Unmanned garbage cleaning and transporting vehicle and cleaning and transporting method thereof

Abstract

The invention relates to the technical field of garbage disposal, and discloses an unmanned garbage clearance truck and a clearance method thereof, and the clearance method comprises the following steps: step 1, task triggering and information collection; step 2, task allocation and path planning; 3, autonomous navigation and obstacle avoidance; step 4, garbage collection operation; step 5, transporting; and step 6, data recording. According to the method, an (E) environment sensing model is formed through comprehensive analysis of environment data including laser radar data, camera data and ultrasonic sensor data, so that a vehicle can be navigated in a complex environment, and a navigation path is automatically adjusted according to traffic conditions by utilizing big data analysis, a cloud computing technology and real-time traffic information; and through an intelligent recognition system and a machine vision technology, the position and posture of the mechanical arm are automatically adjusted during garbage collection, precise parking is achieved, and the beneficial effects of autonomous navigation and intelligent recognition of the recycling bin are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage disposal, and in particular to an unmanned garbage collection and transportation vehicle and a collection and transportation method thereof. Background Art

[0002] With the increasing development of the national economy and the continuous progress of science and technology, people's living standards are constantly improving, but at the same time, it has also brought about a continuous surge in domestic garbage. The effective collection, classification and treatment of a large amount of domestic garbage has become the top priority of environmental protection work in major cities in recent years. The garbage trucks on the market need to place the garbage cans on the lifting device of the garbage truck in advance, or operate the mechanical arm to grab the garbage cans, and then the garbage truck will lift and flip the garbage cans to transport the garbage into the garbage truck.

[0003] With the acceleration of urbanization, the amount of urban garbage generated has increased dramatically. The traditional manual driving garbage collection method faces problems such as high labor costs, low operating efficiency, and great safety hazards. Summary of the invention

[0004] Technical issues solved:

[0005] In view of the deficiencies in the prior art, the present invention provides an unmanned garbage collection vehicle and a garbage collection method thereof, which has the advantages of autonomous navigation, intelligent identification of intelligent recycling bins, and efficient completion of garbage collection, transportation and collection tasks, thus solving the problems of the above-mentioned technologies.

[0006] Technical solution:

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an unmanned garbage collection vehicle, comprising a vehicle body, a power system, a mechanical arm, a garbage collection device and a garbage compression device, the vehicle body also comprising a sensor system, a control system and a communication system, the sensor system comprising a laser radar, a camera, and an ultrasonic sensor, the sensor system being used to perceive the surrounding environment and the state of a target garbage bin in real time; the control system integrating GPS navigation, path planning, obstacle recognition and obstacle avoidance algorithms, being used to ensure that the vehicle performs tasks safely and efficiently;

[0008] The power system includes an energy system, and the energy module is used to ensure long-term operation capability; the energy system is driven by clean energy, including electric or hydrogen energy, and uses fast charging or automatic battery replacement technology.

[0009] A method for removing garbage using an unmanned garbage removal vehicle, the method comprising the following steps:

[0010] Step 1: Task triggering and information collection: Through the central control end human-computer interaction information platform, the overflow status of each garbage collection point in the city is monitored in real time. When a smart recycling bin reaches the set overflow threshold, the system automatically generates a removal demand, combines sensor data and historical garbage generation, analyzes garbage generation trends in different areas, and confirms the specific areas and times that need to be removed;

[0011] Step 2: Task allocation and route planning: Using big data analysis and cloud computing technology, based on real-time traffic information, smart recycling bins and vehicle locations, intelligently allocate collection tasks to each driverless garbage collection truck. Through an integrated control system, using GPS navigation and route planning algorithms, the optimal driving route is developed for each vehicle to ensure efficient collection.

[0012] Step 3: Autonomous navigation and obstacle avoidance: The unmanned garbage truck uses GPS and built-in maps to reach the target collection area according to the set navigation path. Through the integrated sensor system: lidar, camera, ultrasonic sensor, it can perceive the surrounding environment in real time, identify obstacles and pedestrians, and apply obstacle recognition algorithms during driving to adjust the driving route in real time.

[0013] Step 4: Garbage collection operation: Precise parking: After the vehicle arrives at the designated intelligent recycling bin, it automatically adjusts its posture through the intelligent recognition system and machine vision technology to achieve precise parking, start the robotic arm and garbage collection device, and automatically complete the collection, compression and loading of garbage according to the recognized instructions;

[0014] Step 5: Transportation: After the garbage is loaded, the garbage truck will transport it to the designated removal location according to the planned route and remove it;

[0015] Step 6. Data recording: After the removal is completed, the system records the data of each removal, including the amount of garbage, removal time, and driving route information, providing basic data for subsequent analysis.

[0016] Preferably, the overflow threshold is determined by:

[0017] If V current ≥T, then a removal demand is generated

[0018] Where: V current is the amount of garbage in the current smart recycling bin; T is the set overflow threshold of the smart recycling bin;

[0019] The garbage generation prediction expression in step 1 is:

[0020] W t(n+1 )=a*t(n+1)+b

[0021] Where: W t(n+1) is the predicted amount of garbage generated at the next time point t(n+1); a is the slope in the linear regression, representing the rate of change of the amount of garbage over time; b is the intercept in the linear regression, representing the initial amount of garbage.

[0022] Preferably, the step 1 further includes a removal urgency index:

[0023]

[0024] Among them: U is the urgency index of removal, which is used to determine the priority of removal; is the growth rate of the amount of garbage;

[0025] Estimation of the garbage growth rate:

[0026]

[0027] Where: W t(n) and W t(n-1) are the garbage generation at the current moment and the previous moment respectively, t(n) and t(n-1) are the current time point and the previous time point respectively.

[0028] Preferably, in step 2, the task is assigned to vehicle i:

[0029] Task i =f(V garbage , D traffic , P vehicle )

[0030] Among them: Task i : The cleaning task assigned to vehicle i; V garbage is the number and location of the smart recycling bins to be removed; D traffic Real-time traffic information, including traffic flow and congestion conditions; vehicle The current location and status information of the vehicle;

[0031] The expression of real-time traffic information impact weight is:

[0032]

[0033] Where: W traffic is the weight of real-time traffic information, which is used to influence path planning; C traffic Score the current traffic situation: flow, speed; C max : Maximum traffic situation score, as a standardized benchmark; W max is the maximum weight coefficient.

[0034] Preferably, the optimization expression of the removal path in step 2 is:

[0035] Shortest path (P) = f(position start ,Location end , D traffic )

[0036] Among them: the shortest path (P) is the optimal driving path of the vehicle from the starting position to the target position; position start is the current vehicle position, position end The target location for the smart recycling bin.

[0037] Preferably, the navigation path setting in step 3 is:

[0038] Navigation path (P) = f(position 2 start , position 2 end , GPS information, map data)

[0039] Where: Position 2 start is the starting position of the vehicle; Position 2 end The target removal area location of the vehicle;

[0040] The environment perception model is:

[0041] Environmental data (E) = f (lidar data, camera data, ultrasonic sensor data).

[0042] Preferably, the precise docking algorithm expression in step 4 is:

[0043] Stop position (P stop ) = f(target position, vehicle position, sensor data)

[0044] Where: P stop = target position + AP; AP is the displacement that the vehicle needs to adjust.

[0045] Preferably, the transportation time estimation in step 5 is:

[0046] Estimated transportation time (T estimated )=f(T path , D traffic , vehicle speed)

[0047] Where: Estimated transportation time (T estimated ) is the estimated time to complete the transportation; D traffic It is real-time traffic condition data, which affects the transportation time; the vehicle speed is the driving speed of the cleaning truck, which may vary according to the situation.

[0048] Preferably, the data recording operation:

[0049] Record data (R data ) = T (amount of garbage, time of removal, driving route information)

[0050] Among them: Record data (R data ) is the basic data recorded after the removal is completed, including: garbage volume: the total amount of garbage collected in one removal; removal time: the total time from departure to completion of removal; driving path information: the specific path data of the vehicle during the removal process.

[0051] Compared with the prior art, the present invention provides an unmanned garbage collection vehicle and a garbage collection method thereof, which have the following beneficial effects:

[0052] 1. The present invention integrates laser radar, camera and ultrasonic sensor to perceive the surrounding environment in real time, identify obstacles and pedestrians, adopts GPS navigation and path planning algorithm, and formulates the optimal driving path before the vehicle sets off to ensure efficient and safe transportation process. Through comprehensive analysis of environmental data: laser radar data, camera data, ultrasonic sensor data, an (E) environmental perception model is formed to enable the vehicle to navigate in complex environments. By using big data analysis, cloud computing technology and real-time traffic information, the navigation path is automatically adjusted according to traffic conditions to further improve the accuracy and efficiency of autonomous navigation. Through the human-computer interaction information platform of the central control end, the overflow status of the garbage collection point is monitored in real time. Once a smart recycling bin reaches the set overflow threshold, the system automatically generates a transportation demand. In step 4, through the intelligent recognition system and machine vision technology, the position and posture of the robotic arm are automatically adjusted when collecting garbage to achieve precise docking, thereby achieving the beneficial effects of autonomous navigation and intelligent recognition of smart recycling bins.

[0053] 2. Through the human-computer interaction information platform of the central control end, the system of the present invention can monitor the overflow status of various garbage collection points in the city in real time. Once a certain intelligent recycling bin reaches the set overflow threshold, the system will automatically generate a removal demand to ensure timely processing. Combined with sensor data and historical garbage generation, the system can analyze the garbage generation trends in different areas, thereby confirming the specific areas and times that need to be removed. This predictability helps to avoid unnecessary scheduling and waste and improves efficiency. The system uses big data analysis and cloud computing technology to intelligently assign removal tasks to each unmanned garbage truck to ensure the rational use of resources and avoid excessive burdens on a single vehicle. The control system integrates GPS navigation, real-time traffic information and path planning algorithms. The vehicle can intelligently plan the optimal driving route according to the current traffic conditions, reduce driving time and fuel consumption, and achieve the beneficial effect of efficiently completing garbage collection, transportation and removal tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 , an unmanned garbage collection vehicle, including a vehicle body, a power system, a mechanical arm, a garbage collection device and a garbage compression device. The vehicle body also includes a sensor system, a control system and a communication system. The sensor system includes a laser radar, a camera, and an ultrasonic sensor. The sensor system is used to perceive the surrounding environment and the state of the target intelligent recycling box in real time; the control system integrates GPS navigation, path planning, obstacle recognition and obstacle avoidance algorithms to ensure that the vehicle performs tasks safely and efficiently;

[0057] The power system includes an energy system, and the energy module is used to ensure long-term operation capability; the energy system is driven by clean energy, including electric or hydrogen energy, and uses fast charging or automatic battery replacement technology.

[0058] A method for removing garbage using an unmanned garbage removal vehicle, the steps of the method are as follows:

[0059] Step 1: Task triggering and information collection: Through the central control end human-computer interaction information platform, the overflow status of each garbage collection point in the city is monitored in real time. When a smart recycling bin reaches the set overflow threshold, the system automatically generates a removal demand, combines sensor data and historical garbage generation, analyzes garbage generation trends in different areas, and confirms the specific areas and times that need to be removed;

[0060] Step 2: Task allocation and route planning: Using big data analysis and cloud computing technology, based on real-time traffic information, smart recycling bins and vehicle locations, intelligently allocate collection tasks to each driverless garbage collection truck. Through an integrated control system, using GPS navigation and route planning algorithms, the optimal driving route is developed for each vehicle to ensure efficient collection.

[0061] Step 3: Autonomous navigation and obstacle avoidance: The unmanned garbage truck uses GPS and built-in maps to reach the target collection area according to the set navigation path. Through the integrated sensor system: lidar, camera, ultrasonic sensor, it can perceive the surrounding environment in real time, identify obstacles and pedestrians, and apply obstacle recognition algorithms during driving to adjust the driving route in real time.

[0062] Step 4: Garbage collection operation: Precise parking: After the vehicle arrives at the designated intelligent recycling bin, it automatically adjusts its posture through the intelligent recognition system and machine vision technology to achieve precise parking, start the robotic arm and garbage collection device, and automatically complete the collection, compression and loading of garbage according to the recognized instructions;

[0063] Step 5: Transportation: After the garbage is loaded, the garbage truck will transport it to the designated removal location according to the planned route and remove it;

[0064] Step 6. Data recording: After the removal is completed, the system records the data of each removal, including the amount of garbage, removal time, and driving route information, providing basic data for subsequent analysis.

[0065] By integrating lidar, camera and ultrasonic sensor, it can sense the surrounding environment in real time, identify obstacles and pedestrians, and use GPS navigation and path planning algorithm to formulate the optimal driving path before the vehicle sets off to ensure efficient and safe transportation. Through comprehensive analysis of environmental data: lidar data, camera data, ultrasonic sensor data, an (E) environmental perception model is formed to enable the vehicle to navigate in complex environments. By using big data analysis, cloud computing technology and real-time traffic information, the navigation path is automatically adjusted according to traffic conditions to further improve the accuracy and efficiency of autonomous navigation. Through the human-computer interaction information platform of the central control end, the overflow status of the garbage collection point can be monitored in real time. Once a smart recycling bin reaches the set overflow threshold, the system automatically generates a transportation demand. In step 4, through the intelligent recognition system and machine vision technology, the position and posture of the robotic arm are automatically adjusted when collecting garbage to achieve precise docking.

[0066] Specifically, overflow threshold judgment:

[0067] If V current ≥T, then a removal demand is generated

[0068] Where: V current is the amount of garbage in the current smart recycling bin; T is the set overflow threshold of the smart recycling bin;

[0069] The prediction expression of garbage generation in step 1 is:

[0070] W t(n+1 )=a*t(n+1)+b

[0071] Where: W t(n+1) is the predicted amount of garbage generated at the next time point t(n+1); a is the slope in the linear regression, representing the rate of change of the amount of garbage over time; b is the intercept in the linear regression, representing the initial amount of garbage.

[0072] Specifically, step one also includes the removal urgency index:

[0073]

[0074] Among them: U is the urgency index of removal, which is used to determine the priority of removal; is the growth rate of the amount of garbage;

[0075] Estimation of garbage growth rate:

[0076]

[0077] Where: W t(n) and W t(n-1) are the garbage generation at the current moment and the previous moment respectively, t(n) and t(n-1) are the current time point and the previous time point respectively.

[0078] Specifically, in step 2, tasks are assigned to vehicle i:

[0079] Task i =f(V garbage , D traffic , P vehicle )

[0080] Among them: Task i : The cleaning task assigned to vehicle i; V garbage is the number and location of the smart recycling bins to be removed; D traffic Real-time traffic information, including traffic flow and congestion conditions; vehicle The current location and status information of the vehicle;

[0081] The expression of real-time traffic information impact weight is:

[0082]

[0083] Where: W traffic is the weight of real-time traffic information, which is used to influence path planning; C traffic Score the current traffic situation: flow, speed; C max : Maximum traffic situation score, as a standardized benchmark; W max is the maximum weight coefficient.

[0084] Specifically, the optimization expression of the removal path in step 2 is:

[0085] Shortest path (P) = f(position start ,Location end , D traffic )

[0086] Among them: the shortest path (P) is the optimal driving path of the vehicle from the starting position to the target position; position startis the current vehicle position, position end The target location for the smart recycling bin.

[0087] Specifically, the navigation path setting in step 3:

[0088] Navigation path (P) = f(position 2 start , position 2 end , GPS information, map data)

[0089] Where: Position 2 start is the starting position of the vehicle; Position 2 end The target removal area location of the vehicle;

[0090] The environmental perception model is:

[0091] Environmental data (E) = {(lidar data camera data ultrasonic sensor data).

[0092] Specifically, the precise docking algorithm expression in step 4 is:

[0093] Stop position (P stop ) = f(target position, vehicle position, sensor data)

[0094] Where: P stop = target position + AP; AP is the displacement that the vehicle needs to adjust.

[0095] Specifically, the transportation time estimation in step 5 is:

[0096] Estimated transportation time (T estimated )=f(T path , D traffic , vehicle speed)

[0097] Where: Estimated transportation time (T estimated ) is the estimated time to complete the transportation; D traffic It is real-time traffic condition data, which affects the transportation time; the vehicle speed is the driving speed of the cleaning truck, which may vary according to the situation.

[0098] Specifically, data recording operations:

[0099] Record data (R data ) = f (amount of garbage, time of removal, driving route information)

[0100] Among them: Record data (R data) is the basic data recorded after the removal is completed, including: garbage volume: the total amount of garbage collected in one removal; removal time: the total time from departure to completion of removal; driving path information: the specific path data of the vehicle during the removal process. Through the human-computer interaction information platform of the central control end, the system can monitor the overflow status of various garbage collection points in the city in real time. Once a smart recycling bin reaches the set overflow threshold, the system will automatically generate a removal demand to ensure timely processing. Combined with sensor data and historical garbage generation, the system can analyze the garbage generation trend in different areas, thereby confirming the specific areas and times that need to be removed. This foresight helps avoid unnecessary scheduling and waste and improves efficiency. The system uses big data analysis and cloud computing technology to intelligently assign removal tasks to each unmanned garbage truck to ensure the rational use of resources and avoid excessive burden on a single vehicle. The control system integrates GPS navigation, real-time traffic information and path planning algorithms. The vehicle can intelligently plan the optimal driving path according to the current traffic conditions to reduce driving time and fuel consumption.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An unmanned garbage collection vehicle, comprising a vehicle body, a power system, a mechanical arm, a garbage collection device and a garbage compression device, characterized in that: The vehicle body also includes a sensor system, a control system and a communication system. The sensor system includes a laser radar, a camera, and an ultrasonic sensor. The sensor system is used to sense the surrounding environment and the state of the target trash can in real time. The control system integrates GPS navigation, path planning, obstacle recognition and avoidance algorithms to ensure that the vehicle performs tasks safely and efficiently. The power system includes an energy system, and the energy module is used to ensure long-term operation capability; the energy system is driven by clean energy, including electric or hydrogen energy, and uses fast charging or automatic battery replacement technology.

2. A method for removing garbage using an unmanned garbage removal vehicle, characterized in that: The cleaning and transportation method is used for the cleaning and transportation vehicle according to claim 1, and the steps of the cleaning and transportation method are: Step 1: Task triggering and information collection: Through the central control end human-computer interaction information platform, the overflow status of each garbage collection point in the city is monitored in real time. When a smart recycling bin reaches the set overflow threshold, the system automatically generates a removal demand, combines sensor data and historical garbage generation, analyzes garbage generation trends in different areas, and confirms the specific areas and times that need to be removed; Step 2: Task allocation and route planning: Using big data analysis and cloud computing technology, based on real-time traffic information, smart recycling bins and vehicle locations, intelligently allocate collection tasks to each driverless garbage collection truck. Through an integrated control system, using GPS navigation and route planning algorithms, the optimal driving route is developed for each vehicle to ensure efficient collection. Step 3: Autonomous navigation and obstacle avoidance: The unmanned garbage truck uses GPS and built-in maps to reach the target collection area according to the set navigation path. Through the integrated sensor system: lidar, camera, ultrasonic sensor, it can perceive the surrounding environment in real time, identify obstacles and pedestrians, and apply obstacle recognition algorithms during driving to adjust the driving route in real time. Step 4: Garbage collection operation: Precise parking: After the vehicle arrives at the designated intelligent recycling bin, it automatically adjusts its posture through the intelligent recognition system and machine vision technology to achieve precise parking, start the robotic arm and garbage collection device, and automatically complete the collection, compression and loading of garbage according to the recognized instructions; Step 5: Transportation: After the garbage is loaded, the garbage truck will transport it to the designated removal location according to the planned route and remove it; Step 6. Data recording: After the removal is completed, the system records the data of each removal, including the amount of garbage, removal time, and driving route information, providing basic data for subsequent analysis.

3. The method for removing garbage using an unmanned garbage removal vehicle according to claim 2, characterized in that: The overflow threshold determination: If V current ≥T, then a removal demand is generated Where: V current is the amount of garbage in the current smart recycling bin; T is the set overflow threshold of the smart recycling bin; The garbage generation prediction expression in step 1 is: W t(n+1) =a*t(n+1)+b Where: W t(n+1) is the predicted amount of garbage generated at the next time point t(n+1); a is the slope in the linear regression, representing the rate of change of the amount of garbage over time; b is the intercept in the linear regression, representing the initial amount of garbage.

4. The method for removing garbage using an unmanned garbage removal vehicle according to claim 3, characterized in that: The step 1 also includes a removal urgency index: Among them: U is the urgency index of removal, which is used to determine the priority of removal; is the growth rate of the amount of garbage; Estimation of the garbage growth rate: Where: W t(n) and W t(n-1) are the garbage generation at the current moment and the previous moment respectively, t(n) and t(n-1) are the current time point and the previous time point respectively.

5. The method for removing garbage using an unmanned garbage removal vehicle according to claim 4, characterized in that: In step 2, tasks are assigned to vehicle i: Task i =f(V garbage ,D traffic ,P vehicle Among them: Task i : The cleaning task assigned to vehicle i; V garbage is the number and location of the garbage bins to be removed; D traffic Real-time traffic information, including traffic flow and congestion conditions; ve h icle The current location and status information of the vehicle; The expression of real-time traffic information impact weight is: Where: W traffic is the weight of real-time traffic information, which is used to influence path planning; C traffic Score the current traffic situation: flow, speed; C max : Maximum traffic situation score, as a standardized benchmark; W max is the maximum weight coefficient.

6. The method for removing garbage using an unmanned garbage removal vehicle according to claim 5, characterized in that: The optimization expression of the cleaning path in step 2 is: Shortest path (P) = f(position start ,Location end ,D traffic Among them: the shortest path (P) is the optimal driving path of the vehicle from the starting position to the target position; position start is the current vehicle position, position end The target location for the trash can.

7. The method for removing garbage using an unmanned garbage removal vehicle according to claim 6, characterized in that: In step 3, the navigation path is set: Navigation path (P) = f(position 2 start , position 2 end , GPS information, map data) Where: Position 2 start is the starting position of the vehicle; Position 2 end The target removal area location of the vehicle; The environment perception model is: Environmental data (E) = f (lidar data, camera data, ultrasonic sensor data).

8. The method for removing garbage using an unmanned garbage removal vehicle according to claim 7, characterized in that: The precise docking algorithm expression in step 4 is: Stop position (P stop ) = f(target position, vehicle position, sensor data) Where: P stop = target position + ΔP; ΔP is the displacement that the vehicle needs to adjust.

9. The method for removing garbage using an unmanned garbage removal vehicle according to claim 8, characterized in that: Estimation of transportation time in step 5: Estimated transportation time (T estimated )=f(T path ,D traffic , vehicle speed Where: Estimated transportation time (T estimated ) is the estimated time to complete the transportation; D traffic It is real-time traffic condition data, which affects the transportation time; the vehicle speed is the driving speed of the cleaning truck, which may vary according to the situation.

10. The method for removing garbage using an unmanned garbage removal vehicle according to claim 9, characterized in that: The data recording operation: Record data (R data ) = f (amount of garbage, time of removal, driving route information) Among them: Record data (R data ) is the basic data recorded after the removal is completed, including: garbage volume: the total amount of garbage collected in one removal; removal time: the total time from departure to completion of removal; driving path information: the specific path data of the vehicle during the removal process.

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