CN114578822A - Intelligent garbage recycling method based on UWB indoor positioning - Google Patents
Intelligent garbage recycling method based on UWB indoor positioning Download PDFInfo
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Abstract
The invention discloses an intelligent garbage recycling method based on UWB indoor positioning and an unmanned logistics vehicle, which comprise a plurality of positioning base stations with known coordinates arranged indoors, a positioning label is arranged on a garbage recycling trolley, the garbage recycling trolley moves to a specified position after receiving a garbage recycling signal, the positioning label emits UWB pulses according to a certain frequency in the moving process, the UWB pulses continuously interact with the positioning base stations with known positions, so that the real-time position of the garbage recycling trolley is calculated, and then the real-time position is utilized to carry out path planning until the garbage recycling trolley reaches the specified position, thereby realizing garbage recycling. The invention can recycle the garbage under the condition of no contact, reduces the contact times of personnel, avoids the spread of epidemic situations, and has the advantages of anti-interference capability and high positioning precision.
Description
Technical Field
The invention relates to the technical field of garbage recovery, in particular to an intelligent garbage recovery method based on UWB indoor positioning and an unmanned logistics vehicle.
Background
With the rapid development of artificial intelligence and the continuous improvement of vehicle technology, the research and application of intelligent vehicles become more and more important aspects of production and life. As a new product of modern society, various functions developed on intelligent vehicles, such as automatic parking, anti-collision early warning, auxiliary driving systems and the like, can ensure the driving safety and further improve the intelligent level. Simultaneously along with the development of epidemic situation, for the propagation of avoiding the epidemic situation, reduce the number of times that personnel contacted, garbage collection etc. need do little thing again every day, can give the intelligent vehicle to realize. The disposal bag is born also more and more when social development, and single dormitory just can produce about 2L rubbish every day, and rubbish is stored up for a long time and is influenced all ring edge borders, and the rubbish that rottes easily very much more needs to clear up every day, and the existence of intelligent car and provide a fine mode can let intelligent car help people to lose rubbish when inconvenient to reach epidemic situation control, reduce the manpower, beautify the effect of environment. In order to realize the intelligent vehicle to help people to lose garbage, indoor positioning and related path regulation are required. At present, indoor positioning at home and abroad generally comprises the following methods such as GPS, ultrasonic wave, WIFI, UWB, RFID and the like. The GPS is easily influenced by factors such as weather, earth rotation, satellite operation, cloud layer flow and the like, the indoor signal intensity is weak, and the precision cannot meet the requirement; WIFI is a wireless local area network formed by wireless access points, but the positioning error is large, and the RFID which is easy to interfere can realize centimeter-level positioning within a few milliseconds, but the working distance is short and the wireless local area network has no communication capability. Ultra Wide-Band (UWB) technology is a pulsed radio, and has been used in the military field for a long time. The U.S. Federal Communications Commission (FCC) approved UWB for civilian use beginning in 2002 and established strict usage regulations. The ultra-wideband system has the advantages of ultra-low power consumption, higher multipath resolution capability, easier integration, working time period, multipath effect resistance, high positioning performance and the like by means of the bandwidth far exceeding that of a traditional communication system, so that the ultra-wideband system is selected as the communication system of the intelligent garbage recycling trolley, and the high-precision positioning requirement of the mobile trolley can be met. Therefore, how to apply the Ultra Wide-Band (UWB) technology to the garbage recycling trolley becomes a technical problem that the applicant needs to solve urgently.
Disclosure of Invention
The invention aims to provide an intelligent garbage recycling method based on UWB indoor positioning. The invention can recycle the garbage under the condition of no contact, reduces the contact times of personnel, avoids the spread of epidemic situation, and has the advantages of anti-interference capability and high positioning precision.
The technical scheme of the invention is as follows: an intelligent garbage recycling method based on UWB indoor positioning comprises the steps that a plurality of positioning base stations with known coordinates are arranged indoors, positioning labels are arranged on a garbage recycling trolley, the garbage recycling trolley moves to a specified position after receiving a garbage recycling signal, UWB pulses are emitted by the positioning labels in a moving process according to certain frequency, the UWB pulses continuously interact with the positioning base stations with the known positions, so that the real-time position of the garbage recycling trolley is calculated, and the real-time position is reused for path planning until the garbage recycling trolley reaches the specified position, so that garbage recycling is achieved.
According to the intelligent garbage recycling method based on UWB indoor positioning, the real-time position of the garbage recycling trolley is calculated by arranging two positioning base stations with known coordinates indoors, the positioning label transmits UWB pulses to the two positioning base stations so as to obtain the linear distances d1 and d2 between the positioning label and the base stations as well as between the positioning label and the positioning base stations, and the Pythagorean theorem is used for:
h2＝x12+d12
h2＝x22+d22；
in the formula: h is the height of the positioning base station, and the two positioning base stations are equal in height;
further calculating the horizontal linear distance x1 between the positioning label and the first positioning base station and the horizontal linear distance x2 between the positioning label and the second positioning base station;
and then calculating a cosine value of an included angle between the x1 and the connecting line of the positioning base station and a cosine value of an included angle between the x2 and the connecting line of the positioning base station by the trilateral cosine theorem:
cosθ＝(x12+l2-x22)/(2×x1×l)；
and further calculating the coordinate value of the position of the positioning label in the coordinate system:
n＝x1×cosθ；
the coordinate value (m, n) is the real-time position of the garbage recycling trolley.
In the intelligent garbage recycling method based on UWB indoor positioning, the distance between the positioning tag and the positioning base station is obtained by using a bilateral two-way ranging method, and the process is as follows:
the positioning tag emits UWB pulse and records the sending time T1Recording the arrival time T after the positioning base station receives the UWB pulse2And is at T3Constantly transmitting a response packet, and recording the receiving time T after the positioning tag receives the response packet4And transmitting an end packet at time T5 to locate the base stationAfter receiving the end packet, recording the time T6Thus, four time differences are obtained:
Tround1＝T4-T1；
Tround2＝T6-T3；
Treply1＝T3-T2；
Treply2＝T5-T4；
and further calculating the propagation time of the UWB pulse in the space as follows:
the distance is calculated from the travel time and the speed of light c:
d＝Tprop×c。
according to the intelligent garbage recycling method based on UWB indoor positioning, during path planning, a secondary A-star algorithm and a bidirectional artificial potential field method are combined together, meanwhile, the connection position of the secondary A-star algorithm and the bidirectional artificial potential field method during switching is optimized, and finally, a path planned together by the secondary A-star algorithm and the bidirectional artificial potential field method is optimized by a Bessel curve, so that the path is in accordance with the running track of a garbage recycling trolley.
According to the secondary A-star algorithm, after the A-star algorithm obtains the cost values of the paths and the child nodes in the neighborhood of the father node, the child node which is not adjacent to the starting point of the path and has the smallest cost value is selected as a new father node, if no obstacle exists between the starting point of the path and the new father node and the time value is also the smallest, the path is updated to remove the middle father node, and the next search is carried out; otherwise, the path is not updated; and continuously and circularly searching until a path from the starting point to the target point is found.
In the obstacle avoidance path planning method for the automatic driving vehicle, the bidirectional artificial potential field method is improved by adopting a forward and reverse alternating search mechanism, and the bidirectional path planning is completed by the bidirectional artificial potential field method, so that the search efficiency is improved; the path planning by the bidirectional artificial potential field method is to take a starting point A and an end point B of a path as initial positions of forward and reverse searching respectively; when the forward search starts, taking A as a starting point and B as a target point to obtain a next path point A1; and then starting reverse search, taking B as a starting point, and taking the just obtained forward path point A1 as a target point to perform path planning, obtaining the next path point B1 at the moment, completing one-time alternate search, circularly and alternately advancing in such a way until the same path point is searched in the forward and reverse directions, and finally obtaining the path between the meeting point and the two end points, namely the bidirectional artificial potential field path.
According to the obstacle avoidance path planning method for the automatic driving vehicle, the optimization of the joints comprises the steps of dividing a grid into four completely identical small squares by using the grids in the quadratic A-star algorithm, and then determining the starting point and the target point of the quadratic A-star algorithm according to the advancing direction of the bidirectional artificial potential field method path and the positions of the ending point in the four small squares, so that the reasonable planning of the joints of the quadratic A-star algorithm and the bidirectional artificial potential field method is ensured;
after the bidirectional artificial potential field method is terminated due to the fact that the path points repeatedly oscillate, the last three path points including the termination point are selected and deleted from the path points, the exocenter coordinates of a triangle taking the coordinates of the three points as the vertex are obtained through calculation, and the starting point and the target point of the secondary A-star algorithm are selected according to the position of the point, so that the operation result is prevented from being influenced;
for the vehicle which is trapped in the local minimum trap preferentially, the path is not updated any more, and if the vehicle can be separated from the position within a period of time, the path is continuously updated;
when the path planning is carried out by the bidirectional artificial potential field method, parameters are continuously adjusted, and the width of the vehicle is fully considered; after the secondary A-star algorithm, the distance between each obstacle is calculated, if the distance is smaller than the width of the vehicle, virtual obstacles are added between the obstacles, and the secondary A-star algorithm is prevented from carrying out wrong path planning.
According to the obstacle avoidance path planning method for the automatic driving vehicle, the garbage recycling trolley adopts Arduino Mega2560 as a main control board, and the main control board is connected with an ESP8266 networking module, a UWB positioning module, an infrared obstacle avoidance module and a voice broadcasting module.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, a plurality of positioning base stations with known coordinates are arranged indoors, the positioning label is arranged on the garbage recycling trolley, when the garbage recycling trolley receives a garbage recycling signal, the garbage recycling trolley moves to a specified position, the positioning label emits UWB pulses according to a certain frequency in the moving process, the UWB pulses continuously interact with the positioning base stations with known positions, so that the real-time position of the garbage recycling trolley is calculated, and then the real-time position is utilized to perform path planning until the garbage recycling trolley reaches the specified position, so that the garbage recycling is realized. The invention can recycle the garbage under the condition of no contact, reduces the contact times of personnel, avoids the spread of epidemic situations, and has the advantages of anti-interference capability and high positioning precision.
2. The invention can be adapted to use only by fixing the positions of the two base stations and adaptively modifying codes of the garbage recycling trolley, and the scene change is convenient. The garbage recycling trolley is small and exquisite and light in size, can receive signals in real time for a long time, and is long in endurance, low in cost and low in moving error.
3. And finally, optimizing the path planned by the quadratic A-star algorithm and the bidirectional artificial potential field method together by using a Bezier curve so as to enable the path to be in accordance with the driving track of the vehicle. Simulation results show that the method can quickly plan a collision-free and smooth automatic driving vehicle path in a complex environment, and has the advantages of shorter path generation time and higher efficiency. According to the method, because the artificial potential field method has the problem that the artificial potential field method is inevitably trapped in a local minimum trap, and meanwhile, the path also has the oscillation phenomenon, so that a complete path cannot be generated, the method combines a secondary A-star algorithm and a bidirectional artificial potential field method, and finishes path planning by using the secondary A-star algorithm after an automatic driving vehicle is trapped in the local minimum trap. Simulation results show that compared with an artificial potential field method, the method can adapt to various environments and is more beneficial to path planning of vehicles in complex environments.
Drawings
FIG. 1 is a schematic diagram of a real-time position calculation of a garbage collection cart;
FIG. 2 is a schematic diagram of a two-sided two-way ranging method;
FIG. 3 is a flow chart of a quadratic A-star algorithm;
FIG. 4 is a schematic diagram of a search of a bi-directional artificial potential field method;
FIG. 5 is a schematic diagram of a path planning system according to the present invention;
FIG. 6 is a schematic system view of a refuse collection cart;
fig. 7 is a schematic diagram of voice transmission of the voice broadcast module;
fig. 8 is a schematic view of the operation of the garbage recycling cart.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not to be construed as limiting the invention.
Example (b): an intelligent garbage recycling method based on UWB indoor positioning comprises the steps of arranging a plurality of positioning base stations with known coordinates indoors, and arranging positioning labels on a garbage recycling trolley, and is characterized in that: the garbage recycling trolley receives a garbage recycling signal and then moves to a specified position, a mobile process positioning tag emits UWB (ultra wide band) pulses according to a certain frequency, and the UWB pulses continuously interact with a positioning base station in a known position to calculate the real-time position of the garbage recycling trolley, and then the real-time position is utilized to perform path planning until the garbage recycling trolley reaches the specified position, so that garbage recycling is realized.
In this embodiment, as shown in fig. 1, the calculation of the real-time position a0 of the garbage collection cart is performed by arranging two positioning base stations (a1 and a2) with known coordinates indoors, and the positioning tag transmits UWB pulses to the two positioning base stations to obtain the linear distances d1 and d2 between the positioning tag and the base stations and between the positioning tag and the positioning base stations, and according to the pythagorean theorem:
h2＝x12+d12
h2＝x22+d22；
in the formula: h is the height of the positioning base station, and the two positioning base stations are equal in height;
further calculating the horizontal linear distance x1 between the positioning label and the first positioning base station and the horizontal linear distance x2 between the positioning label and the second positioning base station;
and then calculating a cosine value of an included angle between the x1 and the connecting line of the positioning base station and a cosine value of an included angle between the x2 and the connecting line of the positioning base station by the trilateral cosine theorem:
cosθ＝(x12+l2-x22)/(2×x1×l)；
and further calculating the coordinate value of the position of the positioning label in the coordinate system:
n＝x1×cosθ；
the coordinate value (m, n) is the real-time position of the garbage recycling trolley.
The distance between the positioning tag and the positioning base station is obtained by using a bilateral two-way ranging method, as shown in fig. 2, the process is as follows:
the positioning tag emits UWB pulse and records the sending time T1Recording the arrival time T after the positioning base station receives the UWB pulse2And at T3Constantly transmitting a response packet, and recording the receiving time T after the positioning tag receives the response packet4And sending an end packet at T5 time, and recording the time T after the positioning base station receives the end packet6Thus, four time differences are obtained:
Tround1＝T4-T1；
Tround2＝T6-T3；
Treply1＝T3-T2；
Treply2＝T5-T4；
and further calculating the propagation time of the UWB pulse in the space as follows:
the distance is calculated from the travel time and the speed of light c:
d＝Tprop×c。
after the real-time position of the garbage recycling trolley is obtained, the garbage recycling trolley carries out path planning by utilizing the real-time position, during path planning, a secondary A-star algorithm and a bidirectional artificial potential field method are combined and carried out together, meanwhile, the connection position of the secondary A-star algorithm and the bidirectional artificial potential field method during switching is optimized, and finally, a path planned together by the secondary A-star algorithm and the bidirectional artificial potential field method is optimized by utilizing a Bezier curve, so that the path planned together by the secondary A-star algorithm and the bidirectional artificial potential field method is in accordance with the running track of the garbage recycling trolley.
As shown in fig. 3, the quadratic a-star algorithm selects a child node with the smallest cost value, which is not adjacent to the starting point of the path, as a new parent node after the a-star algorithm obtains the cost values of the path and the child nodes in the neighborhood of the parent node, and updates the path to remove the middle parent node and enters the next search if no obstacle exists between the starting point of the path and the new parent node and the value of the time is also the smallest; otherwise, the path is not updated; and continuously and circularly searching until a path from the starting point to the target point is found.
The bidirectional artificial potential field method is characterized in that a forward and reverse alternating search mechanism is adopted to improve the artificial potential field method, bidirectional path planning is completed through the bidirectional artificial potential field method, and search efficiency is improved; as shown in fig. 4, the path planning by the bidirectional artificial potential field method is to use a starting point a and an end point B of a path as initial positions for forward and reverse search respectively; when the forward search starts, taking A as a starting point and B as a target point to obtain a next path point A1; and then starting reverse search, taking B as a starting point, and taking the just obtained forward path point A1 as a target point to perform path planning, obtaining the next path point B1 at the moment, completing one-time alternate search, circularly and alternately advancing in such a way until the same path point is searched in the forward and reverse directions, and finally obtaining the path between the meeting point and the two end points, namely the bidirectional artificial potential field path.
The optimization of the joints comprises the steps of dividing a grid into four completely identical small squares by using the grid in the quadratic A-star algorithm, and then determining the starting point and the target point of the quadratic A-star algorithm according to the advancing direction of the path of the bidirectional artificial potential field method and the position of the ending point in the four small squares so as to ensure that the path planning of the joints of the quadratic A-star algorithm and the bidirectional artificial potential field method is reasonable;
after the bidirectional artificial potential field method is terminated due to the fact that the path points repeatedly oscillate, the last three path points including the termination point are selected and deleted from the path points, the exocenter coordinates of a triangle taking the coordinates of the three points as the vertex are obtained through calculation, and the starting point and the target point of the secondary A-star algorithm are selected according to the position of the point, so that the operation result is prevented from being influenced;
for the vehicle which is trapped in the local minimum trap preferentially, the path is not updated any more, and if the vehicle can be separated from the position within a period of time, the path is continuously updated;
when the path planning is carried out by the bidirectional artificial potential field method, parameters are continuously adjusted, and the width of the vehicle is fully considered; after the secondary A-star algorithm, the distance between each obstacle is calculated, if the distance is smaller than the width of the vehicle, virtual obstacles are added between the obstacles, and the secondary A-star algorithm is prevented from carrying out wrong path planning.
After the improvement, the reasonableness of the path is further considered, and the path is subjected to Bezier curve fitting, as shown in FIG. 5, so that a final complete path is obtained. As can be seen from the figure 5, the optimized path of the invention has no collision with the obstacle, is short and smooth, and can meet the requirement of the driving path of the garbage recycling trolley.
In this embodiment, as shown in fig. 6, the total weight of the cart for the garbage collection cart is about 2 kg, the length is 297mm, the width is 210mm, four hall encoder type motors of the dc speed reduction belts are used to enable the cart to move, one model airplane battery with the voltage of 11.1V and the battery capacity of 2200mAh continues the journey, the average speed of the cart operation is 0.4m/s, and the current cart operation error is not more than 20 cm. Adopt Arduino Mega2560 as the main control board, the main control board is connected with ESP8266 networking module, UWB orientation module, infrared obstacle avoidance module and voice broadcast module. Arduino Mega2560 is an ATmega2560 based master control development board. Arduino Mega2560 is a core circuit board using a USB interface. The circuit has 54 digital input and output circuits, and is suitable for designs requiring a large number of IO interfaces. The processor core is ATmega2560 and has 54 digital input/output ports, 16 analog inputs, 4 UART interfaces, a 16MHz crystal oscillator, a USB port, a power socket, an ICSP header and a reset button. All resources supporting one master control board are on the board. Arduino Mega2560 is also compatible with expansion boards designed for Arduino UNO. The power supply mode in 3 can be automatically selected: an external direct current power supply supplies power through a power socket; the battery is connected with the GND pin and the VIN pin of the power connector; and the USB interface supplies power by direct current.
The networking part in the garbage recycling trolley is an ESP8266 networking module, the ESP8266 is an internet of things WiFi chip, and an internet of things serial port WiFi module can be developed based on the ESP 8266; or the system can be operated as an independent Wi-Fi MCU, and a product with a Wi-Fi connection function is developed through an SDK based on an RTOS. The user can easily realize functions of out-of-box cloud connection, low-power-consumption operation mode, Wi-Fi safety support including WPA3 and the like. And the NodeMCU is an open-source Internet of things platform. The ESP-12 module-based hardware system comprises firmware which can run on an ESP8266 chip and ESP-12 module-based hardware, and communication with a board can be realized only by connecting a TX pin with an RX pin of Arduino and connecting a power supply and GND.
According to the garbage recycling trolley, the infrared obstacle avoidance module is connected with electricity, an object in front is sensed, the LED is lightened, and OUT outputs a continuous low level. Until the object disappears, the LED goes OUT, and the OUT output is at a continuous high level. The infrared ray beam emission presents a three-dimensional (3D) space, so that an object can be sensed in a horizontal conical three-dimensional space right in front. The response time of the induction detection and output is less than 21ms, and the induction distance can be adjusted by the potentiometer. The stable induction distance is 100CM on average, and the performance is excellent and can reach 200 CM. The garbage recycling trolley is provided with a plurality of infrared modules, and normal receiving can be blocked due to mutual crosstalk of infrared light rays. At the moment, the EN control end is utilized, after the detection of the single module is circularly opened, the next module is opened for detection. When the trolley moves forward, the infrared enabling end pin at the front end is given a high level to control and realize front obstacle avoidance, otherwise, the infrared sensor at the rear of the trolley is controlled to work and realize obstacle avoidance.
The voice broadcasting module Q8900-16P of the garbage recycling trolley adopts an SOC scheme, integrates a 16-bit MCU and an ADSP special for audio decoding, and adopts a hard decoding mode, thereby ensuring the stability and tone quality of the system. The biggest advantage of the chip is that voice content in the SPI-FLASH can be flexibly replaced, the trouble that a traditional voice chip needs to be installed by an upper computer to replace voice is eliminated, and the SPI FLASH is directly simulated into a USB FLASH disk which is the same as a copied USB FLASH disk, so that the SPI-FLASH voice replacement chip is very convenient. Meanwhile, a one-line serial port control mode is adopted to facilitate voice transmission and playing. And the one-line serial port communication protocol sda is a data sending port and used for sending a voice address. The low order bits are sent first. Such as the example transmission 89H of fig. 7. Therefore, the operation can be realized only by connecting one line transmission pin VPP, a power supply and the ground. Meanwhile, a loudspeaker is added for amplifying sound in order to make the sound clearer and brighter, and the loudspeaker is connected with an SPK + -pin.
The garbage recycling trolley realizes wireless transmission by utilizing the Wi-Fi module of the ESP8266, obtains the positioning information of the intelligent trolley by the UWB carrier-free communication technology, and then communicates with the mobile phone, so that the trolley can realize real-time feedback of user instructions. As shown in fig. 8, a user can connect to an admin local area network generated by an esp8266WIFI module on a trolley, a room number is input at a mobile phone client, the trolley can be called to a doorway of the room, the trolley can automatically return to a departure place after garbage is released in 10 seconds and clicking is completed, the client can display the number of queued people and the busyness degree of the trolley if a second user also inputs the room number in the period, and the trolley can travel to the room input by the next user after clicking of the previous user is completed. For the voice broadcast module, the car is started before the car is started, the car arrives at a target position, the car arrives at a destination, garbage is taken, and the user is thanked for use! "wait for voice, also increased when meeting the obstacle" meets the obstacle, stopped. "voice broadcast.
In summary, the invention arranges a plurality of positioning base stations with known coordinates indoors, arranges the positioning tag on the garbage recycling trolley, when the garbage recycling trolley receives a garbage recycling signal, the garbage recycling trolley moves to a specified position, the positioning tag emits a UWB pulse according to a certain frequency in the moving process, and continuously interacts with the positioning base stations with the known position through the UWB pulse, so as to calculate the real-time position of the garbage recycling trolley, and then utilizes the real-time position to plan a path until the garbage recycling trolley reaches the specified position, thereby realizing garbage recycling. The invention can recycle the garbage under the condition of no contact, reduces the contact times of personnel, avoids the spread of epidemic situations, and has the advantages of anti-interference capability and high positioning precision.
Claims (8)
1. An intelligent garbage recycling method based on UWB indoor positioning comprises the steps of arranging a plurality of positioning base stations with known coordinates indoors, and arranging positioning labels on a garbage recycling trolley, and is characterized in that: the garbage recycling trolley receives a garbage recycling signal and then moves to a specified position, a mobile process positioning tag emits UWB (ultra wide band) pulses according to a certain frequency, and the UWB pulses continuously interact with a positioning base station in a known position to calculate the real-time position of the garbage recycling trolley, and then the real-time position is utilized to perform path planning until the garbage recycling trolley reaches the specified position, so that garbage recycling is realized.
2. The intelligent garbage collection method based on UWB indoor positioning of claim 1, characterized in that: the real-time position of the garbage recycling trolley is calculated by arranging two positioning base stations with known coordinates indoors, transmitting UWB (ultra wide band) pulses to the two positioning base stations by the positioning labels to further obtain the linear distances d1 and d2 between the positioning labels and the base stations as well as between the positioning labels and the positioning base stations, and performing pythagorean theorem:
h2＝x12+d12
h2＝x22+d22；
in the formula: h is the height of the positioning base station, and the two positioning base stations are equal in height;
further calculating the horizontal linear distance x1 between the positioning label and the first positioning base station and the horizontal linear distance x2 between the positioning label and the second positioning base station;
and then calculating a cosine value of an included angle between the x1 and the connecting line of the positioning base station and a cosine value of an included angle between the x2 and the connecting line of the positioning base station by the trilateral cosine theorem:
cosθ＝(x12+l2-x22)/(2×x1×l)；
and further calculating the coordinate value of the position of the positioning label in the coordinate system:
n＝x1×cosθ；
the coordinate value (m, n) is the real-time position of the garbage recycling trolley.
3. The intelligent garbage collection method based on UWB indoor positioning of claim 2, characterized in that: the distance between the positioning tag and the positioning base station is obtained by adopting a bilateral two-way ranging method, and the process is as follows:
the positioning tag emits UWB pulse and records the sending time T1Recording the arrival time T after the positioning base station receives the UWB pulse2And is at T3Constantly transmitting a response packet, and recording the receiving time T after the positioning tag receives the response packet4And sending an end packet at T5 time, and recording the time T after the positioning base station receives the end packet6Thus, four time differences are obtained:
Tround1＝T4-T1；
Tround2＝T6-T3；
Treply1＝T3-T2；
Treply2＝T5-T4；
and then calculating the propagation time of the UWB pulse in the space as follows:
the distance is calculated from the travel time and the speed of light c:
d＝Tprop×c。
4. the intelligent garbage collection method based on UWB indoor positioning of claim 2, characterized in that: and during path planning, a secondary A-star algorithm and a bidirectional artificial potential field method are combined together, the connection part of the secondary A-star algorithm and the bidirectional artificial potential field method during switching is optimized, and finally, a path planned together by the secondary A-star algorithm and the bidirectional artificial potential field method is optimized by a Bezier curve so as to be in accordance with the driving track of the garbage recycling trolley.
5. An obstacle avoidance path planning method for an autonomous vehicle according to claim 4, characterized in that: the secondary A-star algorithm is that after the cost values of a path and child nodes in the neighborhood of a father node are obtained by the A-star algorithm, the child node which is not adjacent to the starting point of the path and has the smallest cost value is selected as a new father node, if no barrier exists between the starting point of the path and the new father node and the time value is also the smallest, the path is updated to remove the middle father node, and the next search is carried out; otherwise, the path is not updated; and continuously and circularly searching until a path from the starting point to the target point is found.
6. An obstacle avoidance path planning method for an autonomous vehicle according to claim 4, characterized in that: the bidirectional artificial potential field method is characterized in that a forward and reverse alternating search mechanism is adopted to improve the artificial potential field method, bidirectional path planning is completed through the bidirectional artificial potential field method, and search efficiency is improved; the path planning by the bidirectional artificial potential field method is to take a starting point A and an end point B of a path as initial positions of forward and reverse searching respectively; when the forward search starts, taking A as a starting point and B as a target point to obtain a next path point A1; and then starting reverse search, taking B as a starting point, and taking the just obtained forward path point A1 as a target point to perform path planning, obtaining the next path point B1 at the moment, completing one-time alternate search, circularly and alternately advancing in such a way until the same path point is searched in the forward and reverse directions, and finally obtaining the path between the meeting point and the two end points, namely the bidirectional artificial potential field path.
7. An obstacle avoidance path planning method for an autonomous vehicle according to claim 1, characterized in that: the joint optimization comprises the steps of dividing a grid into four completely same small grids by using the grid in the quadratic A-star algorithm, and then determining a starting point and a target point of the quadratic A-star algorithm according to the advancing direction of a path in the bidirectional artificial potential field method and the position of a termination point in the four small grids, so as to ensure that the path planning of the joint of the quadratic A-star algorithm and the bidirectional artificial potential field method is reasonable;
after the bidirectional artificial potential field method is terminated due to the fact that the path points repeatedly oscillate, the last three path points including the termination point are selected and deleted from the path points, the exocenter coordinates of a triangle taking the coordinates of the three points as the vertex are obtained through calculation, and the starting point and the target point of the secondary A-star algorithm are selected according to the position of the point, so that the operation result is prevented from being influenced;
for the vehicle which is trapped in the local minimum trap preferentially, the path is not updated any more, and if the vehicle can be separated from the position within a period of time, the path is continuously updated;
when the path planning is carried out by the bidirectional artificial potential field method, parameters are continuously adjusted, and the width of the vehicle is fully considered; after the secondary A-star algorithm, the distance between each barrier is calculated, if the distance is smaller than the width of the vehicle, virtual barriers are added between the barriers, and the secondary A-star algorithm is prevented from carrying out wrong path planning.
8. An obstacle avoidance path planning method for an autonomous vehicle according to claim 1, characterized in that: the dolly is retrieved to rubbish adopts Arduino Mega2560 as the main control board, and the main control board is connected with ESP8266 networking module, UWB orientation module, infrared obstacle avoidance module and voice broadcast module.
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