CN118068841A - An intelligent obstacle avoidance system for logistics AGV based on multi-sensor technology - Google Patents

Abstract

The invention relates to the technical field of obstacle avoidance of a logistics AGV, in particular to an intelligent obstacle avoidance system of the logistics AGV based on a multi-sensor technology, which comprises an obstacle avoidance management platform, an information acquisition unit, a response blocking unit, an obstacle avoidance performance monitoring unit, a vehicle self evaluation unit, an obstacle avoidance blocking analysis unit, a route monitoring unit and an execution response unit; according to the invention, on the premise that the obstacle avoidance efficiency of the logistics AGV is qualified, analysis is carried out from two points of potential obstacle regulation and control and the movement of the logistics AGV, so that the obstacle avoidance risk situation of the AGV is known, namely, the movement state parameters are subjected to self-movement supervision feedback evaluation analysis, so that the influence degree of the logistics AGV on the obstacle avoidance is reduced, the influence of interference factors is reduced by analyzing in a face mode and combining with the potential obstacle regulation and control, and on the premise that the overall obstacle avoidance of the logistics AGV is normal, the obstacle avoidance risk prediction supervision analysis is carried out on the running risk data, so that the obstacle avoidance efficiency of the logistics AGV is predicted, optimized and adjusted in advance.

Description

An intelligent obstacle avoidance system for logistics AGV based on multi-sensor technology

Technical Field

The present invention relates to the field of logistics AGV obstacle avoidance technology, and in particular to a logistics AGV intelligent obstacle avoidance system based on multi-sensor technology.

Background technique

Logistics system technology is an important part of advanced manufacturing technology. From its broad connotation analysis, it can be seen that it has developed from simple material handling in the past to today's comprehensive technology integrating mechanical design, computer science, management, and automatic control technology;

AGV is the abbreviation of automatic guided vehicle, which is a modern intelligent logistics equipment mainly used for unmanned cargo handling. With the advancement of logistics technology, AGV has been widely used in automated logistics systems. However, as far as current technology is concerned, AGV is hindered by obstacles during its movement and cannot avoid them in a timely and effective manner, which in turn affects the operation safety and work efficiency of AGV. In addition, it is impossible to analyze the safety efficiency of AGV's current execution strategy, and thus it is impossible to optimize it in a timely manner, which increases the collision risk of AGV. In addition, it is impossible to supervise AGV itself, which in turn affects the obstacle avoidance efficiency and stability of AGV.

In view of the above technical defects, a solution is now proposed.

Summary of the invention

The purpose of the present invention is to provide a logistics AGV intelligent obstacle avoidance system based on multi-sensor technology to solve the above-mentioned technical defects. Under the premise that the logistics AGV obstacle avoidance efficiency is qualified, the present invention analyzes from two points of potential obstacle regulation and logistics AGV's own movement to understand the AGV obstacle avoidance risk situation, that is, the motion state parameters are self-motion supervision feedback evaluation analysis to determine whether the logistics AGV's own movement interferes with obstacle avoidance, so as to reduce the impact of the logistics AGV's own obstacle avoidance reaction and regulation on obstacle avoidance. The analysis is performed in an information fusion manner, that is, the analysis is performed in a surface manner and combined with potential obstacle regulation to determine whether the overall obstacle avoidance risk of the logistics AGV is too high, so as to reasonably optimize the logistics AGV, avoid collisions of the logistics AGV, and reduce the impact of interference factors. Under the premise that the overall obstacle avoidance of the logistics AGV is normal, the driving risk data is subjected to obstacle avoidance risk prediction supervision analysis to determine the logistics AGV obstacle avoidance risk trend, so as to predict and optimize the adjustment in advance, so as to improve the logistics AGV's obstacle avoidance efficiency and obstacle avoidance stability.

Preferably, the purpose of the present invention can be achieved by the following technical solutions: a logistics AGV intelligent obstacle avoidance system based on multi-sensor technology, including an obstacle avoidance management platform, an information collection unit, a response obstacle unit, an obstacle avoidance performance supervision unit, a vehicle self-evaluation unit, an obstacle avoidance analysis unit, a route supervision unit and an execution response unit;

When the obstacle avoidance management platform generates a transportation management instruction, the transportation management instruction is sent to the information collection unit and the response obstacle unit. After receiving the transportation management instruction, the information collection unit immediately collects the response risk data and motion state parameters of the logistics AGV. The response risk data includes the obstacle avoidance risk value and the response performance value. The motion state parameters include the steering deviation value and the state reaction value. The response risk data and the motion state parameters are sent to the obstacle avoidance performance supervision unit and the vehicle self-evaluation unit respectively. After receiving the response risk data, the obstacle avoidance performance supervision unit immediately performs obstacle avoidance reaction delay risk assessment and analysis on the response risk data, sends the obtained qualified signal to the vehicle self-evaluation unit, and sends the obtained unqualified signal to the execution response unit;

After receiving the transport management instruction, the response obstacle unit immediately collects the obstacle control data of the logistics AGV, which includes the response performance value and the short-touch risk rate, and performs control interference supervision feedback analysis on the obstacle control data, and sends the obtained delay risk assessment coefficient B to the obstacle avoidance analysis unit;

After receiving the qualified signal, the vehicle self-evaluation unit immediately performs self-motion supervision feedback evaluation and analysis on the motion state parameters, sends the obtained safety signal to the obstacle avoidance analysis unit, and sends the obtained risk signal to the execution response unit;

After receiving the delay risk assessment coefficient B and the safety signal, the obstacle avoidance analysis unit immediately enters into information fusion assessment analysis, sends the obtained normal signal to the route supervision unit, and sends the obtained warning signal to the execution response unit;

After receiving the normal signal, the route supervision unit immediately collects the driving risk data, which includes the safety warning value and the safety avoidance value, and performs obstacle avoidance risk prediction supervision analysis on the driving risk data, and sends the obtained warning signal to the execution response unit.

Preferably, the obstacle avoidance reaction delay risk assessment and analysis process of the obstacle avoidance performance supervision unit is as follows:

Set the monitoring cycle and set it as the time threshold, obtain the obstacle avoidance risk value of the logistics AGV within the historical m time thresholds, m is a natural number greater than zero, the obstacle avoidance risk value represents the proportion of the number of obstacle avoidance failures in the historical obstacle avoidance times, and then the product value obtained by normalizing the data with the obstacle avoidance delay warning times, the obstacle avoidance delay warning times represents the number of times the distance between the logistics AGV and the obstacle is less than the preset threshold when the obstacle avoidance warning starts, establish a rectangular coordinate system with the number as the X-axis and the obstacle avoidance risk value as the Y-axis, draw the obstacle avoidance risk value curve by drawing points, and then obtain the ratio of the length of the segment of the obstacle avoidance risk value curve above the preset obstacle avoidance risk value curve to the length of the segment of the obstacle avoidance risk value curve below the preset obstacle avoidance risk value curve, and set it as the obstacle avoidance efficiency risk rate, and compare the obstacle avoidance efficiency risk rate with the preset obstacle avoidance efficiency risk rate threshold value stored in its internal entry:

If the ratio between the obstacle avoidance efficiency risk rate and the preset obstacle avoidance efficiency risk rate threshold is less than 1, a qualified signal is generated;

If the ratio between the obstacle avoidance efficiency risk rate and the preset obstacle avoidance efficiency risk rate threshold is greater than or equal to 1, a failure signal is generated.

Preferably, the regulatory interference monitoring feedback analysis process of the response hindering unit is as follows:

S1: Obtain the response performance value of the logistics AGV within the time threshold. The response performance value indicates the ratio of the number of sensors corresponding to the preset threshold to the total number of sensors obtained by normalizing the product of the maximum operating temperature value of each sensor in the historical logistics AGV exceeding the initial operating temperature value and the duration.

S2: Obtain the total number of collisions of the logistics AGV within the time threshold, and at the same time obtain the product value of the oxidation area of the internal line port of the logistics AGV within the time threshold and the minimum contact area after data normalization, and set it as the interruption risk value, and set the product value of the total number of collisions and the interruption risk value after data normalization as the short touch risk rate, and label the response performance value and the short touch risk rate as XB and DC respectively;

S3: According to the formula The delay risk assessment coefficient is obtained, where a1 and a2 are the preset proportional factor coefficients of the response performance value and the short touch risk rate, respectively, and both a1 and a2 are greater than zero. A3 is the preset correction factor coefficient, and its value is 2.191. B is the delay risk assessment coefficient.

Preferably, the vehicle self-motion supervision feedback evaluation and analysis process of the vehicle self-evaluation unit is as follows:

T1: The actual maximum steering angle of the logistics AGV within the time threshold is obtained, and the portion of the actual maximum steering angle that is lower than the preset steering angle threshold is set as the steering error value. At the same time, the portion of the rotational friction force of the logistics AGV rotation axis that exceeds the preset threshold within the time threshold is obtained and set as the friction resistance value. The product value obtained after data normalization of the steering error value and the friction resistance value is set as the steering deviation value;

T2: Obtain the state reaction value of the logistics AGV within the time threshold. The state reaction value represents the product of the number of operating voltage fluctuations of the logistics AGV and the average reactive power after data normalization, and then the product of the over-temperature operation value after data normalization. The over-temperature operation value represents the proportion of the number of times the operating temperature exceeds the preset operating temperature and the corresponding time is longer than the preset time in the total number of historical operations of the logistics AGV;

T3: Compare and analyze the steering deviation value and the state reaction value with the preset steering deviation value threshold and the preset state reaction value threshold that are stored internally:

If the steering deviation value is less than a preset steering deviation value threshold, and the state reaction value is less than a preset state reaction value threshold, a safety signal is generated;

If the steering deviation value is greater than or equal to a preset steering deviation value threshold, or the state reaction value is greater than or equal to a preset state reaction value threshold, a risk signal is generated.

Preferably, the information fusion evaluation and analysis process of the obstacle avoidance analysis unit is as follows:

Obtain the steering deviation value and the state reaction value within the time threshold, and at the same time obtain the optimization demand evaluation coefficient B within the time threshold, and label the steering deviation value and the state reaction value as ZP and ZF respectively;

According to the formula The potential risk assessment coefficient is obtained, where f1, f2 and f3 are respectively the preset weight factor coefficients of the steering deviation value, the state reaction value and the optimization demand assessment coefficient, f4 is the preset fault tolerance factor coefficient, f1, f2, f3 and f4 are all greater than zero, R is the potential risk assessment coefficient, and the potential risk assessment coefficient R is compared and analyzed with the preset potential risk assessment coefficient threshold value stored in the internal input:

If the ratio between the potential risk assessment coefficient R and the preset potential risk assessment coefficient threshold is less than 1, a normal signal is generated;

If the ratio between the potential risk assessment coefficient R and the preset potential risk assessment coefficient threshold is greater than or equal to 1, an alarm signal is generated.

Preferably, the obstacle avoidance risk prediction supervision and analysis process of the route supervision unit is as follows:

Obtain the driving time period of the logistics AGV within the time threshold and mark it as the analysis time, obtain the number of obstacle avoidance times of the logistics AGV within the analysis time, obtain the obstacle avoidance parameters of each obstacle avoidance time within the analysis time, and the obstacle avoidance parameters include the warning risk distance and the obstacle avoidance safety distance. The warning risk distance represents the driving path distance between the logistics AGV and the obstacle at the moment of the logistics AGV obstacle avoidance warning. The obstacle avoidance safety distance represents the minimum straight-line distance between the logistics AGV and the obstacle. A rectangular coordinate system is established with the number of obstacle avoidances as the X-axis and the warning risk distance and the obstacle avoidance safety distance as the Y-axis. The warning risk distance curve and the obstacle avoidance safety distance curve are drawn by plotting points, respectively, and then the area enclosed by the warning risk distance curve and the X-axis and the area enclosed by the obstacle avoidance safety distance curve and the X-axis are obtained, and they are set as the safety warning value and the safety avoidance value respectively;

Compare and analyze the safety warning value and safety avoidance value with the preset safety warning value threshold and preset safety avoidance value threshold that are stored internally:

If the safety warning value is greater than or equal to the preset safety warning value threshold, or the safety avoidance value is greater than or equal to the preset safety avoidance value threshold, no signal is generated;

If the safety warning value is less than the preset safety warning value threshold, and the safety avoidance value is less than the preset safety avoidance value threshold, a warning signal is generated.

The beneficial effects of the present invention are as follows:

(1) The present invention analyzes the obstacle avoidance risk of the logistics AGV in a point-to-surface manner, thereby improving the obstacle avoidance efficiency of the logistics AGV. The analysis is performed from the point of the logistics AGV obstacle avoidance efficiency, that is, the obstacle avoidance reaction delay risk assessment analysis is performed on the response risk data to determine whether the obstacle avoidance risk of the logistics AGV is too high, so as to perform reasonable optimization processing according to the information feedback to improve the obstacle avoidance reaction performance of the logistics AGV, and at the same time facilitate timely optimization and adjustment of the logistics AGV obstacle avoidance decision;

(2) Under the premise that the obstacle avoidance efficiency of the logistics AGV is qualified, the present invention analyzes the potential obstacle control and the logistics AGV's own movement to understand the AGV's obstacle avoidance risk, that is, the motion state parameters are self-motion supervision feedback evaluation analysis to reduce the impact of the logistics AGV's own obstacle avoidance reaction and regulation on obstacle avoidance. The analysis is performed by information fusion, that is, the analysis is performed by combining the surface method and the potential obstacle control to determine whether the overall obstacle avoidance risk of the logistics AGV is too high, so as to reasonably optimize the logistics AGV, avoid collisions of the logistics AGV, and reduce the impact of interference factors. Under the premise that the overall obstacle avoidance of the logistics AGV is normal, the driving risk data is subjected to obstacle avoidance risk prediction supervision analysis to determine the logistics AGV's obstacle avoidance risk trend, so as to predict and optimize the adjustment in advance to improve the logistics AGV's obstacle avoidance efficiency and stability.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be further described below in conjunction with the accompanying drawings;

Fig. 1 is a flow chart of the system of the present invention;

FIG. 2 is a partial reference diagram of the first embodiment of the present invention.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Embodiment 1: Please refer to Figures 1 to 2. The present invention is a logistics AGV intelligent obstacle avoidance system based on multi-sensor technology, including an obstacle avoidance management platform, an information collection unit, a response obstacle unit, an obstacle avoidance performance supervision unit, a vehicle self-evaluation unit, an obstacle avoidance analysis unit, a route supervision unit and an execution response unit. The obstacle avoidance management platform is connected to the information collection unit and the response obstacle unit in a one-way communication manner, the information collection unit is connected to the obstacle avoidance performance supervision unit and the vehicle self-evaluation unit in a one-way communication manner, the obstacle avoidance performance supervision unit is connected to the execution response unit and the vehicle self-evaluation unit in a one-way communication manner, the vehicle self-evaluation unit is connected to the obstacle avoidance analysis unit and the execution response unit in a one-way communication manner, the response obstacle unit is connected to the obstacle avoidance analysis unit in a one-way communication manner, the obstacle avoidance analysis unit is connected to the route supervision unit and the execution response unit in a one-way communication manner, and the route supervision unit is connected to the execution response unit in a one-way communication manner;

When the obstacle avoidance management platform generates a transportation management instruction, the transportation management instruction is sent to the information collection unit and the response obstacle unit. After receiving the transportation management instruction, the information collection unit immediately collects the response risk data and motion state parameters of the logistics AGV. The response risk data includes the obstacle avoidance risk value and the response performance value. The motion state parameters include the steering deviation value and the state reaction value. The response risk data and the motion state parameters are sent to the obstacle avoidance performance supervision unit and the vehicle self-evaluation unit respectively. After receiving the response risk data, the obstacle avoidance performance supervision unit immediately performs an obstacle avoidance reaction delay risk assessment and analysis on the response risk data to determine whether the obstacle avoidance risk of the logistics AGV is too high, so as to perform reasonable optimization processing according to the information feedback to improve the obstacle avoidance reaction performance of the logistics AGV. The specific obstacle avoidance reaction delay risk assessment and analysis process is as follows:

Set the monitoring cycle and set it as the time threshold, obtain the obstacle avoidance risk value of the logistics AGV within the historical m time thresholds, m is a natural number greater than zero, the obstacle avoidance risk value represents the proportion of the number of obstacle avoidance failures in the historical obstacle avoidance times, and then the product value obtained by normalizing the data with the obstacle avoidance delay warning times, the obstacle avoidance delay warning times represents the number of times the distance between the logistics AGV and the obstacle is less than the preset threshold when the obstacle avoidance warning starts, establish a rectangular coordinate system with the number as the X-axis and the obstacle avoidance risk value as the Y-axis, draw the obstacle avoidance risk value curve by drawing points, and then obtain the ratio of the length of the segment of the obstacle avoidance risk value curve above the preset obstacle avoidance risk value curve to the length of the segment of the obstacle avoidance risk value curve below the preset obstacle avoidance risk value curve, and set it as the obstacle avoidance efficiency risk rate, and compare the obstacle avoidance efficiency risk rate with the preset obstacle avoidance efficiency risk rate threshold value stored in its internal entry:

If the ratio between the obstacle avoidance efficiency risk rate and the preset obstacle avoidance efficiency risk rate threshold is less than 1, a qualified signal is generated and sent to the vehicle's own evaluation unit;

If the ratio between the obstacle avoidance efficiency risk rate and the preset obstacle avoidance efficiency risk rate threshold is greater than or equal to 1, a failure signal is generated and sent to the execution response unit. After receiving the failure signal, the execution response unit immediately performs the preset warning operation corresponding to the failure signal, so as to optimize and adjust the logistics AGV obstacle avoidance decision in time to improve the obstacle avoidance efficiency of the logistics AGV.

After receiving the transport management instruction, the response obstacle unit immediately collects the obstacle control data of the logistics AGV. The obstacle control data includes the response performance value and the short-touch risk rate, and performs control interference supervision feedback analysis on the obstacle control data to understand the impact of the obstacle control factors on the logistics AGV obstacle avoidance, so as to provide data support for subsequent management. The specific control interference supervision feedback analysis process is as follows:

The response performance value of the logistics AGV within the time threshold is obtained. The response performance value indicates the ratio of the number of sensors corresponding to the preset threshold to the total number of sensors obtained by the product of the maximum operating temperature value of each sensor in the historical logistics AGV exceeding the initial operating temperature value and the duration after data normalization. It should be noted that the larger the value of the response performance value, the greater the risk of obstacle avoidance delay of the logistics AGV;

The total number of collisions of the logistics AGV within the time threshold is obtained, and the product value of the oxidation area of the internal line port of the logistics AGV and the minimum contact area after data normalization is obtained within the time threshold, and it is set as the interruption risk value. The product value of the total number of collisions and the interruption risk value after data normalization is set as the short-touch risk rate. It should be noted that the larger the value of the short-touch risk rate, the greater the risk of obstacle avoidance delay of the logistics AGV. The response performance value and the short-touch risk rate are labeled as XB and DC respectively;

According to the formula The delay risk assessment coefficient is obtained, where a1 and a2 are the preset proportional factor coefficients of the response performance value and the short touch risk rate, respectively. The proportional factor coefficients are used to correct the deviations of various parameters in the formula calculation process, so as to make the calculation results more accurate. Both a1 and a2 are greater than zero. A3 is the preset correction factor coefficient, which is 2.191. B is the delay risk assessment coefficient. The delay risk assessment coefficient B is sent to the obstacle avoidance analysis unit.

Embodiment 2: After receiving the qualified signal, the vehicle self-evaluation unit immediately performs self-motion supervision feedback evaluation and analysis on the motion state parameters to determine whether the logistics AGV's own motion interferes with obstacle avoidance, so as to provide timely early warning feedback management to reduce the collision risk of the logistics AGV and improve the transportation safety of the logistics AGV. The specific self-motion supervision feedback evaluation and analysis process is as follows:

The actual maximum steering angle of the logistics AGV within the time threshold is obtained, and the portion of the actual maximum steering angle that is lower than the preset steering angle threshold is set as the steering error value. At the same time, the portion of the rotational friction force of the logistics AGV rotation axis that exceeds the preset threshold within the time threshold is obtained and set as the friction resistance value. The product value obtained after data normalization of the steering error value and the friction resistance value is set as the steering deviation value. It should be noted that the larger the value of the steering deviation value, the higher the risk of the logistics AGV's own movement obstacle avoidance;

The state reaction value of the logistics AGV within the time threshold is obtained. The state reaction value represents the product of the number of operating voltage fluctuations of the logistics AGV and the mean value of reactive power after data normalization, and then the product of the over-temperature operation value after data normalization. The over-temperature operation value represents the proportion of the number of times the operating temperature exceeds the preset operating temperature and the corresponding time is longer than the preset time in the total number of historical operations of the logistics AGV. It should be noted that the larger the value of the state reaction value, the higher the risk of obstacle avoidance of the logistics AGV itself;

The steering deviation value and the state reaction value are compared and analyzed with the preset steering deviation value threshold and the preset state reaction value threshold that are stored in the internal input:

If the steering deviation value is less than the preset steering deviation value threshold, and the state reaction value is less than the preset state reaction value threshold, a safety signal is generated and sent to the obstacle avoidance analysis unit;

If the steering deviation value is greater than or equal to the preset steering deviation value threshold, or the state reaction value is greater than or equal to the preset state reaction value threshold, a risk signal is generated and sent to the execution response unit. After receiving the risk signal, the execution response unit immediately performs the preset warning operation corresponding to the risk signal, so as to timely maintain and manage the logistics AGV, so as to reduce the impact of the logistics AGV's own obstacle avoidance reaction and regulation on obstacle avoidance;

After receiving the delay risk assessment coefficient B and the safety signal, the obstacle avoidance analysis unit immediately enters the information fusion evaluation analysis to determine whether the overall obstacle avoidance risk of the logistics AGV is too high, so as to reasonably optimize the logistics AGV. The specific information fusion evaluation analysis process is as follows:

Obtain the steering deviation value and the state reaction value within the time threshold, and at the same time obtain the optimization demand evaluation coefficient B within the time threshold, and label the steering deviation value and the state reaction value as ZP and ZF respectively;

According to the formula The potential risk assessment coefficient is obtained, where f1, f2 and f3 are respectively the preset weight factor coefficients of the steering deviation value, the state reaction value and the optimization demand assessment coefficient, f4 is the preset fault tolerance factor coefficient, f1, f2, f3 and f4 are all greater than zero, R is the potential risk assessment coefficient, and the potential risk assessment coefficient R is compared and analyzed with the preset potential risk assessment coefficient threshold value stored in the internal input:

If the ratio between the potential risk assessment coefficient R and the preset potential risk assessment coefficient threshold is less than 1, a normal signal is generated and sent to the route supervision unit;

If the ratio between the potential risk assessment coefficient R and the preset potential risk assessment coefficient threshold is greater than or equal to 1, an alarm signal is generated and sent to the execution response unit. After receiving the alarm signal, the execution response unit immediately performs the preset warning operation corresponding to the alarm signal, so as to timely maintain and manage the logistics AGV, improve the obstacle avoidance efficiency of the logistics AGV, avoid collisions of the logistics AGV, and reduce the impact of interference factors;

After receiving the normal signal, the route supervision unit immediately collects the driving risk data, which includes the safety warning value and the safety avoidance value, and performs obstacle avoidance risk prediction and supervision analysis on the driving risk data to determine the obstacle avoidance risk trend of the logistics AGV, so as to predict and optimize the adjustment in advance to improve the obstacle avoidance efficiency and stability of the logistics AGV. The specific obstacle avoidance risk prediction and supervision analysis process is as follows:

Obtain the driving time period of the logistics AGV within the time threshold and mark it as the analysis time, obtain the number of obstacle avoidance times of the logistics AGV within the analysis time, obtain the obstacle avoidance parameters of each obstacle avoidance time within the analysis time, and the obstacle avoidance parameters include the warning risk distance and the obstacle avoidance safety distance. The warning risk distance represents the driving path distance between the logistics AGV and the obstacle at the moment of the logistics AGV obstacle avoidance warning. The obstacle avoidance safety distance represents the minimum straight-line distance between the logistics AGV and the obstacle. Take the number of obstacle avoidances as the X-axis, and the warning risk distance and the obstacle avoidance safety distance as the Y-axis to establish a rectangular coordinate system. Draw the warning risk distance curve and the obstacle avoidance safety distance curve by drawing points, and then obtain the area enclosed by the warning risk distance curve and the X-axis and the area enclosed by the obstacle avoidance safety distance curve and the X-axis, and set them as the safety warning value and the safety avoidance value respectively. It should be noted that the safety warning value and the safety avoidance value are two influencing parameters that reflect the trend of the logistics AGV's driving obstacle avoidance risk, which are helpful to predict the logistics AGV's driving obstacle avoidance risk in advance.

Compare and analyze the safety warning value and safety avoidance value with the preset safety warning value threshold and preset safety avoidance value threshold that are stored internally:

If the safety warning value is greater than or equal to the preset safety warning value threshold, or the safety avoidance value is greater than or equal to the preset safety avoidance value threshold, no signal is generated;

If the safety warning value is less than the preset safety warning value threshold, and the safety avoidance value is less than the preset safety avoidance value threshold, a warning signal is generated and sent to the execution response unit. After receiving the warning signal, the execution response unit immediately performs the preset warning operation corresponding to the warning signal, so as to reasonably adjust the logistics AGV to avoid collision during the subsequent driving process of the logistics AGV, which helps to improve the prediction of the collision risk of the logistics AGV, and further helps to improve the obstacle avoidance efficiency of the logistics AGV;

In summary, the present invention analyzes the obstacle avoidance risk of the logistics AGV in a point-to-surface manner, thereby improving the obstacle avoidance efficiency of the logistics AGV. The analysis is performed from the point of the logistics AGV obstacle avoidance efficiency, that is, the obstacle avoidance reaction delay risk assessment analysis is performed on the response risk data to determine whether the obstacle avoidance risk of the logistics AGV is too high, so as to perform reasonable optimization processing according to the information feedback to improve the obstacle avoidance reaction performance of the logistics AGV, and at the same time facilitate timely optimization and adjustment of the logistics AGV obstacle avoidance decision. Under the premise that the logistics AGV obstacle avoidance efficiency is qualified, the obstacle avoidance risk of the AGV is understood by analyzing from two points of potential obstacle regulation and logistics AGV's own movement, that is, the motion state parameters are analyzed for their own movement. Dynamic supervision feedback evaluation and analysis is carried out to determine whether the logistics AGV's own movement interferes with obstacle avoidance, so as to reduce the impact of the logistics AGV's own obstacle avoidance reaction and regulation on obstacle avoidance. The analysis is carried out through information fusion, that is, through the surface method and combined with potential obstacle regulation to determine whether the overall obstacle avoidance risk of the logistics AGV is too high, so as to reasonably optimize the logistics AGV, avoid collisions of the logistics AGV, and reduce the impact of interference factors. Under the premise that the overall obstacle avoidance of the logistics AGV is normal, the driving risk data is subjected to obstacle avoidance risk prediction and supervision analysis to determine the obstacle avoidance risk trend of the logistics AGV, so as to predict and optimize the adjustments in advance, so as to improve the obstacle avoidance efficiency and stability of the logistics AGV.

The threshold is set to facilitate comparison. The threshold depends on the amount of sample data and the number of bases set by technicians in this field for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value.

The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims (6)

1. The logistics AGV intelligent obstacle avoidance system based on the multi-sensor technology is characterized by comprising an obstacle avoidance management platform, an information acquisition unit, a response blocking unit, an obstacle avoidance performance monitoring unit, a vehicle self evaluation unit, an obstacle avoidance blocking analysis unit, a route monitoring unit and an execution response unit;

When the obstacle avoidance management platform generates an operation instruction, the operation instruction is sent to an information acquisition unit and a response blocking unit, the information acquisition unit immediately acquires response risk data and motion state parameters of the logistics AGV after receiving the operation instruction, the response risk data comprises an obstacle avoidance risk value and a response representation value, the motion state parameters comprise a steering deviation value and a state response value, the response risk data and the motion state parameters are respectively sent to an obstacle avoidance performance monitoring unit and a vehicle self-assessment unit, the obstacle avoidance performance monitoring unit immediately carries out obstacle avoidance response delay risk assessment analysis on the response risk data after receiving the response risk data, and sends an obtained qualified signal to the vehicle self-assessment unit and an obtained unqualified signal to an execution response unit;

The response blocking unit immediately acquires blocking regulation data of the logistics AGV after receiving the pipe conveying instruction, wherein the blocking regulation data comprises a response representation value and a short touch risk rate, carries out regulation and interference supervision feedback analysis on the blocking regulation data, and sends an obtained delay risk assessment coefficient B to the obstacle avoidance analysis unit;

the vehicle self-evaluation unit immediately performs self-motion supervision feedback evaluation analysis on the motion state parameters after receiving the qualified signals, sends the obtained safety signals to the obstacle avoidance and obstruction analysis unit, and sends the obtained risk signals to the execution response unit;

The obstacle avoidance and obstruction analysis unit immediately enters information fusion evaluation analysis after receiving the delay risk evaluation coefficient B and the safety signal, sends an obtained normal signal to the route supervision unit, and sends an obtained alarm signal to the execution response unit;

The route supervision unit receives the normal signals, immediately collects running risk data, wherein the running risk data comprises a safety early warning value and a safety avoiding value, carries out obstacle avoidance risk prediction supervision analysis on the running risk data, and sends the obtained early warning signals to the execution response unit.

2. The logistics AGV intelligent obstacle avoidance system based on the multi-sensor technology according to claim 1, wherein the obstacle avoidance response delay risk assessment analysis process of the obstacle avoidance performance supervision unit is as follows:

Setting a monitoring period, setting the monitoring period as a time threshold, acquiring a barrier avoiding risk value of the logistics AGV in a history m time thresholds, wherein m is a natural number larger than zero, the barrier avoiding risk value represents a ratio of the number of barrier avoiding failures in the history barrier avoiding times, then setting the ratio to a product value obtained after data normalization processing of the barrier avoiding delay early warning times, wherein the distance between the logistics AGV and the barrier is smaller than the number corresponding to a preset threshold when the barrier avoiding starts to early warning, setting an orthogonal coordinate system with the number as an X axis, setting a barrier avoiding risk value as a Y axis, drawing a barrier avoiding risk value curve in a description mode, further acquiring a ratio of the length of a line segment above the barrier avoiding risk value curve to the length of a line segment below the preset barrier avoiding risk value curve, setting the ratio to a barrier avoiding efficiency risk rate, and comparing the barrier avoiding efficiency risk rate with a preset barrier efficiency rate threshold stored in the barrier avoiding efficiency rate by the internal record of the barrier efficiency rate.

If the ratio between the obstacle avoidance efficiency risk rate and the preset obstacle avoidance efficiency risk rate threshold is smaller than 1, generating a qualified signal;

if the ratio of the obstacle avoidance efficiency risk rate to the preset obstacle avoidance efficiency risk rate threshold is greater than or equal to 1, generating a disqualified signal.

3. The logistics AGV intelligent obstacle avoidance system based on the multi-sensor technology according to claim 1, wherein the regulatory interference supervision feedback analysis process of the response blocking unit is as follows:

S1: obtaining a response representation value of the logistic AGV in the time threshold, wherein the response representation value represents the ratio of the number of sensors corresponding to the preset threshold to the total number of sensors, which is obtained by carrying out data normalization processing on the part of the maximum operation temperature value of each sensor in the historical logistic AGV exceeding the initial operation temperature value and the duration;

S2: acquiring the total collision times of the logistics AGVs in a time threshold, acquiring a product value obtained by carrying out data normalization processing on the oxidation area and the contact minimum area of the internal circuit ports of the logistics AGVs in the time threshold, setting the product value as an interruption risk value, setting the product value obtained by carrying out data normalization processing on the total collision times and the interruption risk value as a short-touch risk rate, and respectively marking a response representation value and the short-touch risk rate as XB and DC;

S3: according to the formula Obtaining a delay risk assessment coefficient, wherein a1 and a2 are preset scale factor coefficients of a response representation value and a short touch risk rate respectively, a1 and a2 are both larger than zero, a3 is a preset correction factor coefficient, the value is 2.191, and B is the delay risk assessment coefficient.

4. The logistics AGV intelligent obstacle avoidance system based on the multi-sensor technology according to claim 1, wherein the self-motion supervision feedback evaluation analysis process of the vehicle self-evaluation unit is as follows:

T1: acquiring an actual steering maximum angle of the logistics AGV in a time threshold, setting a part of the actual steering maximum angle which is lower than a preset steering angle threshold as a steering error value, acquiring a part of the rotational friction force of a rotating shaft of the logistics AGV in the time threshold which exceeds the preset threshold, setting the part as a friction resistance value, and setting a product value obtained by carrying out data normalization processing on the steering error value and the friction resistance value as a steering deviation value;

T2: acquiring a state reaction value of the logistics AGV in a time threshold, wherein the state reaction value represents a product value obtained by carrying out data normalization processing on the fluctuation times of the operation voltage of the logistics AGV and a reactive power average value, and then carrying out data normalization processing on the product value and an over-temperature operation value, and the over-temperature operation value represents a ratio of times that the operation temperature exceeds a preset operation temperature by a time length longer than a preset time length in the total historical operation times of the logistics AGV;

T3: comparing the steering deviation value and the state response value with a preset steering deviation value threshold value and a preset state response value threshold value which are recorded and stored in the steering deviation value and the state response value:

If the steering deviation value is smaller than a preset steering deviation value threshold value and the state response value is smaller than a preset state response value threshold value, generating a safety signal;

And if the steering deviation value is greater than or equal to a preset steering deviation value threshold value or the state response value is greater than or equal to a preset state response value threshold value, generating a risk signal.

5. The logistics AGV intelligent obstacle avoidance system based on the multi-sensor technology according to claim 1, wherein the information fusion evaluation analysis process of the obstacle avoidance block analysis unit is as follows:

Acquiring a steering deviation value and a state response value in a time threshold, and acquiring an optimization demand evaluation coefficient B in the time threshold, wherein the steering deviation value and the state response value are respectively marked as ZP and ZF;

According to the formula Obtaining a potential risk assessment coefficient, wherein f1, f2 and f3 are respectively preset weight factor coefficients of a steering deviation value, a state reaction value and an optimization demand assessment coefficient, f4 is a preset fault tolerance factor coefficient, f1, f2, f3 and f4 are all larger than zero, R is a potential risk assessment coefficient, and the potential risk assessment coefficient R is compared with a preset potential risk assessment coefficient threshold value recorded and stored in the potential risk assessment coefficient R:

if the ratio between the potential risk assessment coefficient R and the preset potential risk assessment coefficient threshold is smaller than 1, generating a normal signal;

And if the ratio between the potential risk assessment coefficient R and the preset potential risk assessment coefficient threshold value is more than or equal to 1, generating an alarm signal.

6. The logistics AGV intelligent obstacle avoidance system based on the multi-sensor technology according to claim 1, wherein the obstacle avoidance risk prediction, supervision and analysis process of the route supervision unit is as follows:

Acquiring a running time period of the logistics AGV in a time threshold, marking the running time period as analysis time period, acquiring obstacle avoidance times of the logistics AGV in the analysis time period, acquiring obstacle avoidance parameters of each obstacle avoidance time in the analysis time period, wherein the obstacle avoidance parameters comprise an early warning risk distance and an obstacle avoidance safety distance, the early warning risk distance represents a running path distance between the logistics AGV and an obstacle at the obstacle avoidance early warning moment of the logistics AGV, the obstacle avoidance safety distance represents a minimum straight line distance between the logistics AGV and the obstacle, the obstacle avoidance time is taken as an X axis, a rectangular coordinate system is established by taking the early warning risk distance and the obstacle avoidance safety distance as Y axes respectively, an early warning risk distance curve and an obstacle avoidance safety distance curve are drawn respectively in a dot drawing mode, and further an area surrounded by the early warning risk distance curve and the X axis and an area surrounded by the obstacle avoidance safety distance curve and are respectively set as a safety early warning value and a safety avoidance value;

comparing the safety early warning value and the safety avoidance value with a preset safety early warning value threshold value and a preset safety avoidance value threshold value which are recorded and stored in the safety early warning value and the safety avoidance value, and analyzing the safety early warning value and the safety avoidance value:

If the safety early warning value is greater than or equal to a preset safety early warning value threshold or the safety avoidance value is greater than or equal to a preset safety avoidance value threshold, no signal is generated;

if the safety early warning value is smaller than the preset safety early warning value threshold value and the safety avoidance value is smaller than the preset safety avoidance value threshold value, generating an early warning signal.