Full-automatic obstacle-avoiding paying-off mobile equipment and using method thereof

Abstract

The invention discloses a full-automatic obstacle-avoiding paying-off mobile device and a using method thereof.The full-automatic obstacle-avoiding paying-off mobile device comprises a mobile unit, an obstacle-avoiding unit, a control unit and a paying-off unit, and the mobile unit runs according to a planned path through position feedback and driving control; the obstacle avoidance unit is used for detecting front obstacles and determining an obstacle avoidance strategy according to the positions and the distances of the obstacles; when the error between the actual running route and the preset track is smaller than a preset value, the paying-off unit releases the marking material; the moving unit comprises a driving device, a supporting device and a positioning device; the obstacle avoidance unit comprises a visual sensor and a laser radar; the pay-off unit comprises a discharging assembly, a storage assembly and an execution assembly. And when the error between the actual running route and the preset track is smaller than a preset value, the execution assembly controls the discharging assembly to scatter ash outwards. The equipment has an accurate and automatic ash scattering function, and can stably move and accurately pay off in a complex and uneven terrain of a construction site, so that the automation degree and the construction efficiency of pay-off operation are improved.

Description

Technical Field

[0001] This invention relates to the field of construction surveying technology, specifically to a fully automatic obstacle avoidance and line-laying mobile device and its usage method. Background Technology

[0002] Surveying and setting out is a core preliminary process in engineering construction, directly determining the accuracy and quality of construction, and is particularly critical in scenarios such as foundation pit excavation. Traditional setting out relies on manual labor. Taking foundation pit excavation as an example, workers need to set up control points, use steel tape measures, and spread mortar to mark them. Large-scale operations can take several days to several weeks, resulting in extremely low efficiency. Furthermore, uneven tension of the steel tape and reading deviations are easily caused by human factors, leading to deviations from the design. Workers also experience high labor intensity due to prolonged bending and walking, and obstacles at the construction site can increase detour errors and safety risks.

[0003] In recent years, the application of surveying robots has gradually improved efficiency and accuracy, but significant limitations still exist. In terms of media applicability, most devices use liquid media such as latex paint, which are only suitable for hard surfaces. On rough sites such as foundation pits, they are easily affected by mud and gravel, resulting in insufficient clarity and durability of markings.

[0004] The existing equipment has the following technical problems: First, it has poor environmental adaptability and can only move stably on flat ground. It is difficult to maintain its posture stability in foundation pits and gravel sites. Second, it lacks obstacle avoidance capabilities and has no accurate detection and path adjustment mechanism. It is easy to stop or deviate from the trajectory when encountering obstacles such as steel bar piles and pits, requiring manual intervention to interrupt the operation. Third, the line laying mechanism is imperfect. The simplified spraying or marking method cannot form clear and continuous marks on complex terrain.

[0005] In summary, existing equipment is difficult to adapt to complex environments such as foundation pit excavation, and its efficiency and accuracy cannot meet the requirements. Therefore, it is urgent to develop fully automated equipment with strong adaptability, reliable obstacle avoidance capabilities, and a complete wire laying mechanism. Summary of the Invention

[0006] The purpose of this invention is to provide a fully automatic obstacle avoidance line laying device and its usage method. The technical problem solved by this device is the low efficiency and poor accuracy of line laying in complex construction environments, which are problems existing in the prior art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A fully automatic obstacle avoidance and line-laying mobile device includes a moving unit, an obstacle avoidance unit, a control unit, and a line-laying unit. The moving unit travels along a planned path through position feedback and drive control. The obstacle avoidance unit detects obstacles ahead and determines an obstacle avoidance strategy based on the position and distance of the obstacles. When the error between the actual running route and the preset trajectory is less than a preset value, the line-laying unit releases marking material. The moving unit includes a driving device, a supporting device, and a positioning device. The supporting device is disposed on the driving device, and the positioning device is disposed on the supporting device. The obstacle avoidance unit includes a visual sensor and a lidar. The visual sensor and the lidar send the detected information to the control unit, and the control unit processes the information and delineates the obstacle avoidance route. The wire feeding unit includes a discharge component, a storage component, and an execution component. The storage component is used to store marking materials, and the discharge component is used to sprinkle ash outwards during the journey. When the error between the actual running route and the preset trajectory is less than a preset value, the execution component controls the discharge component to sprinkle ash outwards.

[0008] Preferably, the positioning device includes a positioning differential signal receiver and a multi-frequency positioning antenna, wherein the multi-frequency positioning antenna receives satellite signals and feeds them back to the positioning differential signal receiver.

[0009] Preferably, the storage assembly includes a ash bucket and a stirrer. The ash bucket is mounted on the support device, and the stirrer is rotatably mounted inside the ash bucket for stirring the marking material. The discharge assembly is a discharge port fixedly mounted at the bottom of the ash bucket, through which the marking material inside the ash bucket is sprinkled outward.

[0010] A method for using a fully automatic obstacle avoidance and line-laying mobile device includes the following steps: Step 1: Input the planned route information and ash-spreading point information into the control unit, and set the minimum safety distance. Optimal edge distance and ash spreading rate ; Step 2, set the route i The sampling point was determined, and the first sampling point was determined. i The shortest distance from each sampling point to the global path ; Step 3: Initiate equipment movement. During the movement, the obstacle avoidance unit continuously detects obstacles ahead. If an obstacle is detected, the LiDAR will acquire the information. i Distance from each sampling point to the nearest obstacle , No. i Real-time distance from each sampling point to the nearest obstacle edge and the i linear velocity at each sampling point ; and the first is obtained by the vision sensor. i acceleration at each sampling point and the i angular acceleration at each sampling point ; Step four: Within the control unit, use a multi-objective cost function. FGenerate an edge-hugging strategy based on real-time optimization of local trajectories, with a multi-objective cost function. F for , In the formula, α , β , γ , δ and μ All of these are configurable weighting coefficients; This is the cost of path tracing; The cost is the distance to the obstacle; The cost of motion smoothing; Cost of edge-fitting preference; Cost to the efficiency of the wire laying task; Step 5: During the movement of the equipment, the movement path of the equipment is continuously adjusted according to the trajectory generated in real time based on the edge-hugging strategy; Step 6: When the equipment reaches the ash-spreading point, the control unit controls the line-laying unit to release the marking material onto the ground to form lines or dots. Step 7: After the line is laid out, the control unit stops the line laying unit and the moving unit continues to the next ash spreading point or returns to the standby position.

[0011] Preferably, the path tracing cost is specifically: , In the formula, For the first on the trajectory i The shortest distance from each sampling point to the global path.

[0012] Preferably, the obstacle distance cost is specifically: , In the formula, For the first on the trajectory i The distance from each sampling point to the nearest obstacle λ is the minimum safe distance set, and λ is the attenuation coefficient.

[0013] Preferably, the motion smoothing cost is specifically: , In the formula, The weighting coefficients for the rotation penalty. For the first i The acceleration of each sampling point For the first i Angular acceleration at each sampling point.

[0014] Preferably, the edge-fitting preference cost is specifically: , In the formula, For the first on the trajectory i The real-time distance from each sampling point to the nearest obstacle edge. The optimal edge distance set for the system.

[0015] Preferably, the performance cost of the wire laying task is specifically as follows: , In the formula, This is a binary indicator function; it is 1 when the device is in the "gray flag segment" and 0 otherwise. Let this be the linear velocity of the device at that trajectory point. This is the preset ash spreading rate for this point.

[0016] Preferably, in step five, the effect of adjusting the equipment movement path is evaluated by path length, minimum obstacle gap, smoothness index, travel time, ash spreading continuity, and edge contact error.

[0017] Beneficial effects: In this invention, the mobile unit possesses strong obstacle-crossing and stability capabilities, enabling reliable movement in conditions such as pits, potholes, and uneven ground. The obstacle avoidance unit can flexibly respond to dynamically changing environments without prior knowledge of the terrain. The control unit precisely controls the layout unit, activating it when the equipment accurately reaches the target point to create continuous or dotted construction markings. The equipment can complete the entire process of point arrival, obstacle avoidance, and layout based on the input task, reducing manual intervention and improving construction efficiency.

[0018] The core of the edge-hugging detour strategy based on real-time local trajectory optimization lies in its independence from the global map. Instead, it generates an optimal trajectory in real-time within a local area in front of the device, taking into account edge-hugging effect, smooth movement, and safety, based on real-time detected obstacle information. This flexibility greatly improves the device's adaptability in complex environments, especially in venues with many obstacles or narrow spaces.

[0019] This strategy results in lower computational resource consumption and faster response times, making it particularly suitable for handling dynamic obstacles that suddenly appear in construction environments. Furthermore, the efficiency of the line-laying task is incorporated into path optimization considerations, using a multi-objective optimization algorithm to simultaneously maximize both safe obstacle avoidance and operational efficiency.

[0020] In summary, this application solves the technical problems of low efficiency and poor accuracy in line laying in complex construction environments in existing equipment. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is a schematic diagram comparing the hybrid A* path planning algorithm of this invention with the optimized path of this invention under the condition of obstacle expansion coefficient of 2m; Figure 3 This is a graph showing the velocity variation of the present invention under the condition that the obstacle expansion coefficient is 2m. Figure 4 This is a graph showing the change in edge-attaching distance under the condition of an obstacle expansion coefficient of 2m. In the diagram: 1. Moving unit; 2. Obstacle avoidance unit; 3. Control unit; 4. Line feeding unit; 10. Drive device; 11. Support device; 12. Positioning device; 20. Vision sensor; 21. LiDAR; 40. Discharge assembly; 41. Storage assembly; 42. Execution assembly; 120. Positioning differential signal receiver; 121. Multi-frequency positioning antenna; 410. Ash bucket; 411. Agitator. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings: like Figures 1-4 The illustrated fully automatic obstacle avoidance and line laying mobile device includes a mobile unit 1, an obstacle avoidance unit 2, a control unit 3, and a line laying unit 4. The mobile unit 1 travels along a planned path through position feedback and drive control. The mobile unit 1 includes a drive device 10, a support device 11, and a positioning device 12. The drive device 10 is a tracked chassis, which can adapt to relatively harsh working conditions such as mud and potholes.

[0023] The support device 11 is fixedly mounted on the drive device 10 to provide an installation base for the working parts. In this embodiment, the support device 11 is spliced ​​together from multiple aluminum profiles, but it can also be welded from steel profiles.

[0024] The positioning device 12 is fixedly mounted on the support device 11. The positioning device 12 includes a positioning differential signal receiver 120 and a multi-frequency positioning antenna 121. The multi-frequency positioning antenna 121 receives satellite signals and feeds them back to the positioning differential signal receiver 120. Specifically, 1) the multi-frequency positioning antenna 121 simultaneously receives multiple frequency signals from multiple satellites, such as the B1, B2, and B3 frequencies of BeiDou and the L1 and L2 frequencies of GPS, and transmits the signals to the positioning differential signal receiver 120; 2) the positioning differential signal receiver 120 receives differential correction signals sent by nearby base stations via wireless communication such as 4G or radio; 3) the positioning differential signal receiver 120 processes the multi-frequency satellite signals, calculates the original position, and then combines the differential correction data to eliminate errors, finally outputting high-precision navigation information such as latitude, longitude, and elevation to provide positioning guidance for the measuring robot. Both the positioning differential signal receiver 120 and the multi-frequency positioning antenna 121 are commercially available mature products, and their specific structural principles are not described in detail in this specification.

[0025] Obstacle avoidance unit 2 detects obstacles ahead and determines an obstacle avoidance strategy based on the obstacle's position and distance. Obstacle avoidance unit 2 includes a visual sensor 20 and a lidar 21. The visual sensor 20 and lidar 21 send the detected information to the control unit 3, which processes the information and delineates an obstacle avoidance route. Specifically, the visual sensor 20 analyzes the presence of obstacles through image acquisition, feature extraction, object recognition and classification, and distance estimation and decision-making. The lidar 21 detects obstacles through laser emission and reception, distance calculation, and point cloud generation. Both the visual sensor 20 and lidar 21 are commercially available mature products, and their specific structural principles are not described in detail in this specification.

[0026] Control unit 3 uses a commercially available robot controller and is equipped with a ROS system. Control unit 3 employs a dynamic edge-following obstacle avoidance strategy based on real-time optimization of local trajectories.

[0027] When the error between the actual running route and the preset trajectory is less than the preset value, the line-laying unit 4 releases the marking material. In this embodiment, the marking material is white lime.

[0028] The feeding unit 4 includes a discharge assembly 40, a storage assembly 41, and an execution assembly 42. The storage assembly 41 stores the marking material and includes a ash bucket 410 and a stirrer 411. The ash bucket 410 is fixedly mounted on the support device 11, and the stirrer 411 is rotatably mounted inside the ash bucket 410 for stirring the marking material. The stirrer 411 includes stirring blades rotatably mounted inside the ash bucket 410 via a bracket and a drive motor fixedly mounted on the top of the ash bucket 410. The rotation of the drive motor drives the stirring blades to rotate, preventing the ash from sticking together. The bottom of the stirrer 411 is located inside the discharge port, and during the stirring process, the ash is conveyed to the discharge port, and the discharge rate can be controlled.

[0029] The discharge assembly 40 is used to spread ash outwards during the process. The discharge assembly 40 is a discharge port fixedly installed at the bottom of the ash bucket 410, through which the marking material inside the ash bucket 410 flows downwards. In another embodiment, the discharge port is located on the side of the ash bucket 410, the agitator 411 is horizontally arranged, and the discharge amount of quicklime is completely controlled by the rotation speed of the agitator 411.

[0030] When the error between the actual running route and the preset trajectory is less than the preset value, the actuator 42 controls the discharge assembly 40 to sprinkle ash outward. The actuator 42 is an electromagnetic switch installed on the discharge port. The control unit 3 can control the opening and closing of the electromagnetic switch. When the electromagnetic switch is open, the ash flows out; when the electromagnetic switch is closed, the ash stops flowing out.

[0031] like Figures 1-4 The method of using a fully automatic obstacle avoidance and line-laying mobile device shown includes the following steps: Step 1: Input the planned route information and ash-spreading point information into the control unit 3, and set the minimum safety distance. Optimal edge distance and ash spreading rate . Step 2, set the route i The sampling point was determined, and the first sampling point was determined. i The shortest distance from each sampling point to the global path .

[0032] Step 3: Initiate equipment movement. During the movement, obstacle avoidance unit 2 continuously detects obstacles ahead. If an obstacle is detected, the lidar 21 acquires the first... i Distance from each sampling point to the nearest obstacle , No. i Real-time distance from each sampling point to the nearest obstacle edge and the i linear velocity at each sampling point ; and the visual sensor 20 acquires the first i acceleration at each sampling point and the i angular acceleration at each sampling point .

[0033] Step four: Within the control unit, use a multi-objective cost function. F Generate an edge-hugging strategy based on real-time optimization of local trajectories, with a multi-objective cost function. F for , In the formula, α , β , γ , δ and μ All of these are configurable weighting coefficients; The specific cost of path tracing is as follows: , In the formula, For the first on the trajectory i The shortest distance from each sampling point to the global path; The specific cost for obstacle distance is as follows: , In the formula, For the first on the trajectory i The distance from each sampling point to the nearest obstacle The minimum safe distance is set, and λ is the attenuation coefficient. The specific cost of motion smoothing is as follows: , In the formula, The weighting coefficients for the rotation penalty. For the first i The acceleration of each sampling point For the first i angular acceleration at each sampling point; The specific cost of edge preference is as follows: , In the formula, For the first on the trajectory i The real-time distance from each sampling point to the nearest obstacle edge. The optimal edge distance set for the system; The specific performance cost for the line laying task is as follows: , In the formula, This is a binary indicator function; it is 1 when the device is in the "gray flag segment" and 0 otherwise. Let this be the linear velocity of the device at that trajectory point. This is the preset ash spreading rate for this point.

[0034] Step 5: During the movement of the equipment, the movement path of the equipment is continuously adjusted according to the trajectory generated in real time based on the edge-hugging strategy.

[0035] Step six: When the equipment reaches the ash-spreading point, the control unit 3 controls the line-laying unit 4 to release the marking material onto the ground to form lines or dots.

[0036] Step 7: After the line is laid out, the control unit 3 controls the line laying unit 4 to stop operating, and the moving unit 1 continues to the next ash spreading point or returns to the standby position.

[0037] During actual construction, after receiving the task instruction, the control unit 3 begins to travel along the predetermined path. When the equipment encounters an obstacle, the lidar 21 or vision sensor 20 detects the obstacle's position and distance in real time. Based on the detection information, the control unit 3 determines whether detour is necessary. If detour is required, the control unit 3 adjusts the travel direction and selects the optimal detour path based on the obstacle's distance and relative position. The equipment maintains a sufficient safety distance and continues to move forward until the obstacle is completely bypassed. The travel speed of the drive unit 10 is matched with the discharge volume of the discharge component 40. When the drive unit 10 travels at a high speed, the rotation speed of the agitator 411 is correspondingly increased. This ensures that the ash spreading process is precisely matched with the actual operating state, avoiding the problems of "delayed ash spreading, ash interruption, or misalignment" in traditional technologies.

[0038] 1) Has the ash-spreading point been reached? 2) Does the edge-to-edge distance requirement apply? 3) Are there any obstacles affecting the ash-spreading process? Only when all of the above conditions are met will the line-laying unit 4 be triggered to perform the ash-spreading action, thus avoiding invalid and off-center ash-spreading. A comprehensive control system combining path analysis, real-time obstacle avoidance, and line-laying judgment is adopted, emphasizing intelligence and dynamism.

[0039] Path adjustment evaluation: The experimental site was 20m long, and the obstacles were 1m × 1m cubes. To compare and verify the results, the hybrid A* path planning algorithm involved in the invention patent application number 202411011202.6 was used as a control. The path optimized by the mobile device using the function of this application was compared with the path optimized by the hybrid A* path planning algorithm.

[0040] The effectiveness of adjusting the equipment's movement path was evaluated by path length, minimum obstacle clearance, smoothness index, travel time, ash spreading continuity, and edge contact error. The comparison results are shown in the table below: Evaluation parameters Hybrid A* path planning algorithm Algorithm in this application Path length / m 22.65 21.08 Minimum obstacle gap / m 2.02 2.01 <![CDATA[Smoothness index ∑(dθ 2 )]]> 0.0971 0.0364 Travel time / s 30.5 26.4 Ash spreading continuity rate / % 84.3 96.7 Edge fit error / m 0.46 0.13 As shown in the table above, the path optimization using the function proposed in this application has advantages over the path optimization using the hybrid A* path planning algorithm in all evaluation parameters.

[0041] The above embodiments are merely illustrative of the concept and implementation of the present invention and are not intended to limit it. Under the concept of the present invention, technical solutions without substantial changes are still within the scope of protection.

Claims

1. A fully automatic obstacle avoidance and line-laying mobile device, characterized in that: It includes a mobile unit (1), an obstacle avoidance unit (2), a control unit (3) and a line-laying unit (4). The mobile unit (1) travels along the planned path through position feedback and drive control. The obstacle avoidance unit (2) detects obstacles in front and determines the obstacle avoidance strategy based on the position and distance of the obstacles. When the error between the actual running route and the preset trajectory is less than the preset value, the line-laying unit (4) releases the marking material; The moving unit (1) includes a driving device (10), a supporting device (11) and a positioning device (12). The supporting device (11) is disposed on the driving device (10), and the positioning device (12) is disposed on the supporting device (11). The obstacle avoidance unit (2) includes a visual sensor (20) and a lidar (21). The visual sensor (20) and the lidar (21) send the detected information to the control unit (3). The control unit (3) processes the information and delineates the obstacle avoidance route. The wire feeding unit (4) includes a material discharge component (40), a material storage component (41), and an execution component (42). The material storage component (41) is used to store marking materials, and the material discharge component (40) is used to scatter ash outward during the journey. When the error between the actual running route and the preset trajectory is less than the preset value, the execution component (42) controls the material discharge component (40) to scatter ash outward.

2. The fully automatic obstacle avoidance and line laying mobile device according to claim 1, characterized in that: The positioning device (12) includes a positioning differential signal receiver (120) and a multi-frequency positioning antenna (121). The multi-frequency positioning antenna (121) receives satellite signals and feeds them back to the positioning differential signal receiver (120).

3. The fully automatic obstacle avoidance and line laying mobile device according to claim 1, characterized in that: The storage component (41) includes a ash bucket (410) and a stirrer (411). The ash bucket (410) is mounted on the support device (11), and the stirrer (411) is rotatably mounted inside the ash bucket (410) for stirring the marking material. The discharge component (40) is a discharge port fixedly mounted at the bottom of the ash bucket (410), through which the marking material inside the ash bucket (410) is scattered outward.

4. A method of using a fully automatic obstacle avoidance and line-laying mobile device as described in any one of claims 1 to 3, characterized in that, Includes the following steps: Step 1: Input the planned route information and ash-spreading point information into the control unit (3) and set the minimum safety distance. Optimal edge distance and ash spreading rate ; Step 2, set the route i The sampling point was determined, and the first sampling point was determined. i The shortest distance from each sampling point to the global path ; Step 3: Start the equipment movement. During the equipment movement, the obstacle avoidance unit (2) detects obstacles in front in real time. If an obstacle is detected, the lidar (21) obtains the first... i Distance from each sampling point to the nearest obstacle , No. i Real-time distance from each sampling point to the nearest obstacle edge and the i linear velocity at each sampling point ; and the visual sensor (20) acquires the first i acceleration at each sampling point and the i angular acceleration at each sampling point ; Step four, within the control unit (3), a multi-objective cost function is used. F Generate an edge-hugging strategy based on real-time optimization of local trajectories, with a multi-objective cost function. F for ， In the formula, α , β , γ , δ and μ All of these are configurable weighting coefficients; This is the cost of path tracing; The cost is the distance to the obstacle; The cost of motion smoothing; Cost of edge-fitting preference; Cost to the efficiency of the wire laying task; Step 5: During the movement of the equipment, the movement path of the equipment is continuously adjusted according to the trajectory generated in real time based on the edge-hugging strategy; Step 6: When the equipment reaches the ash-spreading point, the control unit (3) controls the line-laying unit (4) to release the marking material onto the ground to form lines or dots. Step 7: After the line is laid out, the control unit (3) controls the line laying unit (4) to stop its operation, and the moving unit (1) continues to the next ash-spreading point or returns to the standby position.

5. The method of use according to claim 4, characterized in that, The path tracing cost is specifically as follows: ， In the formula, For the first on the trajectory i The shortest distance from each sampling point to the global path.

6. The method of use according to claim 4, characterized in that, The obstacle distance cost is specifically as follows: ， In the formula, For the first on the trajectory i The distance from each sampling point to the nearest obstacle λ is the minimum safe distance set, and λ is the attenuation coefficient.

7. The method of use according to claim 4, characterized in that, The motion smoothing cost is specifically as follows: ， In the formula, The weighting coefficients for the rotation penalty. For the first i The acceleration of each sampling point For the first i Angular acceleration at each sampling point.

8. The method of use according to claim 4, characterized in that, The specific cost of edge-fitting preference is: ， In the formula, For the first on the trajectory i The real-time distance from each sampling point to the nearest obstacle edge. The optimal edge distance set for the system.

9. The method of use according to claim 4, characterized in that, The specific performance cost of the wire laying task is as follows: ， In the formula, This is a binary indicator function; it is 1 when the device is in the "gray flag segment" and 0 otherwise. Let this be the linear velocity of the device at that trajectory point. This is the preset ash spreading rate for this point.

10. The method of use according to claim 4, characterized in that: In step five, the effect of adjusting the equipment movement path is evaluated by path length, minimum obstacle gap, smoothness index, travel time, ash spreading continuity, and edge contact error.

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