading angle and real-time speed Simultaneously, the system reads the pose and motion intention information of all neighboring smart cones from the local cache. For each neighbor... Obtain its location Heading angle Current speed and expected speed .

[0139] S4.2 Each intelligent road cone extracts the target pose point corresponding to the current moment and the expected target pose point at the next moment from the local target trajectory, as the reference input for path tracking.

[0140] The local target trajectory of the i-th smart road cone is In each control cycle, the traffic cone adjusts its position based on its current real-time location. Find the nearest target pose point in the target position and record it as the tracking point at the current moment. That is, the target pose point at the current moment is... Simultaneously, retrieve the next path point from that point. As the expected target pose point in the next moment These two points serve as reference inputs for path tracing.

[0141] S4.3 Each intelligent traffic cone calculates its path tracking speed based on the deviation between its current real-time position and the target pose point corresponding to the current moment, and records it as the first speed. The first speed is used to drive the intelligent traffic cone to approach the curved formation path.

[0142] Define the position deviation vector:

[0143] ;

[0144] The magnitude of the vector is:

[0145] ;

[0146] Simultaneously define the heading deviation:

[0147] ;

[0148] Normalization to The direction angle of the first velocity vector Let the direction angle be the angle from the current position to the current target pose point:

[0149] ;

[0150] Where atan2(y, x) is the two-parameter arctangent function;

[0151] Magnitude of the first velocity vector Proportional control is adopted:

[0152] ;

[0153] in, The maximum speed of the intelligent traffic cone is 1 meter per second. and These are proportionality coefficients, with typical values ​​of 0.5 and 0.2 respectively. When the distance is long, the first velocity is... The upper limit is given. Therefore, the first velocity vector is expressed as:

[0154] ;

[0155] S4.4 Each smart cone calculates the current distance between itself and each neighboring smart cone based on the pose information of all neighboring smart cones, and determines whether the current distance is less than a preset safe distance threshold; if so, it calculates the avoidance speed used to increase the spacing, which is recorded as the second speed; if not, it sets the second speed to zero; the second speed is used to avoid collisions between adjacent smart cones.

[0156] Let the current position of road cone i be... The position of neighbor j is The real-time straight-line distance between road cone i and its neighbor j is... It can be expressed in terms of Euclidean distance as:

[0157] ;

[0158] Preset safe distance threshold The typical value is 1.5 meters. If If so, there is no need to avoid it, and the second velocity is set to zero vector. If Then calculate the avoidance speed used to increase the clearance. The direction of the avoidance speed is the direction away from the neighbor j, that is, the direction from the neighbor towards itself, and the direction angle of the avoidance speed is... :

[0159] ;

[0160] The magnitude of the avoidance speed is inversely proportional to the distance. The second speed, i.e., the avoidance speed, is calculated using the following formula. :

[0161] ;

[0162] in, To avoid the strength coefficient, a value of 0.8 is taken; The minimum allowable distance is set at 0.3 meters. hour, Pick Ultimately, the second velocity vector is the sum of the vectors contributed by all neighbors:

[0163] ;

[0164] S4.5 Each intelligent traffic cone vector superimposes the first velocity and the second velocity to generate its own desired velocity vector, and outputs the desired velocity vector as its motion parameter for the next moment to the mobile chassis for execution.

[0165] Each smart traffic cone vector-wise superimposes its first velocity and second velocity to generate its own desired velocity vector:

[0166] ;

[0167] To prevent the speed from exceeding the maximum limit, the amplitude of the composite vector is limited:

[0168] ;

[0169] Desired velocity vector Includes the expected linear velocity. and the expected heading angle The desired velocity vector is output as the motion parameter for the next moment to the mobile chassis for execution. Based on the desired linear velocity and desired heading angle, the mobile chassis drives the motors via the underlying motion controller to achieve actual movement.

[0170] Through the above independent planning, each smart traffic cone in the cluster actively adjusts its speed according to the real-time status of its neighbors while following the curved formation path, thereby ensuring that the cluster as a whole moves synchronously along the curved formation path and that adjacent smart traffic cones always remain at a safe distance.

[0171] S5. During operation, each smart cone detects obstacles ahead in real time via its environmental perception module. If an obstacle is detected, it either avoids it or stops, and broadcasts the obstacle information to other smart cones in the cluster. Figure 2 Specifically, this includes:

[0172] S5.1. During operation, each intelligent traffic cone collects detection data within a preset angle range ahead using its own environmental perception module at a pre-defined detection frequency. The environmental perception module is a forward-facing lidar with a detection frequency of 20 Hz, a preset angle range of 120 degrees horizontally ahead, and a detection distance range of 0.1 meters to 30 meters. The lidar outputs point cloud data, with each point containing its distance and azimuth relative to the traffic cone itself.

[0173] S5.2 Each smart traffic cone identifies whether there is an obstacle ahead based on the detection data. If an obstacle exists, its position coordinates and relative velocity are extracted. The obstacle identification method is as follows: the point cloud data is clustered, spatially adjacent points are grouped into one object, and the size of the object is calculated. If the minimum bounding rectangle of the clustered object has a height greater than 0.3 meters and a width greater than 0.1 meters, it is determined to be an obstacle. If an obstacle exists, its position coordinates and relative velocity are extracted. The position coordinates of the obstacle ( , The relative velocity is obtained by transforming the center point of the obstacle detected by the lidar from the road cone's body coordinate system to the global coordinate system. The obstacle's position is calculated by the difference between the detection results of two consecutive frames, i.e.:

[0174] ;

[0175] in The detection period is set to 0.05 seconds.

[0176] S5.3 Each smart traffic cone determines its current real-time location. Real-time speed and the location coordinates of the obstacles and relative velocity Calculate the collision time between itself and the obstacle. .

[0177] When the obstacle is stationary, the collision time is calculated using the following formula:

[0178] ;

[0179] When the obstacle is moving, considering relative velocity, the collision time is calculated using the following formula:

[0180] ;

[0181] in The current Euclidean distance between itself and the obstacle. This is the angle between the user's own heading and the direction of the obstacle's movement. If the calculated TTC is negative, it means that the two are moving away from each other, and in this case, TTC is taken as infinity.

[0182] S5.4 Each smart traffic cone compares the collision time with a preset first threshold and a second threshold;

[0183] Each smart traffic cone will have its collision time TTC compared to a preset first threshold. Second threshold Comparison, among which First threshold Take 3.0 seconds, second threshold. Take 1.0 second. The comparison rule is: if If so, it is determined that there is no danger at present, and the current driving state is maintained. If a potential collision risk is detected, a deceleration and avoidance maneuver is initiated, and a new local path is planned to bypass the obstacle. If an emergency collision is detected, braking should be initiated immediately to bring the vehicle to a stop.

[0184] When performing a deceleration and obstacle avoidance maneuver, the intelligent traffic cone first reduces its current linear velocity to 50% of its original velocity. Simultaneously, it acquires the outline point cloud of the obstacle through its environmental perception module and uses a dynamic window algorithm to generate a local path that bypasses the obstacle from its current position and returns to the original curved formation path. Specifically, it samples multiple sets of linear and angular velocities in the velocity space (v, ω) to simulate the trajectory within the next second, selecting the velocity command corresponding to the trajectory that avoids collision with the obstacle and is closest to the original path. When braking to a stop, the intelligent traffic cone sends an emergency braking command to the moving chassis, reducing the linear velocity to zero within 0.2 seconds.

[0185] S5.5. Each smart traffic cone, upon detecting an obstacle and executing an avoidance or stopping maneuver, broadcasts the obstacle information to all other smart traffic cones in the cluster via the self-organizing network established in step S3. The obstacle information data packet format is shown in Table 2 below:

[0186] Table 2 Obstacle Information Data Packet Format

[0187]

[0188] Upon receiving the broadcast, other smart traffic cones store the obstacle information in their local cache and adjust their respective motion plans using an artificial potential field method. Specifically, each smart traffic cone treats the received obstacle as a repulsive force source and superimposes a virtual repulsive force into its local path planning. Let the current position of the i-th smart traffic cone be... The received obstacle location is The virtual repulsive force generated by the obstacle on the i-th smart cone. The calculation formula is:

[0189] ;

[0190] in, The distance between the smart traffic cone and the obstacle is the Euclidean distance. The preset radius of influence of obstacles (typically 5 meters). This is the repulsion gain coefficient (typically 1.0). When the distance d between the smart traffic cone and the obstacle is less than or equal to the radius of influence... At that time, the virtual repulsive force The direction is from the obstacle to the smart cone, and the size is inversely proportional to the distance d; when d is greater than... At this time, the virtual repulsive force is zero, meaning that the obstacle has no effect on the smart traffic cone.

[0191] Each smart traffic cone will exert the virtual repulsive force. Compared with the first velocity vector calculated in step S4 By performing vector superposition, the corrected desired velocity vector is generated:

[0192] ;

[0193] in, This is the velocity-to-force conversion factor (typically 0.1), used to convert the force dimension to the velocity dimension. The corrected desired velocity vector. Replace the original desired velocity vector The output is sent to the mobile chassis for execution, enabling the smart traffic cones to automatically slow down or slightly detour when approaching obstacle areas.

[0194] In this way, the intelligent traffic cones that receive obstacle information can autonomously adjust their local paths by using virtual repulsion without changing the overall formation path, thereby avoiding chain congestion in front of multiple traffic cones in front of the same obstacle.

[0195] S5.6 After the intelligent traffic cone performs a braking stop, it sends a waiting instruction request to the upper-level control platform via a self-organizing network. The request data packet contains the traffic cone's identification, current position, and reason for stopping. Simultaneously, the intelligent traffic cone continuously detects obstacles ahead using its environmental perception module, maintaining a detection frequency of 20 Hz. Upon receiving the waiting instruction request, the upper-level control platform records the traffic cone's status.

[0196] If within the preset waiting time If the obstacle detected has disappeared, the intelligent traffic cone uses the nearest target pose point on the curved formation path as its recovery point, recalculates its motion parameters, and continues along the curved formation path after reaching that recovery point. Preset waiting time. Take 10 seconds. The criteria for determining whether an obstacle has disappeared are: no obstacle is detected in three consecutive detections, and the distance between the obstacle and the traffic cone is greater than 5 meters in the last detection.

[0197] After the intelligent traffic cone performs a deceleration and avoidance maneuver and goes around the obstacle, the intelligent traffic cone obtains its current real-time location through its own Beidou positioning module. , Then, in the target pose point sequence of the curved formation path, find the target pose point that is closest to the real-time position and use that target pose point as the recovery point. The distance is calculated using Euclidean distance.

[0198] ;

[0199] The recovery point is . This is the distance between the current real-time position of the intelligent traffic cone and the nearest target pose point. The intelligent traffic cone uses the recovery point as the new tracking target, and re-plans its own motion parameters according to the path tracking method in step S3 and the motion parameter planning method in step S4, driving itself to the recovery point and continuing to travel along a curved formation path towards the subsequent target pose point.

[0200] If within the preset waiting time If the obstacle persists, the smart traffic cone reports an anomaly to the upper control platform via its self-organizing network. The anomaly information includes the traffic cone's identifier, current location, and the duration of the obstacle's presence. The smart traffic cone remains stationary and reports the anomaly every 5 seconds until it receives a recovery command or manual intervention command from the upper control platform. The manual intervention command is sent by a remote operator through the upper control platform and includes operation codes such as continue driving, replan the route, or return to a designated point. Upon receiving the command, the smart traffic cone executes the corresponding action.

[0201] Through the above process, step S5 enables real-time detection, graded response, information sharing, and automatic recovery after avoidance or stopping of obstacles ahead, ensuring the safety and continuity of the cluster in dynamic road environments.

[0202] S6. Once each smart traffic cone reaches its final parking position on the curved formation path, each smart traffic cone stops moving and activates its warning device, causing the entire cluster to present a curved shape consistent with the curved formation path, thus completing its autonomous placement. Figure 3 The specific steps include:

[0203] S6.1 Each smart traffic cone continuously determines whether its current real-time position has reached its bound final parking position during its operation; the final parking position is the target pose point bound in step S1, and the final parking position of the i-th smart traffic cone is denoted as... Smart traffic cone real-time location Provided by the BeiDou positioning module. Arrival determination uses an Euclidean distance threshold method; if the distance between the current location and the final parking location is less than a preset arrival threshold... When the threshold is reached, it is determined that the threshold has been reached. Taking 0.1 meters as an example, the calculation formula is:

[0204] ;

[0205] At the same time, in order to ensure that the heading also meets the requirements, it is also necessary to determine whether the heading deviation is less than the preset heading threshold. , Take 5 degrees, that is:

[0206] ;

[0207] When both conditions are met, it is determined that the final parking position has been reached.

[0208] S6.2 When the intelligent traffic cone determines that it has reached the preset target position, it sends a braking command to its own moving chassis, reducing its linear velocity to zero and stopping its movement. Simultaneously, the intelligent traffic cone switches its status flag from "moving" to "in position". The status flag is stored in the traffic cone's local non-volatile memory, with an initial value of "not in position".

[0209] S6.3 The smart traffic cone broadcasts its status information of being in place to other smart traffic cones in the cluster through the self-organizing network established in step S2. The format of the status information data packet is shown in Table 3 below:

[0210] Table 3 Status Information Data Packet Format

[0211]

[0212] Upon receiving this broadcast, other smart traffic cones within the cluster record the corresponding traffic cone's identity in their local on-duty list. Each smart traffic cone maintains an on-duty array of length M (where M is the total number of traffic cones in the cluster). The initial value is false. When a traffic cone is received in place, the corresponding position is set to true.

[0213] S6.4 Each smart traffic cone activates its own warning device after confirming that it is in place and receiving status information that all other smart traffic cones in the cluster are in place.

[0214] The condition for confirming that a traffic cone is in position is that its corresponding position in the local positioning array is true. The condition for confirming that all other traffic cones are in position is that all positions in the positioning array except for itself are true. When both conditions are met, the warning device is activated. The warning device includes at least an audible and visual alarm module. The audible and visual alarm module includes a high-brightness LED light group and a buzzer. When the warning device is activated, the LED light group flashes red and yellow light alternately at a frequency of 1 Hz, and the buzzer emits an intermittent alert sound with a period of 0.5 seconds. The activation command for the warning device is generated independently by the microcontroller of each smart traffic cone, without relying on external triggers, ensuring that all smart traffic cones in the cluster are activated simultaneously, thereby avoiding incomplete road closure information caused by some traffic cones issuing warnings prematurely.

[0215] To ensure reliable cluster synchronization, a timeout mechanism is implemented. When a smart traffic cone reaches its final parking position, if it has been within the preset timeout period... If a road cone does not receive a readiness broadcast from all other road cones, it sends a re-interview request to all other road cones in the cluster via the ad hoc network, requesting them to rebroadcast their status. Timeout period. Take 30 seconds. If all positioning information is not received after three consecutive re-queries, the traffic cone will report a positioning timeout anomaly to the upper control platform and wait for manual intervention.

[0216] After all the smart traffic cones in the cluster activate their warning devices, each smart traffic cone comes to a stable stop in its final position. The positions of all the smart traffic cones are connected in sequence to form a curved shape consistent with the curved formation path. At this point, the cluster has completed the entire autonomous placement process, starting from its initial position, moving autonomously along the curved formation path, avoiding obstacles, and finally accurately reaching the target position and activating the warning.

[0217] Each smart traffic cone continuously checks its position to confirm whether it has reached its designated final parking location, and activates the warning device uniformly once all cones are in place.

[0218] This concludes the execution of all steps in the BeiDou-based intelligent traffic cone cluster collaborative control method of this embodiment. This method enables unmanned, autonomous placement of traffic cone clusters, significantly reducing human intervention and improving the safety and efficiency of road closure operations.

[0219] Example 2

[0220] This embodiment was conducted in a closed test field. The test section was an asphalt pavement road with continuous curves, with a total length of about 120 meters and a curve radius of 50 meters.

[0221] Before the test, the upper control platform acquired the road edge geometry data of the test section and generated a curved formation path matching the curve profile along the road edge based on this geometry data. This curved formation path contains 10 target pose points, with an arc length of 1.5 meters between adjacent target pose points, and the 10 target pose points are arranged sequentially along the curved formation path. The cluster consists of ten intelligent traffic cones, numbered #01 to #10. Each intelligent traffic cone is equipped with a two-wheel differential drive mobile chassis, a BeiDou positioning module supporting RTK differential positioning (horizontal positioning accuracy better than 2.5 cm), a forward-facing lidar (detection range 30 meters, horizontal field of view 120 degrees), and a self-organizing communication module based on the ZigBee protocol.

[0222] At the start of the test, the initial positions of the 10 smart traffic cones were randomly scattered in an area about 5 meters behind the starting point of the curved formation path, and each smart traffic cone was in an unpositioned state.

[0223] The upper control platform sends the generated curved formation path as an instruction to all 10 smart cones. Each smart cone receives the path through its own communication module and binds its unique identifier to a unique target pose point in the path. Specifically, smart cone #01 corresponds to the first target pose point, smart cone #02 corresponds to the second target pose point, and so on. Subsequently, each smart cone begins autonomous driving according to steps S2 to S4 of this invention. Each smart cone obtains its real-time position at a frequency of 10 Hz through a Beidou positioning module and tracks the curved formation path as the target trajectory. Taking smart cone #01 as an example, during the transition from a straight section to a curved section, this smart cone projects its real-time position onto the curved formation path, calculates a lateral deviation of 15 centimeters, and calculates a heading deviation of 8 degrees. This smart cone inputs the above lateral and heading deviations into a pre-configured PID controller, which outputs a steering adjustment amount. The two-wheel differential chassis adjusts the speed difference between the left and right wheels according to this steering adjustment amount. Actual measurements showed that the intelligent road cone reduced the lateral deviation to within two centimeters and the heading deviation to within two degrees within 0.5 seconds, verifying the effectiveness of the anti-deviation control method.

[0224] During the aforementioned driving process, each smart cone broadcasts its own pose and motion intention information via a self-organizing network at a period of 0.1 seconds, while simultaneously receiving broadcast information from neighboring smart cones. When the cluster reaches the vicinity of the sixth target pose point, the distance between smart cones #03 and #04 shrinks to 1.2 meters due to initial positional deviation, which is less than the preset safe distance threshold (1.5 meters). Based on the pose information received from #04, smart cone #03 determines that the distance between them is less than the safe threshold, and then calculates the avoidance velocity component, adjusting its desired velocity vector from the pure path tracking direction to a direction offset outward by 0.2 meters / second. The distance between them recovers to 1.6 meters within one second, again exceeding the safe distance threshold. Throughout the entire curve driving process, none of the ten smart cones collided with each other, indicating that the distributed cooperative planning method of this approach can effectively maintain the safe distance between adjacent cones within the cluster.

[0225] After the swarm had been traveling for about sixty seconds, to verify the dynamic obstacle avoidance capability of the invention, the testers placed a stationary engineering vehicle as an obstacle on the curved convoy path about eight meters in front of the intelligent traffic cone #05. The intelligent traffic cone #05 collected forward detection data at a frequency of 20 Hz using its own forward-facing lidar, and identified the obstacle in the next detection cycle (0.05 seconds later), extracting the obstacle's position coordinates. At this time, the obstacle's relative velocity was zero.

[0226] The intelligent traffic cone #05 calculates its Time-to-Collision (TTC) with the obstacle based on its current real-time position, real-time speed, and the obstacle's coordinates. Initially, the TTC is approximately 6.2 seconds, exceeding the first threshold of 3.0 seconds. The intelligent traffic cone maintains its current driving state and continues monitoring. When the intelligent traffic cone continues to travel to approximately four meters from the obstacle, the TTC drops to 2.5 seconds. This value is less than or equal to the first threshold of 3.0 seconds and greater than the second threshold of 1.0 seconds. The intelligent traffic cone then performs a deceleration and avoidance maneuver, reducing its linear velocity from 1.0 m / s to 0.5 m / s. Simultaneously, it uses a dynamic window algorithm to generate a local detour path, successfully bypassing the obstacle approximately 0.8 meters to the left of the obstacle. After bypassing the obstacle, the intelligent traffic cone obtains its current real-time position through the BeiDou positioning module and finds the nearest target pose point on the curved formation path as a recovery point. Using this recovery point as the new tracking target, it re-plans its motion parameters according to methods S2 and S4, driving itself to the recovery point and then continuing along the curved formation path towards the subsequent target pose point. The entire avoidance and recovery process took about six seconds.

[0227] Simultaneously, upon detecting and avoiding an obstacle, smart cone #05 broadcasts the obstacle's location coordinates and relative speed to all other smart cones in the cluster via a self-organizing network. Upon receiving this obstacle information, smart cones #06 through #10 in the following cluster superimpose virtual repulsion forces into their local path tracking, slowing down in advance as they approach the obstacle area, thus preventing chain congestion caused by a single obstacle avoidance.

[0228] The cluster continues to travel along the curved formation path, with each smart cone continuously determining whether its current real-time position has reached its bound target pose point. When smart cone #01 first reaches its bound target pose point, it determines that the distance between its current position and its final parking position is less than the threshold of 0.1 meters, and its heading deviation is less than 5 degrees. It then sends a braking command to the mobile chassis to stop moving, marks its status as "in position," and broadcasts its "in position" status to other smart cones in the cluster via the self-organizing network. In the following three seconds, smart cones #02 through #09 successively reach their respective preset target positions and broadcast their "in position" information.

[0229] When smart traffic cone #10 finally reaches its final parking position and stops, it confirms that it is in place and has received the positioning status information broadcast by all nine other smart traffic cones (numbered #01 to #09) in the ad hoc network. At this point, all positions in its local positioning array are true. Smart traffic cone #10 then triggers its own warning device. Simultaneously, the other nine smart traffic cones, having also confirmed that all cones are in place, synchronously activate their warning devices within a very small time difference (recorded to be no more than 0.1 seconds). The LED lights on each smart traffic cone flash red and yellow alternately at a frequency of Hertz, and the buzzers emit intermittent warning sounds in sync.

[0230] After the test, the final stopping position of each smart traffic cone was measured. The ten stopping points were smoothly connected and compared with the curved formation path generated by the upper control platform. The maximum lateral deviation was only five centimeters, and the average deviation was two and a half centimeters, indicating that all smart traffic cones accurately reached the preset target position. The ten smart traffic cones together formed a curved, closed warning pattern that perfectly conformed to the contour of the road curve.

[0231] Key experimental data are shown in Table 4 below:

[0232] Table 4 Key Experimental Data

[0233]

[0234] This experiment required no human intervention throughout. Ten intelligent traffic cones autonomously started from random initial positions and successfully completed the following processes: generating curved formation paths, tracking paths with high precision using BeiDou navigation and preventing deviations, cluster-distributed collaborative collision avoidance, dynamic obstacle-level response and detour recovery, and synchronous warnings after all cones were precisely in place. The experimental results fully demonstrate the high precision, high reliability, and high autonomy of the control method proposed in this invention in intelligent transportation road closure operations, and it possesses clear engineering application value.

[0235] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0236] The undescribed parts involved in this invention are the same as or implemented using existing technology.

Claims

1. A method for intelligent road cone cluster cooperative control based on Beidou positioning, characterized in that, include: S1. The upper-level control platform generates a curved formation path based on the geometric data of the closed road area and sends the path to a cluster of multiple smart cones. Each smart cone has a mobile chassis, a Beidou positioning module, an environmental perception module, a communication module, and a controller. S2. Multiple intelligent traffic cones within the cluster establish a self-organizing network through communication modules, and exchange their pose and motion intention information using a distributed control architecture; specifically including: S2.1 Each smart cone in the cluster broadcasts its own identity identifier through its own communication module at a preset channel frequency, and simultaneously listens for the identity identifiers broadcast by other smart cones. The neighbor list in the cluster is determined based on the number of identity identifiers received. S2.2 Each smart cone establishes a point-to-point communication link with each neighbor smart cone in the neighbor list, forming a fully distributed self-organizing network; S2.3 Each smart traffic cone broadcasts its current pose information and motion intention information to all neighboring smart traffic cones through a self-organizing network according to a preset time period; S2.4 Each smart cone simultaneously receives pose information and motion intention information broadcast by neighboring smart cones, and stores the received information in a local cache for subsequent motion planning; S3. Each intelligent traffic cone receives the curved formation path and binds it to one of the target pose points. It then uses the BeiDou positioning module to obtain its real-time position, tracks the curved formation path as the target trajectory, and adjusts its steering based on the lateral deviation between its real-time position and the target trajectory. Specifically, this includes: S3.1 After receiving the curved formation path, each intelligent road cone uses the complete curved formation path as a reference and searches for the target pose point and its neighboring points that are closest to its current position in real time during the driving process, forming a dynamic local tracking window and forming the local target trajectory of each intelligent road cone. S3.2 Each smart traffic cone obtains its current real-time location through its own BeiDou positioning module at a preset frequency; S3.3 Each intelligent traffic cone searches for the target pose point closest to its own real-time position in the local target trajectory and uses that target pose point as the current tracking target point; S3.4 Each intelligent traffic cone generates a speed control command to move itself toward the current tracking target point based on the positional relationship between its real-time position and the current tracking target point, thereby driving the mobile chassis to travel; S3.5 Each smart cone projects its real-time position onto the curved formation path to obtain the projection point, and calculates the distance from the real-time position to the projection point as the lateral deviation. S3.6 Each smart cone calculates the difference between its own real-time heading angle and the target heading angle at the projection point, which is used as the heading deviation; S3.7 Each intelligent traffic cone inputs its lateral deviation and heading deviation into its own controller, which uses a PID control algorithm to output steering adjustment. S3.8 Each intelligent traffic cone controls its own moving chassis to perform steering actions based on the steering adjustment amount; S4. Each smart cone independently plans its own motion parameters based on neighborhood information and the curved formation path, so that the cluster as a whole moves synchronously along the curved formation path, and maintains a safe distance between adjacent smart cones without collision. S5. During the driving process, each smart cone detects obstacles ahead in real time through the environmental perception module; if an obstacle is detected, it will perform an avoidance or stop action and broadcast the obstacle information to other smart cones in the cluster. S6. Once each smart traffic cone reaches its final parking position on the curved formation path, each smart traffic cone stops moving and activates its warning device, causing the entire cluster to present a curved shape consistent with the curved formation path, thus completing autonomous placement.

2. The intelligent traffic cone cluster collaborative control method based on BeiDou positioning according to claim 1, characterized in that, The S1 step specifically includes: S1.1 The upper-level control platform acquires the geometric data of the closed road area; Based on geometric data, the S1.2 upper control platform generates a curved formation path that matches the contour of the closed area of ​​the road along the road edge. The curved formation path includes several target pose points. The S1.3 upper control platform sends the coordinates of all target pose points to a cluster of multiple intelligent road cones in the form of instructions; Within the S1.4 cluster, each smart traffic cone receives the coordinate values ​​of all target pose points through its own communication module and binds its unique identifier to one of the target pose points, enabling each smart traffic cone to obtain its corresponding final parking position.

3. The intelligent traffic cone cluster collaborative control method based on BeiDou positioning according to claim 1, characterized in that, The S4 step specifically includes: S4.1 Each smart traffic cone obtains its own status and the status of its neighbors through local caching; S4.2 Each intelligent road cone extracts the target pose point corresponding to the current moment and the expected target pose point at the next moment from the local target trajectory, as the reference input for path tracking; S4.3 Each intelligent road cone calculates its path tracking speed based on the deviation between its current real-time position and the target pose point corresponding to the current moment, and records it as the first speed. The first speed is used to drive the intelligent road cone to approach the curved formation path. S4.4 Each smart cone calculates the current distance between itself and each neighboring smart cone based on the pose information of all neighboring smart cones, and determines whether the current distance is less than the preset safe distance threshold; if so, it calculates the avoidance speed to increase the spacing and records it as the second speed; if not, it sets the second speed to zero. S4.5 Each intelligent traffic cone vector superimposes the first velocity and the second velocity to generate its own desired velocity vector, and outputs the desired velocity vector as its motion parameter for the next moment to the mobile chassis for execution.

4. The intelligent traffic cone cluster collaborative control method based on BeiDou positioning according to claim 1, characterized in that, The S5 step specifically includes: S5.1 During the driving process, each smart traffic cone collects detection data within a set angle range ahead through its own configured environmental perception module at a preset detection frequency; S5.2 Each smart traffic cone identifies whether there is an obstacle ahead based on the detection data. If there is, it extracts the position coordinates and relative speed of the obstacle. S5.3 Each smart traffic cone calculates the collision time between itself and the obstacle based on its current real-time position, real-time speed, and the position coordinates and relative speed of the obstacle. S5.4 Each smart traffic cone will have its collision time TTC compared to a preset first threshold. Second threshold Comparison, among which The comparison rule is: if If there is no immediate danger, the current driving state will be maintained; if If a potential collision risk is identified, a deceleration and avoidance maneuver is initiated, and a new local path is planned to bypass the obstacle; if If an emergency collision is detected, braking should be initiated immediately to bring the vehicle to a stop. S5.

5. When each smart cone detects an obstacle and performs an avoidance or stopping action, it broadcasts the obstacle information to all other smart cones in the cluster through the self-organizing network established in step S3. S5.6 After the intelligent traffic cone performs a braking and stopping action, it sends a waiting instruction request to the upper control platform through a self-organizing network. After receiving the waiting instruction request, the upper control platform records the status of the traffic cone. At the same time, the intelligent traffic cone continuously detects obstacles ahead through the environmental perception module.

5. The intelligent traffic cone cluster collaborative control method based on BeiDou positioning according to claim 4, characterized in that, Step S5.6 specifically includes: If the obstacle is detected to have disappeared within the preset waiting time, the intelligent traffic cone will use the target pose point closest to the current real-time position as the recovery point, and the recovery point as the new tracking target. Following the path tracking method in step S3 and the motion parameter planning method in step S4, it will re-plan its own motion parameters, drive itself to the recovery point, and continue to travel along the curved formation path to the subsequent target pose point. If the obstacle does not disappear within the preset waiting time, the smart traffic cone will report the anomaly to the upper control platform through the self-organizing network. The anomaly information includes the traffic cone's identification, current location, and the duration of the obstacle's presence. The smart traffic cone will remain stopped and report the anomaly again at intervals until it receives a recovery command or manual intervention command from the upper control platform.

6. The intelligent traffic cone cluster collaborative control method based on BeiDou positioning according to claim 2, characterized in that, The S6 step specifically includes: S6.1 Each smart traffic cone continuously determines whether its current real-time location has reached its bound final parking location during the driving process; S6.2 When the intelligent traffic cone determines that it has reached the final parking position, the intelligent traffic cone sends a braking command to its own moving chassis, so that the linear speed drops to zero and the movement stops; S6.3 The smart traffic cone broadcasts its own positioning status information to other smart traffic cones in the cluster through the self-organizing network established in step S2. S6.4 After confirming that it is in place and receiving status information that all other smart traffic cones in the cluster are in place, each smart traffic cone activates its own warning device.

7. The intelligent traffic cone cluster collaborative control method based on BeiDou positioning according to claim 6, characterized in that, In step S6.1, when the distance between the current position of a smart road cone and its final parking position is less than a preset arrival threshold and the heading deviation is less than a preset heading threshold, it is determined that the final parking position has been reached.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the intelligent traffic cone cluster collaborative control method based on BeiDou positioning as described in any one of claims 1 to 7.

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