CN109343064B - Mining truck obstacle detection system and detection method - Google Patents
Mining truck obstacle detection system and detection method Download PDFInfo
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- CN109343064B CN109343064B CN201811408803.5A CN201811408803A CN109343064B CN 109343064 B CN109343064 B CN 109343064B CN 201811408803 A CN201811408803 A CN 201811408803A CN 109343064 B CN109343064 B CN 109343064B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
Abstract
The invention discloses a mining truck obstacle detection system, which comprises: the driving controller is used for controlling the driving of the mining truck; the detection device is used for detecting obstacles on the driving route of the mining truck; the environment detection device is used for collecting at least one environment factor which can influence the obstacle detection result; and the processor can evaluate the confidence degree of the obstacle detection result according to the at least one environmental factor acquired by the environment detection device, determine the obstacle condition according to the confidence degree of the detection result, and enable the driving controller to control the mining truck to make corresponding driving actions based on the obstacle condition. In a complex environment of a mine scene, the invention ensures that the mining truck can still not be influenced by environmental factors, accurately detects obstacles possibly influencing the running of the vehicle, and makes corresponding running actions, thereby ensuring the running safety and the running efficiency of the mining truck.
Description
Technical Field
The invention relates to the field of vehicle engineering, in particular to a mining truck obstacle detection system.
Background
Along with continuous development of unmanned and intelligent vehicle driving fields, more and more automobiles depend on sensors, and unmanned functions are realized by aiming at complex driving algorithms generated by the detection results of the sensors. For unmanned vehicles, detection of an obstacle in the path of travel is critical in determining whether the unmanned technology is safe or not. The related unmanned vehicle adopts a plurality of groups of visual sensors, radars and other sensors to continuously detect the surrounding environment of the vehicle, and the data detected by the plurality of groups of sensors are fused according to a complex algorithm so as to judge whether an obstacle possibly causing potential safety hazards exists in the running path of the vehicle.
For urban roads or rural roads, the road surface condition is better, road dust and sand are less, the visibility is high, and the detectability is strong, so that the visual sensor and the radar can play a role to the greatest extent, and timely detection and timely early warning of obstacles are realized. In addition, no matter the urban road or the rural road, the obstacles on the vehicle driving path are mainly pedestrians or other vehicles, the vehicle driving path has certain active avoidance capability, and when the pedestrians or other vehicle types of obstacles are encountered in the form process, the unmanned execution logic is simpler, namely the vehicle driving path actively decelerates or even stops according to the traffic rules.
For mining trucks with mining scenes in the driving environment, the road surface is paved by slag and sand, and the dust on the road surface is serious; mine roads generally do not have specially designed drainage arrangements, and road surface conditions are more prone to deterioration in rainy and snowy weather; in addition, a large amount of water mist and water vapor generated by the sprinkling operation of the sprinkling truck on the mine road can have adverse effects on the unmanned detector. For example, a laser radar has the advantages of wide detection range, high detection precision and the like, is widely applied to unmanned vehicles, is influenced by factors such as pavement dust, sand dust, rain and snow, water mist and the like in mine scenes, has a large number of noise points in detection results, and can not work stably and reliably.
In addition, in mine scenes, road obstacles affecting the travel of vehicles include ores from which the preceding vehicles fall, in addition to other vehicles. The size and shape of the ore are different, so that the unmanned algorithm needs to accurately evaluate the influence of the ore with different sizes and shapes on the running of the vehicle, namely, the vehicle is prevented from stopping in time before the obstacle can not pass, the vehicle speed is reduced before the obstacle can be decelerated, and the running speed of the vehicle is ensured before the obstacle which does not influence the passing, so that the unmanned algorithm is complex, and the adaptability to the environment with an extreme form is not strong.
Disclosure of Invention
At least one object of the invention is to provide a mining truck obstacle detection system which can adapt to complex environments in mine scenes and accurately detect obstacles which possibly affect the running of a vehicle. The preferred technical solutions of the technical solutions provided by the present invention can produce a plurality of technical effects described below.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the invention provides a mining truck obstacle detection system, which comprises: the driving controller is used for controlling the driving of the mining truck; the detection device is used for detecting obstacles on the driving route of the mining truck; the environment detection device is used for collecting at least one environment factor which can influence the obstacle detection result; and the processor is respectively connected with the running controller, the detection device and the environment detection device in a signal way, can evaluate the confidence level of the obstacle detection result according to the at least one environment factor acquired by the environment detection device, determines the obstacle condition according to the confidence level of the detection result, and enables the running controller to control the mining truck to make corresponding running action based on the obstacle condition.
As an optimization of any one of the technical solutions or any one of the optimized technical solutions provided in the foregoing or the following, the environment detection device includes: the dust sensor is arranged on the mining truck and can detect the dust concentration of the surrounding environment of the mining truck. As an optimization of any one of the technical solutions or any one of the optimized technical solutions provided in the foregoing or the following, the environment detection device includes: the rainfall sensor is in communication connection with the processor and can measure rainfall intensity of the area where the mining truck is located; the sprinkler positioning device is in communication connection with the processor and can position the sprinkler; and/or the custom device is in communication connection with the processor and can manually input environmental factors influencing the mining truck.
As an optimization of any one of the technical solutions or any one of the optimized technical solutions provided in the foregoing or the following, the environment detection device includes: the remote server is respectively in communication connection with the processor and the environment detection device and can transmit the environment factors acquired by the environment detection device to the processor.
As an optimization of any of the technical solutions or any of the optimized technical solutions provided in the foregoing or in the following, the processor includes: a first calculation module for normalizing the at least one environmental factor acquired by the environmental detection device; the second calculation module is used for carrying out weighting processing on the confidence degree of the obstacle detection result obtained by the detection device based on the normalization result of the at least one environmental factor obtained by the first calculation module; and the third calculation module is used for judging the influence of the obstacle on the running of the vehicle according to the weighted processing result obtained by the second calculation module, and enabling the running controller to control the mining truck to make corresponding running actions based on the influence of the obstacle on the running of the vehicle.
As an optimization of any one of the technical solutions or any one of the optimized technical solutions provided in the foregoing or in the following, the detection device includes: a short range lidar for detecting a road surface obstacle, having a first detection angle directed toward a short range road surface of the mining truck; and a remote lidar for detecting a vehicle obstacle, having a second detection angle directed toward a remote surface of the mining truck.
As an optimization of any of the technical solutions or any of the optimized technical solutions provided in the foregoing or in the following, the processor includes: a fourth calculation module for meshing the travel path of the mining truck into a detection grid; a fifth calculation module, configured to perform confidence degree weighting processing on detection results of the short-range lidar and/or the long-range lidar in each detection grid according to the at least one environmental factor; and the sixth calculation module is used for comparing the processing result obtained by the fifth calculation module with a preset result and enabling the driving controller to control the mining truck to make corresponding driving actions according to the comparison result.
As an optimization of any one of the technical solutions or any one of the optimized technical solutions provided in the foregoing or in the following, the detection device includes: the millimeter wave radar is used for detecting vehicle obstacles and has a third detection angle pointing to the remote pavement of the mining truck.
As an optimization of any of the technical solutions or any of the optimized technical solutions provided in the foregoing or in the following, the processor includes: a seventh calculation module for meshing the travel path of the mining truck into a detection grid; an eighth calculation module, configured to perform confidence degree weighting processing on the detection result of the short-range laser radar and/or the long-range laser radar in each detection grid according to the at least one environmental factor, and not perform the confidence degree weighting processing on the detection result of the millimeter wave radar in each detection grid; and the ninth calculation module is used for comparing the processing result obtained by the eighth calculation module with a preset result and enabling the driving controller to control the mining truck to make corresponding driving actions according to the comparison result.
As an optimization of any of the technical solutions provided in the foregoing or in the following or any of the optimized technical solutions, the near-range lidar includes a multi-line lidar, which is disposed at a front and a rear of the mining truck and has a first detection angle pointing to a near-range road surface of the mining truck, where the first detection angle can satisfy that when the near-range lidar detects a road surface obstacle, a distance between the mining truck and the road surface obstacle is greater than a minimum braking distance of the mining truck.
As any one of the technical solutions provided above or below or any one of the optimized technical solutions provided below, the remote lidar includes a surface lidar and/or a multi-line lidar, at least two of the remote lidars are disposed at a head position of the mining truck, the remote lidar has a second detection angle pointing to a remote road surface of the mining truck, and the second detection angle can satisfy that when the remote lidar detects a vehicle obstacle, a distance between the mining truck and the vehicle obstacle is greater than a minimum braking distance of the mining truck.
As any one of the technical schemes provided in the foregoing or the following or the optimization of any one of the optimized technical schemes, at least three millimeter wave radars are arranged at the head position of the mining truck, at least one millimeter wave radar is arranged at the tail position of the mining truck, the millimeter wave radar has a third detection angle pointing to the long-distance pavement of the mining truck, and the third detection angle can meet the condition that when the millimeter wave radar detects a vehicle obstacle, the distance between the mining truck and the vehicle obstacle is larger than the minimum braking distance of the mining truck.
As an optimization of any of the technical solutions or any of the optimized technical solutions provided in the foregoing or in the following, the processor includes: and a tenth calculation module for decelerating and stopping the mining truck when the confidence level of the detection result estimated according to the at least one environmental factor is lower than a confidence level set value.
The invention also provides a mining truck obstacle detection method, which comprises the following steps: detecting an obstacle on a driving route of the mining truck; collecting at least one environmental factor that affects the detection of the obstacle; and evaluating the confidence degree of the obstacle detection result according to the at least one environmental factor, determining the obstacle condition according to the confidence degree of the detection result, and controlling the mining truck to make corresponding driving actions based on the obstacle condition.
As an optimization of any one of the technical solutions or any one of the optimized technical solutions provided in the foregoing or the following, the detection method includes: and decelerating and stopping the mining truck when the confidence level of the detection result estimated according to the at least one environmental factor is lower than a confidence level set value.
The invention also discloses a mining truck, which comprises any mining truck obstacle detection system.
Based on the technical scheme, the embodiment of the invention evaluates the confidence level of the obstacle detection result by collecting the environmental factors which can influence the obstacle detection result, and determines the obstacle condition based on the confidence level of the detection result, so that the mining truck still can accurately detect the obstacle which can influence the running of the vehicle without being influenced by the environmental factors in the complex environment of the mine scene, and the corresponding running action is made, thereby ensuring the running safety and the running efficiency of the mining truck.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic diagram of a mining truck obstacle detection system according to an embodiment of the present application;
FIG. 2 is a schematic side view of a detection device in a mining truck obstacle detection system according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a front view of the installation position of a detection device in a mining truck obstacle detection system according to an embodiment of the present application;
fig. 4 is a schematic top view angle diagram of an installation position of a detection device in a mining truck obstacle detection system according to an embodiment of the present application;
FIG. 5 is a schematic view of a rear view angle of a detecting device in a mining truck obstacle detecting system according to an embodiment of the present application;
fig. 6 is a schematic diagram of a detection range and a detection angle of a detection device in a mining truck obstacle detection system according to an embodiment of the present application;
fig. 7 is a schematic diagram of a method for detecting an obstacle of a mining truck according to an embodiment of the present application;
Reference numerals: 1. a short range lidar; 2. a long-range laser radar; 3. millimeter wave radar.
Detailed Description
The following description of the invention and the differences between the present invention and the prior art will be understood with reference to the accompanying drawings and text. The following describes the invention in further detail, including preferred embodiments, by way of the accompanying drawings and by way of examples of some alternative embodiments of the invention.
It should be noted that: any technical feature and any technical solution in this embodiment are one or several of various optional technical features or optional technical solutions, and in order to describe brevity, all of the optional technical features and the optional technical solutions of the present invention cannot be exhausted in this document, and it is inconvenient for an implementation of each technical feature to emphasize that it is one of various optional implementations, so those skilled in the art should know: any one of the technical means provided by the invention can be replaced or any two or more of the technical means or technical features provided by the invention can be mutually combined to obtain a new technical scheme.
Any technical features and any technical solutions in the present embodiment do not limit the protection scope of the present invention, and the protection scope of the present invention should include any alternative technical solution that can be conceived by a person skilled in the art without performing creative efforts, and a new technical solution obtained by combining any two or more technical means or technical features provided by the present invention with each other by a person skilled in the art.
The technical scheme provided by the invention is described in more detail below with reference to figures 1-7.
As shown in fig. 1, the mining truck obstacle detection system provided by the invention includes: the driving controller is used for controlling the driving of the mining truck; the detection device is used for detecting obstacles on the driving route of the mining truck; the environment detection device is used for collecting at least one environment factor which can influence the obstacle detection result; and the processor is respectively connected with the running controller, the detection device and the environment detection device in a signal way, can evaluate the confidence level of the obstacle detection result according to the at least one environment factor acquired by the environment detection device, determines the obstacle condition according to the confidence level of the detection result, and enables the running controller to control the mining truck to make corresponding running action based on the obstacle condition.
The detection device is arranged on the mining truck and used for detecting obstacles on the driving route of the mining truck. The driving route of the mining truck comprises possible driving paths such as forward, backward or turning. The detection device can be directly arranged on the mining truck, for example, on a shell, a cover body or a bumper of the mining truck; the device can also be arranged on the mining truck in an overhanging way through other mechanisms, for example, the device can be arranged on the mining truck in an auxiliary way through a telescopic and/or steerable mechanical arm, so that a larger detection range and more flexible adjustment of a detection angle can be realized.
The environment detection device is used for detecting environment factors which can influence the detection result of the detection device, wherein the environment factors comprise interference factors which can influence the detection result adversely and enhancement factors which can influence the detection result favorably, so that the comprehensive influence effect of the environment factors on the detection result can be estimated more comprehensively. The environment detection device can be arranged on the mining truck to detect the environment factors near the mining truck in real time and in a short distance, and achieve higher accuracy; and the device can also be arranged outside the mining truck so as to measure the environmental factors in the surrounding environment of the mining truck more conveniently and at lower cost.
Fig. 1 shows an embodiment of the environment detection device in which part of the environment detection device is mounted to the mining truck and part of the environment detection device is mounted outside the mining truck to be more suitable for measuring characteristics of corresponding environmental factors to be detected.
Because dust has extremely strong interference to the detection device, especially the detection device using optics as a medium, and more ores and slag are produced in the mine environment, the mine roads are paved by the slag. In the running process of the mining truck with a large load weight, dust on a mine road is easy to diffuse into air, so that the measurement result of the dust directly influences the accuracy of the detection result evaluation of the detection device. And because the dust has stronger geographic non-uniformity, the running of other vehicles on the mine road has stronger unsteady characteristic, therefore, the environment detection device comprises: the dust sensor is arranged on the mining truck, and can detect the dust concentration of the surrounding environment of the mining truck so as to enable the correction of the detection result of the detection device to be more accurate.
Besides dust, the environmental factors which have a great influence on the detection device further comprise water mist and water vapor, and based on the water mist and the water vapor, the environment detection device comprises: the rainfall sensor is in communication connection with the processor and can measure rainfall intensity of the area where the mining truck is located; the sprinkler positioning device is in communication connection with the processor and can position the sprinkler; and/or the custom device is in communication connection with the processor and can manually input environmental factors influencing the mining truck.
One of the main factors of water mist and water vapor is rainfall. For the measurement of rainfall, the rainfall is substantially uniform over a certain geographical range, and the measurement of rainfall in a non-bumpy ground area has a higher accuracy. And the rainfall sensor is arranged on the mining truck, so that the rainfall sensor is easily influenced by the driving speed, the driving direction and the road surface bump of the mining truck, and inaccurate measurement is caused. Therefore, the rainfall intensity of the area where the mining truck is located is measured outside the mining truck, and the rainfall can be obtained more accurately by transmitting the measurement result to the processor.
Besides the fact that rainfall can generate more water mist and water vapor on a mine road, a sprinkler for sprinkling water on the mine road can also influence the measuring device. And for a certain area on a mine road, the concentration of water mist and water vapor is extremely different before and after the sprinkler passes. Therefore, to accurately evaluate the possible influence of the sprinkler on the detection device, it is no longer meaningful to measure the concentration of water mist and water vapor in a certain geographical range, but the distance between the sprinkler and the mining truck should be taken into consideration. Specifically, the spacing between the sprinkler and the mining truck is measured by positioning the sprinkler and the mining truck, and the influence of the sprinkler on the detection device is evaluated by the spacing. For example, the concentration of water mist and water vapor at different intervals of 5 meters, 10 meters, 15 meters and the like before and after the sprinkler is measured through experiments, and the accuracy of the detection result of the detection device at the corresponding concentration level is achieved through the evaluation of the accuracy of the detection result through the interval between the sprinkler and the mining truck.
Other environmental factors which can influence water mist and water vapor include snowfall, hail and the like, and the environmental factors are difficult to directly measure by a sensor, so that the influence of the corresponding environmental factors on the confidence degree of the detection result of the detection device is manually input through the self-defining device. Besides, the user-defined device can input other environmental factors which are difficult to judge through a standard or difficult to directly measure through a sensor or input a specific environmental factor value so that the mining truck can complete corresponding driving actions besides the influence of the input environmental factors related to water mist and water vapor on the detection result of the detection device. For example, in the event of a local collapse of the mine, a value corresponding to a severe environmental factor may be entered for equipment and personnel safety considerations, and the processor may be directed to stop the mining truck.
For an environmental measurement device that cannot or is inconvenient to install on the mining truck, in order to enable communication between the environmental measurement device and a processor, the environmental detection device includes: the remote server is respectively in communication connection with the processor and the environment detection device and can transmit the environment factors acquired by the environment detection device to the processor. The remote server can be constructed on a local area network of a mining area according to the transmission requirements of different environmental factors so as to improve accuracy, and can also be constructed on a shared network so as to reduce cost.
The remote server can realize the requirement of bidirectional communication connection between the processor and the environment detection device, namely, the remote server can not only transmit the environment factors detected by the environment detection device to the processor for evaluating the influence of the environment factors on the detection device, but also transmit the control instructions of the processor on the running controller and the detection device to the environment detection device or the custom device so as to detect the specific environment factors which have the greatest influence on the mining truck detection device in the current running state. For example, after the processor transmits the positioning information of the mining truck to the remote server, the remote server may determine the sprinkler around the mining truck therefrom and transmit the positioning information of the sprinkler around the mining truck to the processor instead of transmitting the positioning information of all the sprinklers, so as to help an operator or the processor make a decision of a corresponding driving action for the mining truck at the current position more quickly.
As an optimization of any of the technical solutions or any of the optimized technical solutions provided in the foregoing or in the following, the processor includes: a first calculation module for normalizing the at least one environmental factor acquired by the environmental detection device; the second calculation module is used for carrying out weighting processing on the confidence degree of the obstacle detection result obtained by the detection device based on the normalization result of the at least one environmental factor obtained by the first calculation module; and the third calculation module is used for judging the influence of the obstacle on the running of the vehicle according to the weighted processing result obtained by the second calculation module, and enabling the running controller to control the mining truck to make corresponding running actions based on the influence of the obstacle on the running of the vehicle.
The at least one environmental factor includes dust concentration, rainfall intensity, position of the sprinkler, snowfall, hail intensity and other environmental factors. And the normalization of the first calculation module to the environmental factors means that the dimensionality parameters are converted into dimensionless parameters with 1 as a unit so as to facilitate operation.
Specifically, taking dust concentration as an example, the dust concentration detected by the dust sensor is differentiated into a range of 0% -100% within a certain range, and is corresponding to the dust concentration by adopting a linear, quadratic curve or other types of proportional relation functions. The 0% of the normalized amount of the prescribed environment represents the least influence on the detecting device, while the 100% represents the most influence on the detecting device. The normalized dust concentration is recorded as dust environment normalization quantity H1, and the at least one environmental factor is treated similarly correspondingly, so that various environment normalization quantities H1, H2 and H3 … … are obtained
In one embodiment of the invention, the dust environment normalization amount H1 corresponds to 0mg/m at 0% 3 At 10% to 0.1mg/m 3 Corresponding to 500mg/m at 100% 3 . When the dust concentration is more than 500mg/m 3 When the dust is disturbed very seriously to the detecting device, the confidence level of the detecting result obtained by the detecting device is lower, so that the H1 continues to take 100 percent.
Correspondingly, for the rainfall environment normalization quantity H2, taking the rainfall intensity range of 0.1mm/10 min-1 mm/10min to correspond to the rainfall environment normalization quantity H2 with the value of 10% -100%, and continuing to use 100% for the rainfall intensity H2 with the value of more than 1mm/10 min; for the environment normalization quantity H3 of the distance between the sprinkler and the mining truck, the distance between the sprinkler and the mining truck is taken to be 0 m-10 m, which corresponds to the value of H3 by 100% -0%, and for the condition that the distance between the sprinkler is greater than 10m, H3 is continuously represented by 0%; for the haze environment normalization quantity H4, the visibility is 5 km-20 km which is 50-0% of the H4 value, the visibility is 1 km-5 km which is 100-50% of the H4 value, and the visibility is less than 1km which is 100% of the H4 value.
Other environmental factors which are difficult to judge by the standard or difficult to measure by the sensor, such as snow, hail and the like, are corresponding to specific values under specific conditions, and whether to use a direct proportion function is selected according to the situation. For example, for the normalized amount H5 of the snowfall environment, taking the snow in the weather forecast to correspond to the H5 value of 100%, and taking the corresponding snowfall amount of the small snow in the weather forecast to correspond to the H5 value of 0% -100%; for the normalized amount H6 of hail environment, hail is taken to be 100% corresponding to H6 and no hail is taken to be 0% corresponding to H6.
Similar to the hail environment normalization amount H6, other severe conditions which are difficult to characterize can also be represented by the environment normalization amount H with a value of 100% to seriously interfere with the mining truck detection device or the mining truck running, and the mining truck detection device and the mining truck running are not adversely affected by the environment normalization amount H with a value of 0% to represent the hail environment normalization amount H. For example, when whether or not slump or landslide occurs is characterized by H7, the occurrence of slump or landslide corresponds to the value of H7 being 100%, and the occurrence of slump or landslide corresponds to the value of H7 being 0%.
And the second calculation module carries out weighting processing on the confidence degree of the obstacle detection result based on the normalization result of the environmental factors. The confidence degree refers to whether the detection result of the corresponding detection device is reliable, and under the condition of severe environment, the detection effect of part of specific detection devices is greatly reduced, and the conditions of false detection, false detection and the like of obstacles are easy to occur, so that a processor guides a running controller to brake or slow down a vehicle, the traffic efficiency of a mining road is seriously reduced, and even traffic jam or serious traffic accidents are caused. It is therefore necessary for a particular type of detection device to calculate the corresponding confidence level in dependence on environmental factors.
For the embodiment of the present invention, the environmental factors detected by the environmental detection device are more, the normalized environmental normalization amounts (H1-H7) are also more, when the confidence level of the obstacle detection result is weighted by the environmental normalization amounts, one of the environmental normalization amounts with the greatest influence may be selected, for example, h=max (H1, H2 …), or all the environmental normalization amounts may be taken and H may be calculated with different weights, for example, h=c1h1+c2h2+ …, where c1 and c2 … refer to the weights of the environmental normalization amounts of different types, respectively.
In the embodiment of the invention, when the environmental factors are severe, the value of the environmental normalization quantity H is 100%, and when the environmental factors are better, the value of the environmental normalization quantity H is 0%. Therefore, in combination with the corresponding value, for a specific detection device, for example, a detection device which detects by an optical principle, the influence of the environmental factor on the detection device can be expressed more accurately by inversely proportional relation between the confidence level of the detection result of the detection device and the environment normalization amount. For example, the confidence parameter K of the optical detection device may be (1-H), or the confidence parameter K of the detection device may be a function of other inverse proportional relation to the environment normalization amount H. For the detection devices with different influence degrees by the environmental factors, for example, the detection device which also works on the optical principle is insensitive to the environmental factors such as dust, and the value of the confidence parameter K can be correspondingly higher than that of the detection device which is sensitive to the environmental factors such as dust.
By weighting the confidence levels of the different detection devices with the confidence parameter K, the performance of the detection devices under the corresponding environmental conditions can be evaluated more accurately. Based on the method, the third calculation module judges the influence of the obstacle on the running of the vehicle more accurately, and the influence of the false detection of the detection device on the running of the vehicle caused by environmental factors can be avoided better.
And the third calculation module can also enable the driving controller to control the mining truck to make corresponding driving actions according to different influences of the obstacles on the driving of the vehicle. For example, when the detected result after the confidence degree weighting process judges that the vehicle has an obstacle in the advancing direction, the vehicle can be correspondingly stopped from traveling, and the front is free from the obstacle, and when the detecting device erroneously detects the obstacle due to the environmental factor, the vehicle is continuously advanced through the confidence degree weighting process on the detected result, and abnormal parking caused by the erroneous detection and the erroneous detection of the detecting device is removed.
In addition, the vehicle has certain traffic capacity for the obstacle when moving at a low speed, and the obstacle is required to be not passed or crushed as much as possible when moving at a high speed, so that the damage to the components such as the vehicle tire, the chassis and the like is avoided. The third calculation module can also adopt a low-speed running strategy according to the corresponding vehicle speed when the environmental factors are severe and the influence of the obstacle on the running of the vehicle is small. Therefore, on the premise of ensuring the running safety of the vehicle, the traffic efficiency of the mining area road is maximized.
As shown in fig. 2 to 6, the detecting device includes: a short range lidar for detecting a road surface obstacle, having a first detection angle directed toward a short range road surface of the mining truck; and a remote lidar for detecting a vehicle obstacle, having a second detection angle directed toward a remote surface of the mining truck.
The short-range laser radar and the long-range laser radar have higher detection precision and respectively have different inclination angles so as to respectively detect different obstacles on a short-range road surface and a long-range road surface. For mining area scenes, the obstacles on the road surface are mainly ores, ore sand and other running vehicles which are dropped by the transport vehicles, so that ores and slag on the forward or backward road of the vehicles can be accurately detected by arranging a short-range laser radar; by arranging the long-distance laser radar, other vehicles running on the road are correspondingly detected. The first detection angle and the second detection angle may be correspondingly adjusted according to a specific vehicle type and a specific mine road gradient. The detection device and the vehicle can be further arranged in a rotatable connection relationship, so that the first detection angle of the short-range laser radar and the second detection angle of the long-range laser radar can be flexibly adjusted to adapt to complex road conditions.
As an optimization of any of the technical solutions or any of the optimized technical solutions provided in the foregoing or in the following, the processor includes: a fourth calculation module for meshing the travel path of the mining truck into a detection grid; a fifth calculation module, configured to perform confidence degree weighting processing on detection results of the short-range lidar and/or the long-range lidar in each detection grid according to the at least one environmental factor; and the sixth calculation module is used for comparing the processing result obtained by the fifth calculation module with a preset result and enabling the driving controller to control the mining truck to make corresponding driving actions according to the comparison result.
The fourth calculation module is configured to grid a travel path of the vehicle into a detection grid, where the detection grid does not correspond to a near range or a far range of the vehicle, but is centered on the vehicle, and grid a detectable travel path into a detection grid. The detection grids may have the same area, for example square detection grids with a side length of 0.1m, or may have different areas according to the detection accuracy of the driving paths at different distances, for example, a detection grid with a smaller area is taken on a path closer to the vehicle (FB-FC, RB-RC area shown in fig. 6), and a detection grid with a larger area is taken on a path farther from the vehicle (FC-FD, RC-RD area shown in fig. 6).
And because the detection grids are densely paved on the running path of the vehicle, the same detection grid can be detected by the short-range laser radar and the long-range laser radar at the same time, and can also be detected by only one radar. Thus, the fifth calculation module is used to perform confidence weighting on the detection results of the short range lidar and/or the long range lidar in each detection grid to cope with the situation that different detection grids may be detected by different detection devices.
Specifically, for a specific detection grid, the condition parameters N of the obstacle in the detection grid are defined, and n=k1α+k2β is taken, where K1 refers to the confidence parameter of the short-range laser radar, K2 refers to the confidence parameter of the long-range laser radar, as mentioned above, K1 and K2 are parameters in inverse relation to the environment normalization amount, and α and β refer to the condition that the short-range laser radar and the long-range laser radar detect the obstacle, respectively, when the short-range laser radar or the long-range laser radar finds that the obstacle is in the detection grid, the corresponding α or β is taken as 1, and conversely is taken as 0.
And comparing the processing result with a preset result by the sixth calculation module through the detection result weighted by the confidence degree so as to enable the vehicle to make corresponding driving actions. The preset result can be set through experiments according to different vehicle types and different mine environments. As shown in FIG. 6, in one embodiment of the present invention, the preset result for the near zone of FB-FC in the figure is set to 0.6, and the preset result for the far zone of FC-FD in the figure is set to 1.4. And further defining that when the obstacle condition parameter N of the specific detection grid is lower than the preset result of the area where the detection grid is positioned, no obstacle is considered in the grid; and when the obstacle condition parameter N of the specific detection grid is higher than the preset result of the area where the detection grid is positioned, the grid is considered to have no obstacle.
In order to better cope with mine scenes with more dust or water mist and water vapor enrichment, the detection device comprises: the millimeter wave radar is used for detecting vehicle obstacles and has a third detection angle pointing to the remote pavement of the mining truck. The millimeter wave radar has strong adaptability to the environment, is not easy to be interfered by severe environmental conditions, and is matched with the long-distance laser radar to ensure that the detection of the vehicle obstacle is always stable and reliable under different environmental conditions.
For embodiments that use both short range lidar, long range lidar, and millimeter wave radar as detection devices, the processor includes: a seventh calculation module for meshing the travel path of the mining truck into a detection grid; an eighth calculation module, configured to perform confidence degree weighting processing on the detection result of the short-range laser radar and/or the long-range laser radar in each detection grid according to the at least one environmental factor, and not perform the confidence degree weighting processing on the detection result of the millimeter wave radar in each detection grid; and the ninth calculation module is used for comparing the processing result obtained by the eighth calculation module with a preset result and enabling the driving controller to control the mining truck to make corresponding driving actions according to the comparison result.
Because the detection result of the millimeter wave radar is less influenced by dust, water mist or water vapor, the detection result of the millimeter wave radar is not weighted, the detection result of the millimeter wave radar on the vehicle obstacle can be reserved to the greatest extent, and the running of the mining truck is safer and more reliable. Of course, those skilled in the art should understand that, because other environmental factors may weight the detection result of the millimeter wave radar, or the effect of dust, water mist or water vapor on the millimeter wave radar may be added to the calculation of the confidence level thereof through the weighting process as a result of experimental determination.
Specifically, under the condition that the environmental condition is severe, the detection result of the short-range laser radar on the obstacle is ignored, and only the information of the obstacle of the vehicle is focused, so that parking caused by false detection and false detection of the sensor is avoided. For example, when the environment normalization amount H is greater than 40%, the confidence parameter K1 of the short-range laser radar is less than 0.6 by calculating the relation of k= (1-H), and correspondingly, the confidence parameter K2 of the long-range laser radar is also less than 0.6, and the detection result of the millimeter wave radar is not weighted, that is, the confidence parameter K3 of the millimeter wave radar is 1.
At this time, the obstacle condition parameter N is calculated by n=k1α+k2β+k3γ, where α, β, and γ refer to the condition of obstacle detection by the short-range lidar, the long-range lidar, and the millimeter wave radar, respectively, and when the short-range lidar, the long-range lidar, and the millimeter wave radar find that there is an obstacle in the detection grid, the corresponding α, β, and γ take 1, whereas take 0.
For the detection grid of the short-range area, the long-range laser radar and the millimeter wave radar do not detect the detection grid, so that the values of beta and gamma are 0, the obstacle condition parameter N=K1α of the grid is smaller than 0.6, and the range of the obstacle condition parameter N of the grid is [0,0.6 ], which is smaller than the preset result 0.6 of the FB-FC short-range area, and no obstacle is always considered, so that the detection result of the short-range laser radar is ignored.
For the detection grid of the far-distance area, the near-distance laser radar does not detect the detection grid at the position, and even if the value of K2 is smaller than 0.6 and the value range of N is [0,1.6 ] because the environment normalization quantity H is larger than 0.4, the detection grid of the far-distance area still contains two conditions of no obstacle and existence of obstacle, so that the detection results of the far-distance laser radar and the millimeter wave radar on the vehicle obstacle are reserved.
At this time, since the detection result of the short-range lidar is ignored due to the weighted calculation, the ninth calculation module should control the travel controller to keep the vehicle traveling at a low speed to enhance the passing ability to the floor obstacle such as falling stone, ore, slag, etc., and to stop the vehicle in time to avoid the occurrence of an accident such as a collision when the obstacle condition parameter is greater than the preset result.
For the situation that the environmental condition is better, for example, when the environmental normalization amount H is smaller than 40%, corresponding to the calculation process, for the detection grid of the near area, the obstacle condition parameter N takes the value of [0,1], including two cases that are larger than and smaller than the preset result 0.6; for the detection grid of the remote area, the value of the obstacle condition parameter N is [0,2], and the detection grid also comprises two conditions of more than and less than the preset result 1.4. Therefore, under the condition of better environmental conditions, the detection results of all the detection devices are reserved, and the ninth calculation module can compare the obstacle condition parameters N of different detection grids with preset results so as to enable the vehicle to take different driving actions of stopping, decelerating and passing or keeping the speed to continue driving.
For the detection grids which can be detected by the long-range laser radar, the short-range laser radar and the millimeter wave radar at the same time, for example, the detection grid at the junction of the FB-FC region and the FC-FD region, the obstacle condition parameter N is in the range of [0,2.2] when the environmental condition is not good (namely, the environmental normalization parameter H is more than 0.4), and both the preset result of 0.6 for the short-range region and the preset result of 1.4 for the long-range region are more than or less than the preset result. At this time, the ninth calculation unit may continue to make the vehicle take a corresponding driving action on the vehicle according to the relationship between the corresponding obstacle condition parameter N and the preset result.
It should be noted that for a tele-lidar and a millimeter-wave radar, which are detected by a detection grid in a tele-area, both have different confidence parameters K2, K3, no matter what environmental conditions. Taking the foregoing calculation method as an example, the confidence parameter k2= (1-H) of the long-range laser radar, and the confidence parameter k3=1 of the millimeter wave radar, the sensitivity of different types of detection devices to environmental factors is being reflected. Therefore, different confidence parameter calculation methods of the detection devices can be set for different types of detection devices and different types of environmental factors, so that the detection results are less influenced by the environmental factors.
As shown in fig. 2-6, the near-range lidar comprises a multi-line lidar, is arranged at the head and tail positions of the mining truck, and has a first detection angle pointing to the near-range road surface of the mining truck, wherein the first detection angle can meet the condition that when the near-range lidar detects a road surface obstacle, the distance between the mining truck and the road surface obstacle is larger than the minimum braking distance of the mining truck.
The remote laser radar comprises a surface laser radar and/or a multi-line laser radar, at least two remote laser radars are arranged at the head position of the mining truck, the remote laser radars are provided with a second detection angle pointing to the remote pavement of the mining truck, and the second detection angle can meet the condition that when the remote laser radars detect a vehicle obstacle, the distance between the mining truck and the vehicle obstacle is larger than the minimum braking distance of the mining truck.
At least three millimeter wave radars set up in mining truck headstock position, at least one millimeter wave radar set up in mining truck tailstock position, millimeter wave radar has the third detection angle that points to mining truck long-distance road surface, the third detection angle can satisfy when detecting the vehicle barrier, the interval of mining truck and vehicle barrier is greater than mining truck's minimum brake distance.
For mine scenarios where the mining truck is located, the first, second and third detection angles are adjusted for different vehicle types, and wherein the second and third detection angles are required to ensure that at least a minimum volume of driving vehicles, such as pick-up trucks, on the mine road are detected.
The short-range laser radar and the long-range laser radar can be single-line, 16-line, 32-line or 64-line laser radars, and the detection precision is increased along with the increase of the number of lines, but the price is increased along with the increase of the number of lines, so that the short-range laser radars and the long-range laser radars can be reasonably arranged according to different detection requirements. For example, the head position of the mining truck has the strongest detection requirement on the vehicle obstacle, so that the multi-line laser radar with higher precision can be correspondingly selected, the detection precision requirement on the tail position of the mining truck is correspondingly lower, and the single-line laser radar with lower cost or the multi-line laser radar with smaller line number can be selected.
And as shown in fig. 6, for the embodiment of the present invention, since the short-range lidar is applied to detect road obstacle conditions of a short-range region, the short-range lidar should have a high vertical height to more accurately observe the short-range region. And because the long-distance laser radar and the millimeter wave radar are applied to detecting the vehicle obstacle condition in a long-distance area, the long-distance laser radar needs to be arranged at a lower vertical height as far as possible so as to avoid neglecting the vehicle obstacle with a shorter vehicle body. Of course, it should be understood by those skilled in the art that the setting position is not absolute and should be set accordingly depending on the kind of obstacle and the structure of the vehicle.
To cope with more severe environmental conditions, the processor comprises: and a tenth calculation module for decelerating and stopping the mining truck when the confidence level of the detection result estimated according to the at least one environmental factor is lower than a confidence level set value.
The degree of deceleration of the mining truck can be calculated directly from the environment normalization quantity, such as V Deceleration of =v× (1-H), where V Deceleration of Refers to the magnitude of the speed that the mining truck is required to reduce, while V refers to the current travel speed of the vehicle. At this time, as environmental factors become worse, such as the intensity of rainfall increases gradually, the concentration of water mist and water vapor on mine roads is large, the short-range or long-range laser radar can no longer accurately measure the obstacle, and the vehicle can slow down gradually and run under the guidance of the measurement result of the millimeter wave radar at a lower speed. When the environmental conditions continue to deteriorate, the confidence level of the detection result of the detection device is insufficient to enable the vehicle to reasonably cope with the obstacles on the path, and the mining truck is not suitable for continuous running any more, for example, when the area where the mining area is located encounters the condition of medium to large snow, the environment normalization amount H takes a value of 1 along with the snowfall environment normalization amount H5, and at the moment, the mining truck stops under the control of the tenth calculation module, so that dangerous running is avoided.
The tenth calculation module can be applied to unmanned vehicles and also can be applied to manned vehicles, so that the operation efficiency is effectively improved, and the safety of truck operation is improved.
The invention also provides a mining truck obstacle detection method, which comprises the following steps: detecting an obstacle on a driving route of the mining truck; collecting at least one environmental factor that affects the detection of the obstacle; and evaluating the confidence degree of the obstacle detection result according to the at least one environmental factor, determining the obstacle condition according to the confidence degree of the detection result, and controlling the mining truck to make corresponding driving actions based on the obstacle condition.
Further, the detection method comprises the following steps: and decelerating and stopping the mining truck when the confidence level of the detection result estimated according to the at least one environmental factor is lower than a confidence level set value.
Under the control of the above detection method, the mining truck will travel by:
under the condition of better environmental conditions (smaller environment normalization amount H), the mining truck tends to move at a high speed, and at the moment, in order to avoid tire burst risks caused by high-speed rolling of obstacles such as falling rocks, all three radars are started to detect the obstacles on the road surface, and reasonable angles are set to detect other vehicles so as to avoid accidents such as rear-end collisions;
Under the condition of poor environmental regulation (larger environmental normalization amount H), the mining truck tends to move at a low speed, due to the fact that the mining truck has quite trafficability to obstacles at the low speed, the weight of the short-range laser radar on obstacle detection is reduced, parking caused by false detection is avoided, and V is calculated based on the environmental normalization amount H Deceleration of To achieve deceleration of the mining truck;
and under any environmental condition, once the condition parameter N of the obstacle in a certain detection grating is larger than a preset result, the mining truck is stopped.
The invention also discloses a mining truck, which comprises any mining truck obstacle detection system.
Any of the above-described embodiments of the present invention disclosed herein, unless otherwise stated, if they disclose a numerical range, then the disclosed numerical range is the preferred numerical range, as will be appreciated by those of skill in the art: the preferred numerical ranges are merely those of the many possible numerical values where technical effects are more pronounced or representative. Since the numerical values are more and cannot be exhausted, only a part of the numerical values are disclosed to illustrate the technical scheme of the invention, and the numerical values listed above should not limit the protection scope of the invention.
If the terms "first," "second," etc. are used herein to define a part, those skilled in the art will recognize that: the use of "first" and "second" is used merely to facilitate distinguishing between components and not otherwise stated, and does not have a special meaning.
Meanwhile, if the above invention discloses or relates to parts or structural members fixedly connected with each other, the fixed connection may be understood as follows unless otherwise stated: detachably fixed connection (e.g. using bolts or screws) can also be understood as: the non-detachable fixed connection (e.g. riveting, welding), of course, the mutual fixed connection may also be replaced by an integral structure (e.g. integrally formed using a casting process) (except for obviously being unable to use an integral forming process).
In the description of the present invention, if the terms "center", "longitudinal", "lateral", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are used, the above terms refer to the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, only for convenience of describing the present invention and simplifying the description, and do not refer to or suggest that the apparatus, mechanism, component or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the scope of protection of the present invention.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical scheme of the present invention and are not limiting; while the invention has been described in detail with reference to the preferred embodiments, those skilled in the art will appreciate that: modifications may be made to the specific embodiments of the present invention or equivalents may be substituted for part of the technical features thereof; without departing from the spirit of the invention, it is intended to cover the scope of the invention as claimed.
Claims (14)
1. A mining truck obstacle detection system, comprising:
the driving controller is used for controlling the driving of the mining truck;
the detection device is used for detecting obstacles on the driving route of the mining truck;
the environment detection device is used for collecting at least one environment factor which can influence the obstacle detection result; and
the processor is respectively connected with the running controller, the detection device and the environment detection device in a signal way, can evaluate the confidence level of the obstacle detection result according to the at least one environment factor acquired by the environment detection device, determines the obstacle condition according to the confidence level of the detection result, and enables the running controller to control the mining truck to make corresponding running action based on the obstacle condition;
Wherein, the detection device includes: a short range lidar for detecting a road surface obstacle, having a first detection angle directed toward a short range road surface of the mining truck; and a remote lidar for detecting a vehicle obstacle having a second detection angle directed toward a remote surface of the mining truck;
the processor includes:
a fourth calculation module for meshing the travel path of the mining truck into a detection grid;
a fifth calculation module, configured to perform confidence degree weighting processing on detection results of the short-range lidar and/or the long-range lidar in each detection grid according to the at least one environmental factor; and
and the sixth calculation module is used for comparing the processing result obtained by the fifth calculation module with a preset result and enabling the driving controller to control the mining truck to perform corresponding driving actions according to the comparison result.
2. The detection system of claim 1, wherein the environment detection device comprises:
the dust sensor is arranged on the mining truck and can detect the dust concentration of the surrounding environment of the mining truck.
3. The detection system of claim 1, wherein the environment detection device comprises:
The rainfall sensor is in communication connection with the processor and can measure rainfall intensity of the area where the mining truck is located;
the sprinkler positioning device is in communication connection with the processor and can position the sprinkler; and/or
The self-defining device is in communication connection with the processor and can manually input environmental factors influencing the mining truck.
4. A detection system according to claim 3, wherein the environment detection means comprises:
the remote server is respectively in communication connection with the processor and the environment detection device and can transmit the environment factors acquired by the environment detection device to the processor.
5. The detection system of claim 1, wherein the processor comprises:
a first calculation module for normalizing the at least one environmental factor acquired by the environmental detection device;
the second calculation module is used for carrying out weighting processing on the confidence degree of the obstacle detection result obtained by the detection device based on the normalization result of the at least one environmental factor obtained by the first calculation module; and
and the third calculation module is used for judging the influence of the obstacle on the running of the vehicle according to the weighted processing result obtained by the second calculation module, and enabling the running controller to control the mining truck to make corresponding running actions based on the influence of the obstacle on the running of the vehicle.
6. The detection system of claim 1, wherein the detection device comprises:
the millimeter wave radar is used for detecting vehicle obstacles and has a third detection angle pointing to the remote pavement of the mining truck.
7. The detection system of claim 6, wherein the processor comprises:
a seventh calculation module for meshing the travel path of the mining truck into a detection grid;
an eighth calculation module, configured to perform confidence degree weighting processing on the detection result of the short-range laser radar and/or the long-range laser radar in each detection grid according to the at least one environmental factor, and not perform the confidence degree weighting processing on the detection result of the millimeter wave radar in each detection grid; and
and the ninth calculation module is used for comparing the processing result obtained by the eighth calculation module with a preset result and enabling the driving controller to control the mining truck to perform corresponding driving actions according to the comparison result.
8. The detection system of claim 1, wherein the near-range lidar comprises a multi-line lidar disposed at the mining truck head and tail locations having a first detection angle directed toward a near-range road of the mining truck, the first detection angle being capable of satisfying a distance between the mining truck and the road obstacle being greater than a minimum braking distance of the mining truck when the near-range lidar detects the road obstacle.
9. The detection system of claim 1, wherein the telelidar comprises a surface lidar and/or a multi-line lidar, at least two of the telelidars are disposed at the mining truck head location, the telelidar has a second detection angle directed toward the mining truck teleroad, the second detection angle being capable of satisfying that a distance between the mining truck and a vehicle obstacle is greater than a minimum braking distance of the mining truck when the telelidar detects the vehicle obstacle.
10. The detection system of claim 7, wherein at least three of the millimeter wave radars are disposed at the mining truck head position, at least one of the millimeter wave radars is disposed at the mining truck tail position, the millimeter wave radars have a third detection angle directed toward the mining truck remote road surface, the third detection angle being capable of satisfying that the millimeter wave radars have a spacing from the vehicle obstacle greater than a minimum braking distance of the mining truck when the vehicle obstacle is detected.
11. The detection system of claim 1, wherein the processor comprises:
And a tenth calculation module for decelerating and stopping the mining truck when the confidence level of the detection result estimated according to the at least one environmental factor is lower than a confidence level set value.
12. A mining truck obstacle detection method based on the detection system of any one of claims 1 to 11, comprising:
detecting an obstacle on a driving route of the mining truck;
collecting at least one environmental factor that affects the detection of the obstacle; and
and evaluating the confidence degree of the obstacle detection result according to the at least one environmental factor, determining the obstacle condition according to the confidence degree of the detection result, and controlling the mining truck to make corresponding driving actions based on the obstacle condition.
13. The method of probing as recited in claim 12, comprising:
and decelerating and stopping the mining truck when the confidence level of the detection result estimated according to the at least one environmental factor is lower than a confidence level set value.
14. A mining truck comprising the mining truck obstacle detection system of any one of claims 1-11.
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| CN111216628B (en) * | 2020-01-13 | 2023-04-25 | 江苏恒旺数字科技有限责任公司 | Road condition safety early warning device and method for dump truck for strip mine |
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| CN113281783A (en) * | 2021-05-13 | 2021-08-20 | 江苏徐工工程机械研究院有限公司 | Mining truck |
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