CN108280518B - Distributed environment robot and vehicle mobile interconnection carrying method and system - Google Patents
Distributed environment robot and vehicle mobile interconnection carrying method and system Download PDFInfo
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Abstract
The invention discloses a distributed environment robot and vehicle mobile interconnection carrying method and system, wherein the method comprises the following steps: step 1: arranging a ground guide rail between the workbenches where the transportation starting point and the transportation terminal point are located, arranging a guide rail on the workbench, and arranging a landmark on the ground guide rail; step 2: a desktop robot positioned on the object taking workbench grabs the object from the object taking designated position; and step 3: the mobile robot moves along the ground guide rail and grabs and transports an object to another workbench; and 4, step 4: after grabbing an object, a desktop robot positioned on the object placing worktable moves to an object placing designated position of the object placing worktable; and 5: combining an extreme learning machine and a PID neural network, establishing an electric quantity prediction model to make a decision on the next action of the mobile robot; the timing and fixed-point transportation of objects in the industrial laboratory is completed through the cooperation of the desktop robot and the mobile robot, and all-weather transportation of the industrial laboratory is realized.
Description
Technical Field
The invention belongs to the field of robot transportation, and particularly relates to a distributed environment robot and vehicle mobile interconnection carrying method and system.
Background
To deal with a new round of scientific and technological revolution and industrial change, China proposes to realize the manufacturing of 2025 in China. In the face of intense competitive environment at home and abroad, China takes 'intelligent manufacturing' as a main attack direction, but is basically in the laboratory research and development stage at present. Automated industrial laboratories have many advantages in that they can save time for personnel to perform repetitive tasks, ensure safety, reduce personnel training requirements, reduce human error, and improve productivity and experimental accuracy.
Automation of industrial laboratories is a relatively new field, the development of which has led to a huge change in workflow. In this revolution, mobile robots are of particular interest in order to accomplish relatively simple, highly repeatable, but highly accurate tasks in automated laboratories. In the laboratory, autonomous navigation of mobile robots during the execution of transport tasks is mostly done by path planning. However, the instability and the route randomness of the path planning itself are a big drawback of the method. Meanwhile, the mobile robot breaks down when executing the transportation task, and most of the transportation task can only be interrupted. In addition, if the power prediction of the robot cannot be performed in real time during transportation, the robot may not continue to operate due to power exhaustion during transportation, and thus transportation efficiency may be reduced. Therefore, in an industrial automation laboratory, a method and a system for robot mobile internet loading are urgently needed to efficiently realize transportation tasks.
Disclosure of Invention
The invention aims to provide a distributed environment robot and vehicle moving interconnection carrying method and system, which can finish timing and fixed-point transportation of objects in an automated laboratory through the cooperation of a desktop robot and a mobile robot, wherein the desktop robot is responsible for taking and placing the objects in a short distance, the mobile robot finishes long-distance transportation of the objects by using a guide rail and a landmark, and the all-weather transportation of the automated laboratory is realized by matching with the arrangement of the guide rail and a charging area.
A distributed environment robot and vehicle moving interconnection carrying method comprises the following steps:
step 1: a ground guide rail is arranged between the workbenches where the transportation starting point and the transportation terminal point are located, a desktop guide rail, a fetching designated position, a temporary object placing and fetching position and an object placing designated position are arranged on the workbench, and a remote controller is used for sending fetching instructions;
the desktop guide rail is arranged in the middle of the workbench, the fetching designated position, the temporary placing and fetching position and the placing designated position are arranged on two sides of the workbench, photosensitive sensors are arranged on the fetching designated position, the temporary placing and fetching position and the placing designated position, and the photosensitive sensors are connected with a desktop controller on the workbench;
a distance measuring sensing receiver is arranged at the edge of the temporary object placing and taking position of the workbench;
the object fetching instruction is used for conveying an object from an object fetching specified position to an object placing specified position;
step 2: a desktop robot positioned on the object taking workbench receives the object taking instruction, grabs an object from an object taking designated position on the object taking workbench along a desktop guide rail according to a signal acquired by the photosensitive sensor, and then places the object to a first temporary placing designated position of the object taking workbench;
and step 3: the mobile robot receives the fetching instruction, moves to the edge of the temporary placing designated position of the fetching workbench along the ground guide rail, and grabs the object when the distance signal measured by the distance measuring sensor of the fetching workbench meets the object grabbing distance;
moving to the edge of a second temporary placing designated position of the object placing workbench along the ground guide rail, and placing the grabbed object to the second temporary placing designated position of the object placing workbench when a distance signal measured by a distance measuring sensor of the object placing workbench meets the object placing distance;
and 4, step 4: the desktop robot positioned on the object placing worktable receives the object taking instruction, moves to a second temporary object placing designated position of the object placing worktable along the desktop guide rail, and moves to the object placing designated position of the object placing worktable according to signals collected by the photosensitive sensor after grabbing the object, so as to finish the transportation of the object between the worktables;
and 5: after the mobile robot finishes one-time transportation, acquiring an electric quantity predicted value of the mobile robot at the next moment by adopting a mobile robot electric quantity prediction model based on an intelligent network according to electric quantities of the mobile robot at four continuous moments including the current moment, and judging whether to continue to execute a transportation task according to the electric quantity predicted value;
the electric quantity of the mobile robot at the four continuous moments including the current moment is two-layer wavelet decomposition of electric quantity data of the mobile robot at the 500 continuous moments including the current moment, and two groups of high-frequency component electric quantity and two groups of low-frequency component electric quantity of the last four moments are taken out of the electric quantity data;
the intelligent network-based mobile robot electric quantity prediction model comprises an extreme learning machine-based mobile robot electric quantity high-frequency prediction model and a PID neural network-based mobile robot electric quantity low-frequency prediction model; training data used in the construction of the prediction model are high-frequency component quantity and low-frequency component quantity after electric quantity data are subjected to two-layer wavelet decomposition at each moment in the whole process from starting to stopping of the mobile robot;
the mobile robot electric quantity high-frequency prediction model based on the extreme learning machine is obtained by training the extreme learning machine by taking high-frequency component electric quantity at 4 continuous moments as input data and taking high-frequency component electric quantity at the next moment as output data; the mobile robot electric quantity low-frequency prediction model based on the PID neural network is obtained by training the PID neural network by taking the low-frequency component electric quantity of 4 continuous moments as input data and taking the low-frequency component electric quantity of the next moment as output data;
sequentially inputting two groups of high-frequency component electric quantity and two groups of low-frequency component electric quantity at the last four moments into a mobile robot electric quantity high-frequency prediction model based on an extreme learning machine and a mobile robot electric quantity low-frequency prediction model based on a PID (proportion integration differentiation) neural network to obtain two groups of electric quantity high-frequency prediction values and two groups of electric quantity low-frequency prediction values, and taking the accumulated value of the two groups of electric quantity high-frequency prediction values and the two groups of electric quantity low-frequency prediction values as the electric quantity prediction value of the mobile robot at the;
if the predicted value of the electric quantity of the mobile robot at the next moment is more than 25%, the mobile robot returns along the original path of the guide rail to wait for the next transportation task;
and if the predicted value of the electric quantity at the next moment of the mobile robot is less than or equal to 25%, the mobile robot drives to a charging area of the mobile robot through the guide rail, the charging information is fed back to the remote controller, and meanwhile, the remote controller sends an instruction to the standby robot on the same guide rail to move to the initial position of the transportation task on the corresponding rail to replace the mobile robot needing to be charged to carry out the transportation task.
Further, optimizing a weight value and a threshold value in the extreme learning machine-based mobile robot electric quantity high-frequency prediction model by adopting a genetic algorithm;
step A1: initializing a population by taking genes of individual population as a weight and a threshold in a mobile robot electric quantity high-frequency prediction model based on an extreme learning machine;
the value range of the population scale is [50,200], the value range of the cross probability is [0.3,0.5], the value range of the variation probability is [0.05,0.15], the value range of the maximum iteration frequency is [100,500], and the value range of the maximum search precision is [0.005,0.1 ];
step A2: setting a fitness function and acquiring the optimal individual in the population;
substituting the weight value and the threshold value corresponding to the population individual gene into a mobile robot electric quantity high-frequency prediction model based on the extreme learning machine, calculating an output value by using the electric quantity prediction model based on the weight value and the threshold value determined by the population individual gene, and taking the reciprocal of the Mean Square Error (MSE) of the output value and an actual value as a first fitness function f1 (x);
step A3: calculating the fitness of each individual, and entering the sub-population with the maximum individual fitness of each population; individuals in the sub-population are not subjected to cross variation operation and are directly reserved to the next generation;
step A4: selecting a crossover operator and a mutation operator by adopting a roulette method;
step A5: updating individuals except the sub-population in the population by using a crossover operator and a mutation operator;
step A6: calculating the fitness of all updated individuals, judging whether the fitness reaches the maximum iteration times or the maximum search precision, if so, taking the individual with the maximum fitness as an optimal individual, and outputting the weight and the threshold of the mobile robot electric quantity high-frequency prediction model based on the extreme learning machine corresponding to the optimal individual, otherwise, returning to the step A4.
Further, the weight and the threshold in the PID neural network-based low-frequency electric quantity prediction model of the mobile robot are obtained by optimizing through a quantum particle swarm algorithm with a self-adaptive weight;
step B1: setting the position vector of each individual in the quantum particle swarm as a weight and a threshold in a mobile robot electric quantity low-frequency prediction model based on a PID neural network, and initializing each parameter of the swarm individual into a random number of [ -11 ];
the value range of the number of the quantum particle swarm is [20,80], the value range of the number of the particles of the quantum particle swarm is [10,50], the value range of the maximum iteration number is [200, 1000], the value range of the iteration number for constructing the elite swarm is [50,200], the value range of the premature convergence judgment threshold is [0.01, 0.05], and the value range of the worst particle variation ratio delta% of the swarm is [ 2%, 6% ];
step B2: setting a fitness function, and determining an optimal individual position vector of the quantum particles;
substituting the weight and the threshold corresponding to the quantum particle individual position vector into the weight and the threshold in the PID neural network-based mobile robot electric quantity low-frequency prediction model, calculating an output value by using the PID neural network-based mobile robot electric quantity low-frequency prediction model determined by the population individual position, and taking the reciprocal of the Mean Square Error (MSE) of the output value and the actual value as a second fitness function f2 (x);
step B3: operating a premature convergence judgment mechanism;
if the group fitness variance of the quantum particle swarm is smaller than the premature convergence judgment threshold value gamma, the worst particle and the group extreme value of delta% are varied, and the leading global extreme value is updated;
step B4: judging whether to establish an elite population;
when the iteration times are larger than the iteration times of the elite population, extracting extreme values of all populations through information sharing among the populations to establish the elite population, and turning to the step B8, otherwise, turning to the step B5;
step B5: randomly updating the parameters of each population of particles;
step B6: recalculating and comparing the fitness of each particle, and if the fitness is better than the current individual extreme value, updating the individual extreme value; comparing the global extreme value, if the global extreme value is superior to the current group extreme value, updating the global extreme value, and turning to the step B2;
step B7: the elite population continues to evolve;
step B8: and judging whether the maximum iteration times are met, if so, exiting, otherwise, turning to the step B4 until a global optimal value is found, and outputting the weight and the threshold of the PID neural network-based mobile robot electric quantity low-frequency prediction model.
Further, whether the mobile robot breaks down or not is judged according to landmarks with unique IDs, which are arranged at equal intervals on a ground guide rail, of the mobile robot, and the specific process is as follows:
the mobile robot returns the landmark information passing through the ground guide rail to the remote controller in real time, after the mobile robot returns the information passing through the landmark a, if the mobile robot does not return the information passing through the next landmark a +1 within the specified time range, the remote controller judges that the current mobile robot has a fault, and judges the position of the fault mobile robot according to the positions of the landmark a and the next landmark a + 1.
Further, when the mobile robot breaks down on the ground track, the remote controller sends a turnout opening instruction to open a turnout on the ground track and sends a traction instruction to a standby mobile robot which is located on the same ground track as the broken mobile robot, the standby mobile robot is made to move to the location area where the broken mobile robot is located, an electromagnetic suction device of a standby mobile robot base is started to adsorb the broken mobile robot, and the broken mobile robot is pulled to the area to be maintained of the mobile robot through the turnout along the ground guide rail;
an electric control turnout is arranged between the adjacent ground guide rails, and an electric control turnout is arranged between the area to be maintained of the mobile robot and the ground guide rails;
the standby mobile robot is located in a standby robot waiting area, and the standby robot waiting area is located at a transportation task starting point and is communicated with the ground guide rail.
Further, after the standby mobile robot reaches the last landmark a passed by the mobile robot with the fault, the vehicle-mounted binocular camera ZED of the standby mobile robot is opened, the distance between the standby mobile robot and the mobile robot with the fault is measured, and the standby mobile robot is enabled to be close to the mobile robot with the fault according to the measured distance; when the mobile robot for executing the transportation task breaks down and is pulled to a maintenance area, the remote controller sends an instruction to the standby robot in the idle state on the same track to replace the broken robot to execute the transportation task.
Further, the area to be maintained of the mobile robot is communicated with the charging guide rail, if the electric quantity of the standby robot in the area to be maintained of the mobile robot is lower than 25%, the standby robot enters the charging area of the mobile robot along the charging guide rail to be charged, and otherwise, the standby robot enters the waiting area of the standby robot at the starting point of the transportation task along the charging guide rail.
A distributed environment robot and vehicle mobile interconnection carrying system comprises a desktop robot, a desktop controller, a mobile robot, a guide rail and a remote controller;
the guide rails comprise desktop guide rails and ground guide rails, electric control turnouts are arranged between the adjacent ground guide rails, and landmarks with unique IDs are arranged on the ground guide rails at intervals;
the desktop controller, the mobile robot and the electric control turnout on the guide rail are all communicated with the remote controller;
the desktop robot is arranged on the workbench and controlled by the desktop controller to move along a desktop guide rail on the workbench, a fixed object taking and placing position is arranged on the workbench, a photosensitive sensor is arranged on the object taking and placing position, and the photosensitive sensor is connected with the desktop controller;
the side edge of the workbench, which is provided with a fixed object taking and placing position, is provided with a ranging sensing receiver;
the mobile robot is provided with a distance measuring sensor and is arranged on the ground, and the mobile robot moves along a ground guide rail on the ground by adopting the method.
Further, the system also comprises a mobile robot charging area and a standby robot waiting area;
the mobile robot charging area is arranged on a charging guide rail between a transport task starting position and a transport task ending position, the charging guide rail is communicated with a standby robot waiting area guide rail, and the standby robot waiting area guide rail is communicated with a ground guide rail.
Further, still treat the maintenance region including mobile robot, mobile robot treats to be provided with automatically controlled switch between maintenance region and the ground guide rail, and is provided with electromagnetic attraction device and on-vehicle two mesh camera ZED on the mobile robot base, on-vehicle two mesh ZED set up the base top at the robot.
Advantageous effects
The invention provides a distributed environment robot and vehicle mobile interconnection carrying method and system, which can complete the timing and fixed-point transportation of objects in an automated laboratory through the cooperation of a desktop robot and a mobile robot, wherein the desktop robot is responsible for taking and placing the objects in a short distance, the mobile robot completes the long-distance transportation of the objects by using a guide rail and a landmark, and the all-weather transportation of the automated laboratory is realized by matching the arrangement of a charging area, and compared with the prior art, the system has the following advantages:
(1) the mobile robot realizes the long-distance directional transportation of objects by utilizing the guide rail, avoids a complex path planning algorithm and the instability of the algorithm, and improves the accuracy and the stability of the transportation;
(2) the parameters of the extreme learning machine are optimized by adopting a thought evolution algorithm and the parameters of the PID neural network are optimized by adopting a quantum particle swarm algorithm with a self-adaptive weight, so that the problem of local convergence is avoided and the electric quantity prediction precision is improved;
(3) the multi-type robots work cooperatively, the desktop robot is responsible for taking and placing objects in a short distance, and the mobile robot is responsible for long-distance transportation of the objects, so that the transportation efficiency is improved;
(4) the position of the mobile robot and the transportation task are simply, effectively and real-timely monitored by the combination of the ground guide rail and the landmark, and the smooth completion of the transportation task is guaranteed;
(5) based on the characteristics that the electric quantity of the robot is unstable and jumps, a mobile robot electric quantity prediction model based on a PID (proportion integration differentiation) neural network and an extreme learning machine is established to make a decision on the next action of the mobile robot, so that the fault rate of a transportation task is effectively reduced.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic structural diagram of a mobile robot according to the present invention;
FIG. 3 is a schematic view of the distribution of the tracks in the transport area of the present invention;
FIG. 4 is a schematic diagram of a power prediction model according to the present invention;
FIG. 5 is a diagram of an interconnect carrier system of the present invention.
Detailed Description
The invention will be further described with reference to the following figures and examples.
As shown in fig. 1, a method for carrying a distributed environment robot and a vehicle in a mobile and interconnected manner includes the following steps:
step 1: a ground guide rail is arranged between the workbenches where the transportation starting point and the transportation terminal point are located, a desktop guide rail, a fetching designated position, a temporary object placing and fetching position and an object placing designated position are arranged on the workbench, and a remote controller is used for sending fetching instructions;
sending an object-taking instruction to the mobile robot and the desktop controller by using the remote controller, moving the mobile robot to an object-taking workbench, and moving the desktop robot to an object-taking designated position to start an object-taking task;
the desktop guide rail is arranged in the middle of the workbench, the fetching designated position, the temporary placing and fetching position and the placing designated position are arranged on two sides of the workbench, photosensitive sensors are arranged on the fetching designated position, the temporary placing and fetching position and the placing designated position, and the photosensitive sensors are connected with a desktop controller on the workbench;
a distance measuring sensing receiver is arranged at the edge of the temporary object placing and taking position of the workbench;
the object fetching instruction is used for conveying an object from an object fetching specified position to an object placing specified position;
step 2: a desktop robot positioned on the object taking workbench receives the object taking instruction, grabs an object from an object taking designated position on the object taking workbench along a desktop guide rail according to a signal acquired by the photosensitive sensor, and then places the object to a first temporary placing designated position of the object taking workbench;
and step 3: the mobile robot receives the fetching instruction, moves to the edge of the temporary placing designated position of the fetching workbench along the ground guide rail, and grabs the object when the distance signal measured by the distance measuring sensor of the fetching workbench meets the object grabbing distance;
moving to the edge of a second temporary placing designated position of the object placing workbench along the ground guide rail, and placing the grabbed object to the second temporary placing designated position of the object placing workbench when a distance signal measured by a distance measuring sensor of the object placing workbench meets the object placing distance;
and 4, step 4: the desktop robot positioned on the object placing worktable receives the object taking instruction, moves to a second temporary object placing designated position of the object placing worktable along the desktop guide rail, and moves to the object placing designated position of the object placing worktable according to signals collected by the photosensitive sensor after grabbing the object, so as to finish the transportation of the object between the worktables;
and 5: after the mobile robot finishes one-time transportation, according to the electric quantity of the mobile robot at four continuous moments including the current moment, acquiring an electric quantity predicted value of the mobile robot at the next moment by adopting a mobile robot electric quantity prediction model based on an intelligent network, as shown in fig. 4, and judging whether to continue to execute a transportation task according to the electric quantity predicted value;
the electric quantity of the mobile robot at the four continuous moments including the current moment is two-layer wavelet decomposition of electric quantity data of the mobile robot at the 500 continuous moments including the current moment, and two groups of high-frequency component electric quantity and two groups of low-frequency component electric quantity of the last four moments are taken out of the electric quantity data;
the intelligent network-based mobile robot electric quantity prediction model comprises an extreme learning machine-based mobile robot electric quantity high-frequency prediction model and a PID neural network-based mobile robot electric quantity low-frequency prediction model; training data used in the construction of the prediction model are high-frequency component quantity and low-frequency component quantity after electric quantity data are subjected to two-layer wavelet decomposition at each moment in the whole process from starting to stopping of the mobile robot;
the mobile robot electric quantity high-frequency prediction model based on the extreme learning machine is obtained by training the extreme learning machine by taking high-frequency component electric quantity at 4 continuous moments as input data and taking high-frequency component electric quantity at the next moment as output data; the mobile robot electric quantity low-frequency prediction model based on the PID neural network is obtained by training the PID neural network by taking the low-frequency component electric quantity of 4 continuous moments as input data and taking the low-frequency component electric quantity of the next moment as output data;
sequentially inputting two groups of high-frequency component electric quantity and two groups of low-frequency component electric quantity at the last four moments into a mobile robot electric quantity high-frequency prediction model based on an extreme learning machine and a mobile robot electric quantity low-frequency prediction model based on a PID (proportion integration differentiation) neural network to obtain two groups of electric quantity high-frequency prediction values and two groups of electric quantity low-frequency prediction values, and taking the accumulated value of the two groups of electric quantity high-frequency prediction values and the two groups of electric quantity low-frequency prediction values as the electric quantity prediction value of the mobile robot at the;
if the predicted value of the electric quantity of the mobile robot at the next moment is more than 25%, the mobile robot returns along the original path of the guide rail to wait for the next transportation task;
and if the predicted value of the electric quantity at the next moment of the mobile robot is less than or equal to 25%, the mobile robot drives to a charging area of the mobile robot through the guide rail, the charging information is fed back to the remote controller, and meanwhile, the remote controller sends an instruction to the standby robot on the same guide rail to move to the initial position of the transportation task on the corresponding rail to replace the mobile robot needing to be charged to carry out the transportation task.
The weight and the threshold in the extreme learning machine-based mobile robot electric quantity high-frequency prediction model are obtained by optimizing through a genetic algorithm;
step A1: initializing a population by taking genes of individual population as a weight and a threshold in a mobile robot electric quantity high-frequency prediction model based on an extreme learning machine;
the value range of the population scale is [50,200], the value range of the cross probability is [0.3,0.5], the value range of the variation probability is [0.05,0.15], the value range of the maximum iteration frequency is [100,500], and the value range of the maximum search precision is [0.005,0.1 ];
step A2: setting a fitness function and acquiring the optimal individual in the population;
substituting the weight value and the threshold value corresponding to the population individual gene into a mobile robot electric quantity high-frequency prediction model based on the extreme learning machine, calculating an output value by using the electric quantity prediction model based on the weight value and the threshold value determined by the population individual gene, and taking the reciprocal of the Mean Square Error (MSE) of the output value and an actual value as a first fitness function f1 (x);
step A3: calculating the fitness of each individual, and entering the sub-population with the maximum individual fitness of each population; individuals in the sub-population are not subjected to cross variation operation and are directly reserved to the next generation;
step A4: selecting a crossover operator and a mutation operator by adopting a roulette method;
step A5: updating individuals except the sub-population in the population by using a crossover operator and a mutation operator;
step A6: calculating the fitness of all updated individuals, judging whether the fitness reaches the maximum iteration times or the maximum search precision, if so, taking the individual with the maximum fitness as an optimal individual, and outputting the weight and the threshold of the mobile robot electric quantity high-frequency prediction model based on the extreme learning machine corresponding to the optimal individual, otherwise, returning to the step A4.
The weight and the threshold in the PID neural network-based low-frequency prediction model of the electric quantity of the mobile robot are obtained by optimizing through a quantum particle swarm algorithm with a self-adaptive weight;
step B1: setting the position vector of each individual in the quantum particle swarm as a weight and a threshold in a mobile robot electric quantity low-frequency prediction model based on a PID neural network, and initializing each parameter of the swarm individual into a random number of [ -11 ];
the value range of the number of the quantum particle swarm is [20,80], the value range of the number of the particles of the quantum particle swarm is [10,50], the value range of the maximum iteration number is [200, 1000], the value range of the iteration number for constructing the elite swarm is [50,200], the value range of the premature convergence judgment threshold is [0.01, 0.05], and the value range of the worst particle variation ratio delta% of the swarm is [ 2%, 6% ];
step B2: setting a fitness function, and determining an optimal individual position vector of the quantum particles;
substituting the weight and the threshold corresponding to the quantum particle individual position vector into the weight and the threshold in the PID neural network-based mobile robot electric quantity low-frequency prediction model, calculating an output value by using the PID neural network-based mobile robot electric quantity low-frequency prediction model determined by the population individual position, and taking the reciprocal of the Mean Square Error (MSE) of the output value and the actual value as a second fitness function f2 (x);
step B3: operating a premature convergence judgment mechanism;
if the group fitness variance of the quantum particle swarm is smaller than the premature convergence judgment threshold value gamma, the worst particle and the group extreme value of delta% are varied, and the leading global extreme value is updated;
step B4: judging whether to establish an elite population;
when the iteration times are larger than the iteration times of the elite population, extracting extreme values of all populations through information sharing among the populations to establish the elite population, and turning to the step B8, otherwise, turning to the step B5;
step B5: randomly updating the parameters of each population of particles;
step B6: recalculating and comparing the fitness of each particle, and if the fitness is better than the current individual extreme value, updating the individual extreme value; comparing the global extreme value, if the global extreme value is superior to the current group extreme value, updating the global extreme value, and turning to the step B2;
step B7: the elite population continues to evolve;
step B8: and judging whether the maximum iteration times are met, if so, exiting, otherwise, turning to the step B4 until a global optimal value is found, and outputting the weight and the threshold of the PID neural network-based mobile robot electric quantity low-frequency prediction model.
In the fetching process, whether the mobile robot breaks down or not is judged according to landmarks with unique IDs, which are arranged at equal intervals on a ground guide rail, of the mobile robot, and the specific process is as follows:
the mobile robot returns the landmark information passing through the ground guide rail to the remote controller in real time, after the mobile robot returns the information passing through the landmark a, if the mobile robot does not return the information passing through the next landmark a +1 within the specified time range, the remote controller judges that the current mobile robot has a fault, and judges the position of the fault mobile robot according to the positions of the landmark a and the next landmark a + 1.
When the mobile robot breaks down on a ground track, the remote controller sends a turnout opening instruction to open a turnout on the ground track and sends a traction instruction to a standby mobile robot which is positioned on the same ground track with the broken mobile robot, the standby mobile robot is made to move to the position area of the broken mobile robot, an electromagnetic suction device of a standby mobile robot base is started to adsorb the broken mobile robot, and the broken mobile robot is pulled to the area to be maintained of the mobile robot through the turnout along a ground guide rail;
an electric control turnout is arranged between the adjacent ground guide rails, and an electric control turnout is arranged between the area to be maintained of the mobile robot and the ground guide rails;
the standby mobile robot is located in a standby robot waiting area, and the standby robot waiting area is located at a transportation task starting point and is communicated with the ground guide rail.
And when the standby mobile robot reaches the last landmark a passed by the mobile robot with the fault, opening a vehicle-mounted binocular camera ZED of the standby mobile robot, measuring the distance between the standby mobile robot and the mobile robot with the fault, and enabling the standby mobile robot to be close to the mobile robot with the fault according to the measured distance.
When the mobile robot for executing the transportation task breaks down and is pulled to a maintenance area, the remote controller sends an instruction to the standby robot in the idle state on the same track to replace the broken robot to execute the transportation task.
Numbering robots on different tracks, wherein a mobile robot on a track 1 is named as 1A, and standby mobile robots in standby mobile robot waiting areas are named as 1B, 1C, 1D, 1E and 1F; the mobile robot on track 2 is named 2A, the standby robots in the standby mobile robot waiting area are named 2B, 2C, 2D, 2E, 2F, and so on for track 3.
If the remote server finds that the mobile robot 1A breaks down when executing the transportation task, the remote server sends an instruction to the standby mobile robot 1B on the same track to pull the broken robot.
After the standby mobile robot 1B successfully pulls the faulty mobile robot 1A to the maintenance area, an instruction is sent to the remote server. And after receiving the instruction, the remote server commands the standby mobile robot 1C on the same track to replace the mobile robot 1A, and the transportation task is restarted.
The standby mobile robot 1B returns to the standby mobile robot waiting area to stand by according to its own electric power.
A charging guide rail is arranged between a starting point and an end point of a transportation task, the charging guide rail is communicated with a ground guide rail, and a charging area of the mobile robot is arranged on the charging guide rail;
after the mobile robot finishes a transportation task, the mobile robot makes the following decisions according to the electric quantity of the mobile robot:
if the predicted value of the electric quantity of the mobile robot at the next moment is more than 25%, returning along the original path of the ground guide rail to wait for the next transportation task;
and if the predicted value of the electric quantity at the next moment of the mobile robot is less than or equal to 25%, the mobile robot moves to the charging guide rail along the ground guide rail to reach the charging area of the mobile robot, and the charging information is fed back to the remote controller.
The mobile robot to-be-maintained area is communicated with the charging guide rail, if the electric quantity of the standby robot in the mobile robot to-be-maintained area is lower than 25%, the standby robot enters the mobile robot charging area along the charging guide rail to be charged, and otherwise, the standby robot enters the standby robot waiting area at the starting point of the transportation task along the charging guide rail.
A distributed environment robot and vehicle mobile interconnected carrying system, as shown in fig. 5, includes a desktop robot, a desktop controller, a mobile robot, a guide rail and a remote controller;
as shown in fig. 3, the guide rails include a desktop guide rail and a ground guide rail, an electrically controlled turnout is arranged between adjacent ground guide rails, and landmarks with unique IDs are arranged on the ground guide rails at intervals;
the desktop controller, the mobile robot and the electric control turnout on the guide rail are all communicated with the remote controller;
the desktop robot is arranged on the workbench and controlled by the desktop controller to move along a desktop guide rail on the workbench, a fixed object taking and placing position is arranged on the workbench, a photosensitive sensor is arranged on the object taking and placing position, and the photosensitive sensor is connected with the desktop controller;
the side edge of the workbench, which is provided with a fixed object taking and placing position, is provided with a ranging sensing receiver;
the mobile robot is provided with a ranging sensor and is arranged on the ground, and the distributed environment robot and the vehicle moving and interconnecting carrying method are adopted to move along a ground guide rail on the ground.
A mobile robot charging area and a standby robot waiting area are also arranged in the transportation area, and the mobile robot waiting area is maintained;
the mobile robot charging area is arranged on a charging guide rail between a transport task starting position and a transport task ending position, the charging guide rail is communicated with a standby robot waiting area guide rail, and the standby robot waiting area guide rail is communicated with a ground guide rail.
Mobile robot treats to be provided with automatically controlled switch between maintenance area and the ground guide rail, and is provided with earth magnetism suction device and on-vehicle two mesh camera ZED on the mobile robot base, as shown in figure 2, on-vehicle two mesh ZED set up the base top at the robot.
The present invention has been described in detail with reference to the specific embodiments, which should not be construed as limiting the invention. Many variations and modifications may be made by one of ordinary skill in the art without departing from the principles of the present invention, which should also be considered within the scope of the present invention.
Claims (10)
1. A distributed environment robot and vehicle moving interconnection carrying method is characterized by comprising the following steps:
step 1: a ground guide rail is arranged between the workbenches where the transportation starting point and the transportation terminal point are located, a desktop guide rail, a fetching designated position, a temporary object placing and fetching position and an object placing designated position are arranged on the workbench, and a remote controller is used for sending fetching instructions;
the desktop guide rail is arranged in the middle of the workbench, the fetching designated position, the temporary placing and fetching position and the placing designated position are arranged on two sides of the workbench, photosensitive sensors are arranged on the fetching designated position, the temporary placing and fetching position and the placing designated position, and the photosensitive sensors are connected with a desktop controller on the workbench;
a distance measuring sensing receiver is arranged at the edge of the temporary object placing and taking position of the workbench;
the object fetching instruction is used for conveying an object from an object fetching specified position to an object placing specified position;
step 2: a desktop robot positioned on the object taking workbench receives the object taking instruction, grabs an object from an object taking designated position on the object taking workbench along a desktop guide rail according to a signal acquired by the photosensitive sensor, and then places the object to a first temporary placing designated position of the object taking workbench;
and step 3: the mobile robot receives the fetching instruction, moves to the edge of the temporary placing designated position of the fetching workbench along the ground guide rail, and grabs the object when the distance signal measured by the distance measuring sensor of the fetching workbench meets the object grabbing distance;
moving to the edge of a second temporary placing designated position of the object placing workbench along the ground guide rail, and placing the grabbed object to the second temporary placing designated position of the object placing workbench when a distance signal measured by a distance measuring sensor of the object placing workbench meets the object placing distance;
and 4, step 4: the desktop robot positioned on the object placing worktable receives the object taking instruction, moves to a second temporary object placing designated position of the object placing worktable along the desktop guide rail, and moves to the object placing designated position of the object placing worktable according to signals collected by the photosensitive sensor after grabbing the object, so as to finish the transportation of the object between the worktables;
and 5: after the mobile robot finishes one-time transportation, acquiring an electric quantity predicted value of the mobile robot at the next moment by adopting a mobile robot electric quantity prediction model based on an intelligent network according to electric quantities of the mobile robot at four continuous moments including the current moment, and judging whether to continue to execute a transportation task according to the electric quantity predicted value;
the electric quantity of the mobile robot at the four continuous moments including the current moment is two-layer wavelet decomposition of electric quantity data of the mobile robot at the 500 continuous moments including the current moment, and two groups of high-frequency component electric quantity and two groups of low-frequency component electric quantity of the last four moments are taken out of the electric quantity data;
the intelligent network-based mobile robot electric quantity prediction model comprises an extreme learning machine-based mobile robot electric quantity high-frequency prediction model and a PID neural network-based mobile robot electric quantity low-frequency prediction model; training data used in the construction of the prediction model are high-frequency component quantity and low-frequency component quantity after electric quantity data are subjected to two-layer wavelet decomposition at each moment in the whole process from starting to stopping of the mobile robot;
the mobile robot electric quantity high-frequency prediction model based on the extreme learning machine is obtained by training the extreme learning machine by taking high-frequency component electric quantity at 4 continuous moments as input data and taking high-frequency component electric quantity at the next moment as output data; the mobile robot electric quantity low-frequency prediction model based on the PID neural network is obtained by training the PID neural network by taking the low-frequency component electric quantity of 4 continuous moments as input data and taking the low-frequency component electric quantity of the next moment as output data;
sequentially inputting two groups of high-frequency component electric quantity and two groups of low-frequency component electric quantity at the last four moments into a mobile robot electric quantity high-frequency prediction model based on an extreme learning machine and a mobile robot electric quantity low-frequency prediction model based on a PID (proportion integration differentiation) neural network to obtain two groups of electric quantity high-frequency prediction values and two groups of electric quantity low-frequency prediction values, and taking the accumulated value of the two groups of electric quantity high-frequency prediction values and the two groups of electric quantity low-frequency prediction values as the electric quantity prediction value of the mobile robot at the;
if the predicted value of the electric quantity of the mobile robot at the next moment is more than 25%, the mobile robot returns along the original path of the guide rail to wait for the next transportation task;
and if the predicted value of the electric quantity at the next moment of the mobile robot is less than or equal to 25%, the mobile robot drives to a charging area of the mobile robot through the guide rail, the charging information is fed back to the remote controller, and meanwhile, the remote controller sends an instruction to the standby robot on the same guide rail to move to the initial position of the transportation task on the corresponding rail to replace the mobile robot needing to be charged to carry out the transportation task.
2. The method according to claim 1, wherein the weights and the threshold values in the extreme learning machine-based mobile robot electric quantity high-frequency prediction model are obtained by optimizing through a genetic algorithm;
step A1: initializing a population by taking genes of individual population as a weight and a threshold in a mobile robot electric quantity high-frequency prediction model based on an extreme learning machine;
the value range of the population scale is [50,200], the value range of the cross probability is [0.3,0.5], the value range of the variation probability is [0.05,0.15], the value range of the maximum iteration frequency is [100,500], and the value range of the maximum search precision is [0.005,0.1 ];
step A2: setting a fitness function and acquiring the optimal individual in the population;
substituting the weight value and the threshold value corresponding to the population individual gene into a mobile robot electric quantity high-frequency prediction model based on the extreme learning machine, calculating an output value by using the electric quantity prediction model based on the weight value and the threshold value determined by the population individual gene, and taking the reciprocal of the Mean Square Error (MSE) of the output value and an actual value as a first fitness function f1 (x);
step A3: calculating the fitness of each individual, and entering the sub-population with the maximum individual fitness of each population; individuals in the sub-population are not subjected to cross variation operation and are directly reserved to the next generation;
step A4: selecting a crossover operator and a mutation operator by adopting a roulette method;
step A5: updating individuals except the sub-population in the population by using a crossover operator and a mutation operator;
step A6: calculating the fitness of all updated individuals, judging whether the fitness reaches the maximum iteration times or the maximum search precision, if so, taking the individual with the maximum fitness as an optimal individual, and outputting the weight and the threshold of the mobile robot electric quantity high-frequency prediction model based on the extreme learning machine corresponding to the optimal individual, otherwise, returning to the step A4.
3. The method according to claim 1, wherein the weight and the threshold in the PID neural network-based low-frequency prediction model of the electric quantity of the mobile robot are obtained by optimizing through a quantum particle swarm algorithm with a self-adaptive weight;
step B1: setting the position vector of each individual in the quantum particle swarm as a weight and a threshold in a mobile robot electric quantity low-frequency prediction model based on a PID neural network, and initializing each parameter of the swarm individual into a random number of [ -11 ];
the value range of the number of the quantum particle swarm is [20,80], the value range of the number of the particles of the quantum particle swarm is [10,50], the value range of the maximum iteration number is [200, 1000], the value range of the iteration number for constructing the elite swarm is [50,200], the value range of the premature convergence judgment threshold is [0.01, 0.05], and the value range of the worst particle variation ratio delta% of the swarm is [ 2%, 6% ];
step B2: setting a fitness function, and determining an optimal individual position vector of the quantum particles;
substituting the weight and the threshold corresponding to the quantum particle individual position vector into the weight and the threshold in the PID neural network-based mobile robot electric quantity low-frequency prediction model, calculating an output value by using the PID neural network-based mobile robot electric quantity low-frequency prediction model determined by the population individual position, and taking the reciprocal of the Mean Square Error (MSE) of the output value and the actual value as a second fitness function f2 (x);
step B3: operating a premature convergence judgment mechanism;
if the group fitness variance of the quantum particle swarm is smaller than the premature convergence judgment threshold value gamma, the worst particle and the group extreme value of delta% are varied, and the leading global extreme value is updated;
step B4: judging whether to establish an elite population;
when the iteration times are larger than the iteration times of the elite population, extracting extreme values of all populations through information sharing among the populations to establish the elite population, and turning to the step B8, otherwise, turning to the step B5;
step B5: randomly updating the parameters of each population of particles;
step B6: recalculating and comparing the fitness of each particle, and if the fitness is better than the current individual extreme value, updating the individual extreme value; comparing the global extreme value, if the global extreme value is superior to the current group extreme value, updating the global extreme value, and turning to the step B2;
step B7: the elite population continues to evolve;
step B8: and judging whether the maximum iteration times are met, if so, exiting, otherwise, turning to the step B4 until a global optimal value is found, and outputting the weight and the threshold of the PID neural network-based mobile robot electric quantity low-frequency prediction model.
4. The method according to any one of claims 1 to 3, wherein the mobile robot is judged to be in fault according to landmarks with unique IDs which are arranged at equal intervals on the ground guide rail and are passed by the mobile robot, and the specific process is as follows:
the mobile robot returns the landmark information passing through the ground guide rail to the remote controller in real time, after the mobile robot returns the information passing through the landmark a, if the mobile robot does not return the information passing through the next landmark a +1 within the specified time range, the remote controller judges that the current mobile robot has a fault, and judges the position of the fault mobile robot according to the positions of the landmark a and the next landmark a + 1.
5. The method according to claim 4, wherein when the mobile robot fails on the ground track, the remote controller sends a turnout opening command to open a turnout on the ground track and sends a traction command to a standby mobile robot which is located on the same ground track as the failed mobile robot, so that the standby mobile robot moves to a location area where the failed mobile robot is located, an electromagnetic suction device of a standby mobile robot base is started to adsorb the failed mobile robot, and the failed mobile robot is pulled to an area to be maintained of the mobile robot along the ground guide rail through the turnout;
an electric control turnout is arranged between the adjacent ground guide rails, and an electric control turnout is arranged between the area to be maintained of the mobile robot and the ground guide rails;
the standby mobile robot is located in a standby robot waiting area, and the standby robot waiting area is located at a transportation task starting point and is communicated with the ground guide rail.
6. The method according to claim 4, characterized in that, after the standby mobile robot reaches the last landmark a passed by the mobile robot with fault, the vehicle-mounted binocular camera ZED of the standby mobile robot is opened, the distance between the standby mobile robot and the mobile robot with fault is measured, and the standby mobile robot is close to the mobile robot with fault according to the measured distance; when the mobile robot for executing the transportation task breaks down and is pulled to a maintenance area, the remote controller sends an instruction to the standby robot in the idle state on the same track to replace the broken robot to execute the transportation task.
7. The method of claim 6, wherein the area to be serviced of the mobile robot is in communication with a charging track, and wherein the mobile robot charging area is charged along the charging track if the backup robot in the area to be serviced of the mobile robot is less than 25%, and wherein the backup robot waiting area at the beginning of the transportation task is otherwise entered along the charging track.
8. A distributed environment robot and vehicle mobile interconnection carrying system is characterized by comprising a desktop robot, a desktop controller, a mobile robot, a guide rail and a remote controller;
the guide rails comprise desktop guide rails and ground guide rails, electric control turnouts are arranged between the adjacent ground guide rails, and landmarks with unique IDs are arranged on the ground guide rails at intervals;
the desktop controller, the mobile robot and the electric control turnout on the guide rail are all communicated with the remote controller;
the desktop robot is arranged on the workbench and controlled by the desktop controller to move along a desktop guide rail on the workbench, a fixed object taking and placing position is arranged on the workbench, a photosensitive sensor is arranged on the object taking and placing position, and the photosensitive sensor is connected with the desktop controller;
the side edge of the workbench, which is provided with a fixed object taking and placing position, is provided with a ranging sensing receiver;
the mobile robot is provided with a distance measuring sensor, is arranged on the ground and moves along a ground guide rail on the ground by adopting the method of any one of claims 1 to 7.
9. The system of claim 8, further comprising a mobile robot charging area and a standby robot waiting area;
the mobile robot charging area is arranged on a charging guide rail between a transport task starting position and a transport task ending position, the charging guide rail is communicated with a standby robot waiting area guide rail, and the standby robot waiting area guide rail is communicated with a ground guide rail.
10. The system according to claim 9, characterized by further comprising a mobile robot to-be-maintained area, wherein an electric control turnout is arranged between the mobile robot to-be-maintained area and the ground guide rail, an electromagnetic suction device and an on-vehicle binocular camera ZED are arranged on a mobile robot base, and the on-vehicle binocular ZED is arranged above the base of the robot.
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