CN109615057B - Self-organizing task allocation method based on dynamic response threshold value in foraging of swarm robots - Google Patents
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Abstract
The invention discloses a self-organizing task allocation method based on a dynamic response threshold value in foraging of swarm robots, which comprises the following steps: when a foraging task starts, all robots are gathered in a nest and are in a waiting state, when the waiting time exceeds a given time, a dynamic response threshold model is used for calculating the foraging probability, and based on the foraging probability, the robots determine whether to start foraging or not, namely, the robots are switched from the waiting state to a searching state; in the dynamic response threshold value model, traffic flow density, namely the average obstacle avoidance times of the robots in a period of time and the density of the foraging robots, is used as a dynamic change threshold value to measure the traffic condition of the movement of the robots in the environment, and the swarm robots make appropriate response to the change of the environment to generate self-organized task allocation. A dynamic response threshold model based on traffic flow density is constructed, so that the swarm robot system can generate self-organized task allocation, physical interaction among the robots is reduced, and the foraging efficiency of the swarm robots is improved.
Description
Technical Field
The disclosure relates to the technical field of intelligent robots, in particular to a self-organizing task allocation method based on a dynamic response threshold value in foraging of swarm robots.
Background
Swarm intelligence is a branch of artificial intelligence, which is inspired by social insect such as ants and bees and collective behavior of animal communities such as bird swarms and fish swarms, and is intended to simulate highly efficient distributed solutions in nature and applied to complex optimization problems. Swarm robotics is a new method for coordinately controlling a large number of robots, and is the application of swarm intelligence in a multi-robot system. The swarm robot system is a system established by simulating the behaviors of social insects or other social biological groups, and consists of a plurality of undifferentiated autonomous robots under the completely distributed control, the swarm robots can complete complex tasks which can not be completed by a single robot, and the design cost of the swarm robot system consisting of simple individuals is generally lower than that of a single robot with the same capacity. The swarm robot system mainly researches a mechanism of social interaction between robots and between the robots and the surrounding environment, and how to emerge complex swarm behaviors and swarm intelligence in the social interaction process.
The foraging task is a typical task in swarm robot research for sample collection in an unknown environment, and the robot must find and pick up samples distributed in the environment and then transport the collected samples to a designated area. The research of foraging by using swarm robots has important significance in both theoretical research and practical application. From practical application, research utilizes swarm robot to find food, can make swarm robot application prospect wider, can utilize the robot to replace people to accomplish many works, for example clear up poisonous waste, arrange thunder and arrange and explode, space exploration, survivor rescue, mine disaster search and rescue, regional sample collection etc. after the calamity such as earthquake. The robot foraging research can play an important role in the fields of national defense and military, disaster reduction and relief and the like, and can reduce casualties, such as finding and destroying military targets hidden by enemies, removing mines or mines arranged by the enemies, searching battlefields, relieving post-disaster wounded personnel and the like. Theoretically, by researching how swarm robots emerge complex self-organization behaviors by using simple behavior rules, understanding of self-organization emergence mechanism of the swarm robots can be deepened, an emergence model of the swarm robot self-organization behaviors is established, and key factors influencing evolution rules of the swarm robot self-organization behaviors are researched by using the emergence model so as to design, analyze and control the swarm robot self-organization behaviors.
In swarm robot foraging tasks, the amount of food in the environment, the number of foraging robots, and the demand of the swarm for food all affect foraging efficiency. According to environmental changes and the demand of colonies, social insects can realize self-organized task distribution by adjusting the number of foragers to achieve a good foraging effect. Therefore, in the foraging task of the swarm robot, how to distribute the self-organized task to improve the foraging efficiency is an important problem in the foraging research of the swarm robot. In a multi-robot system, the most intuitive approach is to use centralized task allocation, i.e. the master controller collects the required data, such as the position of the robot, the data of the sensors, etc., and then allocates the tasks to the appropriate robots by calculation. Centralized task allocation methods are often difficult to obtain accurate information in large areas, such as remote outdoor environments and unstructured environments. As the number of swarm robots increases, the complexity of the centralized controller increases, limiting the use of centralized task allocation methods. In distributed task allocation, swarm robot systems most widely use a task allocation method based on a fixed response threshold model.
However, the swarm robot system with a fixed response threshold cannot respond properly to changes in the environment, and is less robust. In addition, the existing task allocation methods do not consider the physical interaction (obstacle avoidance) among the robots, and the efficiency of foraging of swarm robots is greatly reduced due to more physical interaction among the robots in the swarm robots.
Therefore, under what conditions the robot starts to execute the foraging task to meet the task requirement and enable the swarm robot system to have high foraging efficiency, the technical problem to be solved by the technical scheme of the application.
Disclosure of Invention
In order to solve the defects of the prior art, the implementation example of the disclosure provides a self-organization task allocation method based on a dynamic response threshold value in foraging of swarm robots, and a dynamic response threshold value model is used for calculating the foraging probability of the robots, so that the self-organization task allocation of the swarm robots is realized.
In order to achieve the purpose, the following technical scheme is adopted in the application:
a self-organizing task allocation method based on a dynamic response threshold value in foraging of swarm robots comprises the following steps:
when a foraging task starts, all robots are gathered in the nest to be in a waiting state, when the waiting time exceeds a given time, a dynamic response threshold model is used for calculating a foraging probability, and based on the foraging probability, the robots use a roulette selection method to determine whether to start foraging or not, namely, the robots are switched from the waiting state to a searching state;
in the dynamic response threshold value model, the traffic flow density, namely the average obstacle avoidance times of the robots in a period of time and the density of foraging robots in the environment are used for quantitatively measuring the traffic conditions of the movement of the robots in the environment, and the traffic flow density is used as a dynamically changing threshold value (theta) so that swarm robots can make appropriate response to the change of the environment to generate self-organized task allocation.
In the further technical scheme, in the searching state, when the robot finds food, the robot is switched to the returning state, and if the robot does not find food in the given searching time, the robot is also switched to the returning state;
in the returning state, after the robot reaches the nest and places food, the robot is switched to the waiting state;
if the robot finds food in the return state, it grabs the food and enters the return state.
According to the further technical scheme, a dynamic response threshold model is used for calculating the foraging probability, wherein the dynamic response threshold model is as follows:
wherein S (t) is an external stimulus value, theta is a dynamically-changing threshold value, n determines the slope of a probability function, different n values can enable the robots to respond differently for the same external stimulus value and traffic flow density, and the n value of each robot is an integer randomly generated in a given interval in the foraging task of the swarm robots.
In a further aspect, S (t) is used to measure the difference between the amount of food in the nest and the desired amount, defined as:
S(t)＝F d -F(t)
wherein F (t) represents the amount of food in the nest at time t, F d Is the desired amount of food that needs to be maintained in the nest.
The further technical proposal takes the traffic flow density as a threshold value of dynamic change and the traffic flow density T f Is defined as:
T f ＝γ*k+η*M A
wherein k is the foraging robot density in the environment, M A Representing a time period T 3 The average obstacle avoidance times, gamma and eta, are adjustment factors.
According to a further technical scheme, the density of the foraging robot in the environment is defined as:
wherein L and L represent the area of the foraging region, N f The number of the foraging robots is represented, k represents the number of the foraging robots in a unit area, and the number of the robots can be used for measuring traffic jam conditions.
A further technical scheme is that a period of time T is used 3 Average obstacle avoidance times M of inner single robot A To estimate traffic congestion conditions:
wherein,
represents a time period T 3 Total number of obstacle avoidance times, N T Representing the total number of robots in the foraging task.
According to the further technical scheme, the robot controls the motion of the robot by using an attractor selection model in a searching state and a returning state, and the attractor selection model is defined as follows:
wherein x represents the pose of the robot,
representing the motion of the robot, f (x) is an attractor related to a specific motion, A (t) represents the fitting degree of the current state of the robot and the environment, A (t) = {0,1}, and epsilon represents system noise.
According to the further technical scheme, when the robot starts to search for food, A (t) =0, epsilon controls the motion of the robot, and the robot searches for the food by walking randomly;
after the robot finds food, A (t) =1,f (x) × A (t) controls the motion of the robot, the robot moves straight to approach the food, after the food is grabbed, the robot enters a return state, at the moment, A (t) =0, epsilon controls the motion of the robot, and the robot searches for a light source through random walking;
when the robot detects light by using the light sensor, A (t) =1,f (x) × A (t) controls the motion of the robot, the robot approaches to the nest by tracking the light source, and after the food is put down, the robot enters a waiting state.
According to the further technical scheme, if the robot finds food in the return state, the robot grabs the food and enters the return state.
The embodiment of the disclosure also discloses a self-organizing task allocation system based on the dynamic response threshold value in foraging of the swarm robot, which comprises a control center, wherein the control center is in local communication with the robot in the nest through a communication module, and the motion state of the swarm robot is controlled by using the self-organizing task allocation method based on the dynamic response threshold value in foraging of the swarm robot.
Embodiments of the present disclosure also disclose a swarm robot configured to move in accordance with a dynamic response threshold based self-organizing task allocation method in foraging by the swarm robot.
Compared with the prior art, the beneficial effect of this disclosure is:
the technical scheme of the disclosure uses the control strategy based on the attractor selection model to enable the swarm robot system to have better robustness, and constructs the dynamic response threshold model based on the traffic flow density to enable the swarm robot system to generate self-organized task distribution, thereby reducing the physical interaction among the robots and improving the foraging efficiency of the swarm robot.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a schematic diagram of a state transition of foraging behavior of a robot in accordance with one or more embodiments of the present disclosure;
fig. 2 is a flow diagram of a robotic foraging process in accordance with one or more embodiments of the present disclosure;
FIG. 3 is a graph of a robot foraging probability function of one or more embodiments of the present disclosure;
FIG. 4 is a swarm robot foraging task waiting state simulation experiment at an initial time of one or more embodiments of the disclosure;
fig. 5 is a swarm robot foraging task search state simulation experiment at an initial time of one or more embodiments of the disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In a typical embodiment of the present application, a robot searches and finds objects (food) distributed in the environment, and then transports the found food to a designated area (nest). In the foraging process, all robots use the same behavior rule, and as shown in fig. 1, the foraging behavior of the robots can be divided into the following states:
a waiting state: at the beginning of the foraging task, all robots are gathered in the nest in a waiting state. Each robot initializes a timer t 1 ＝0，t 1 It represents the waiting time of the robot in the nest and sets a maximum waiting time T 1 . After the waiting time exceeds a given time (t) 1 ≥T 1 ) The robot calculates the foraging probability using a dynamic response threshold model, and based on the foraging probability, the robot determines whether to start foraging using a roulette selection method, i.e., switches from a wait state to a search state. The number and the individuals of the foraging robots are randomly determined by self-organization through a task allocation method based on a dynamic response threshold model.
And (3) searching the state: the process in which the robot searches for food in the environment is called the search state, each robot initializing a timer t 2 ＝0，t 2 Indicates the search time of the robot in the environment and sets a maximum search time T 2 . In the search state, the behavior of the robot is controlled based on the control strategy of the attractor selection model. When the robot finds the food, the robot is switched to a return state; to avoid that the robot is always in a searching state, when the robot is in a given searching time T 2 Food is not found, and the robot is switched to a return state.
And returning to the state: in the return state, the behavior of the robot is controlled based on the control strategy of the attractor selection model. After the robot reaches the nest and places food, the robot switches to a waiting state. If the robot finds food in the return state, it grabs the food and enters the return state.
In the foraging process, the robot completes the foraging task of the swarm robot by switching between different states, and the foraging flow of the robot is shown in fig. 2.
In a specific implementation example, in the foraging process of the robot, the attractor selection model is a simple, robust and easy-to-implement method inspired by the adaptive behavior of biomolecules. Control strategies based on attractor selection models can increase the performance of robot navigation compared to random walk methods. Therefore, the disclosed embodiments use an attractor selection model to control the motion of the robot, defined as follows:
wherein, x represents the pose of the robot,
representing the motion of the robot. f (x) is an attractor (food or light source) associated with a particular motion. A (t) represents the fitting degree of the current state of the robot to the environment, and a (t) = {0,1}. ε represents the system noise.
When the robot starts to search for food (the food can be garbage, mineral substances, resources, toxic and harmful objects in the environment, people waiting for rescue and the like), A (t) =0, epsilon controls the motion of the robot, and the robot searches for the food by random walking. After the robot finds the food, a (t) =1,f (x) × a (t) controls the motion of the robot, and the robot moves straight close to the food. After food is grabbed, the robot enters a return state, A (t) =0, epsilon controls the motion of the robot, and the robot searches for a light source through random walking. When the robot detects light using the light sensor, a (t) =1,f (x) × a (t) controls the motion of the robot, which approaches the nest by tracking the light source. After putting down the food, the robot enters a waiting state.
In a specific implementation example, when the foraging probability of the robots is calculated, the traffic congestion condition in the environment needs to be considered, the foraging robot density in the environment and the physical interaction between the robots influence the foraging efficiency of the swarm robots, so that the disclosed implementation example provides a concept of traffic flow density in order to quantitatively measure the traffic condition in the environment. The traffic flow density is composed of two parts: the density of the foraging robot in the environment and the average obstacle avoidance times of the robot in a period of time.
Specifically, the density of foraging robots in the environment is defined as:
wherein L and L represent the area of the foraging region, N f Indicating the number of foraging robots. k represents the density of the foraging robots in the environment, namely the number of the foraging robots in a unit area, and the number of the robots can be used for measuring the traffic jam condition.
The robots may be concentrated in some areas to cause congestion in local environment, and the size of the robots may also affect traffic conditions, because the traffic conditions in the environment have instantaneity: the traffic is relatively congested in one period of time and is relatively smooth in the other period of time, so that a time value T is selected in the method 3 As a time period and for a period of time T 3 Average obstacle avoidance number M of inner robots A To estimate traffic congestion conditions:
In summary, the traffic flow density (T) f ) Is defined as:
T f ＝γ*k+η*M A
wherein k is the foraging robot density in the environment, M A Represents a time period T 3 Average number of obstacle avoidance within. γ and η are regulatory factors.
In the embodiment of the present disclosure, the task allocation scenario to be handled is as follows: the quantity of food in the nest needs to be maintained at the desired value F d And the food quantity in the environment is constant, and the swarm robot system adjusts the foraging state of the robot through task allocation. In order to be in swarm robot systemImplementing self-organized task allocation, an implementation example of the present disclosure constructs a dynamic response threshold model. The model converts the traffic flow density T f As a dynamically changing threshold θ, so that the robot can respond appropriately to changes in the external environment. In the dynamic response threshold model, the difference between the amount of food in the nest and the expected amount is measured using an external stimulus S (t), which is defined as:
S(t)＝F d -F(t)
wherein F (t) represents the amount of food in the nest at time t, F d Is the desired amount of food that needs to be maintained in the nest. The larger the stimulation value S (t), the larger the food gap; smaller S (t) indicates smaller food gap.
The probability that the robot leaves the nest to perform the foraging task is as follows:
where n determines the slope of the probability function. In the probability function, the food information and the environment information in the nest jointly determine the probability of foraging of the robot, so that the robot can complete foraging tasks and reduce physical interaction among the robots.
The purpose of the swarm robot foraging task is to maintain the quantity of food in the nest at a desired level, and the swarm robot system, the motion of the robot, and the physical interaction between the robots all consume the energy of the food in the nest, so that the robot needs to allocate different quantities of robots to perform the foraging task according to the quantity of the food in the nest and the change of the environment. As shown in fig. 1, the process of switching the robot from the waiting state to the searching state is task allocation, and too few robots simultaneously execute foraging tasks, which cannot meet the consumption of the system; too many robots simultaneously perform foraging tasks, and although more food can be found, more energy is consumed by the movement of the robots and more physical interaction between the robots, thereby reducing foraging efficiency. The problem to be solved by task allocation in the invention is that under what conditions, the robot starts to execute the foraging task to meet the task requirement and enable the swarm robot system to have higher foraging efficiency.
According to the technical scheme, the dynamic response threshold model is used for calculating the foraging probability of the robots, so that the self-organization task allocation of swarm robots is realized. In the dynamic response threshold model, a single robot uses an external stimulus S (t), i.e., the difference between the number of food in the nest and the expected value, and a dynamically changing threshold θ, i.e., the traffic flow density, to jointly affect the probability that the robot starts to forage, as shown in equation (1).
The self-organizing task allocation means that the swarm robot system does not need a centralized controller, tasks do not need to be allocated to each robot, and the individual robots determine whether to start foraging or not through self-organization of the calculated foraging probability (the foraging probability of each robot is different due to the difference of the n values of the robots), so that the task allocation effect is generated. When two robots have the same probability of foraging, foraging may not begin at the same time.
In the embodiment of the disclosure, fig. 3 is a function diagram of the foraging probability of the robot, and as can be seen from fig. 3, the threshold θ affects the foraging probability of the robot, and the robot makes a motion judgment according to the foraging probability. When the external stimulus S (t) is equal to the threshold θ, the probability of foraging by the robot is 0.5. When the threshold value theta is fixed, when the external stimulus S (t) is larger, namely the difference between the quantity of the food in the nest and the expected value is larger, more robots start to execute a foraging task; when the external stimulus S (t) is small, i.e., the amount of food in the nest differs little from the desired value, fewer robots begin to perform the foraging task. When the external stimulus S (t) is fixed, when the threshold value theta is larger, namely the traffic condition in the environment is poorer, more robots in the environment execute foraging tasks at the same time, and the system distributes fewer robots to start to execute the foraging tasks, so that the mutual interaction among the robots is reduced; when the threshold value theta is small, namely the traffic condition in the environment is good, at the moment, fewer robots in the environment execute the foraging task at the same time, and the system distributes more robots to start to execute the foraging task, so that the foraging efficiency is improved. The swarm robot system can realize self-organized task distribution and improve the foraging efficiency of the swarm robots by using a dynamic response threshold model. The embodiment of the disclosure also discloses a self-organizing task allocation system based on the dynamic response threshold value in foraging of the swarm robot, which comprises a control center, wherein the control center is in local communication with the robot in the nest through a communication module, and the motion state of the swarm robot is controlled by using the self-organizing task allocation method based on the dynamic response threshold value in foraging of the swarm robot.
Embodiments of the present disclosure also disclose a swarm robot configured to move in accordance with a dynamic response threshold based self-organizing task allocation method in foraging by the swarm robot.
In order to explain the implementation method of swarm robot task allocation based on a dynamic response threshold model, a simulation experiment is carried out on a mobile robot environment modeling and exploring software platform.
As shown in fig. 4, the gray circular area in the working space represents the nest, the cylindrical object in the nest is the robot used in the experiment, the smaller object in the environment is food, the robot starts to search for food from the nest, the food is randomly distributed in the whole search space, the spherical object in the middle of the nest is the light source, and the light intensity sensor is used to detect the direction of the nest when the robot returns to the nest. At the beginning of the experiment, the robots are all in a waiting state in the nest when waiting for a time t 1 Greater than a given time T 1 The robot then calculates the foraging probability using a dynamic response threshold model, and the robot then determines whether to begin foraging using a roulette selection method.
As shown in fig. 5, the robot randomly searches in the environment (the larger object is the robot and the smaller object is the food), and when the robot finds the food, the robot switches to the return state. The motion of the robot in the search state and the return state is controlled by an attractor selection model-based motion strategy. The robot detects the direction of the nest using the optical sensor, and after the robot returns to the nest and puts down the food, the robot switches to a waiting state.
The robot periodically counts the number of physical interactions in both the search state and the return state, and then is in a waiting stateThe robot calculates the traffic flow density T by using the physical interaction times and the number of foraging robots f I.e. the threshold value theta. And when the waiting time of the robot exceeds the given time, the robot calculates the foraging probability by using the dynamic response threshold model, and determines whether to start foraging according to the probability value.
The technical scheme disclosed by the invention is the self-organizing task allocation method based on the dynamic response threshold model, which can effectively improve foraging efficiency, reduce physical interaction among robots and has stronger robustness. The method and the system have the advantages that the attractor selection model is used for controlling the motion of the robot, so that the robot has better robustness to the change of the environment; the method uses traffic flow density, namely the average obstacle avoidance times of the robot and the density of the foraging robot in a period of time as a dynamically-changed threshold value to measure the traffic condition of the movement of the robot in the environment; the present disclosure constructs a dynamic response threshold model that not only allows the swarm robot system to respond appropriately to changes in the environment but also allows the swarm robots to create self-organized task assignments.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (9)
1. A self-organizing task allocation method based on a dynamic response threshold value in foraging of swarm robots is characterized by comprising the following steps:
when a foraging task starts, all robots are gathered in the nest to be in a waiting state, when the waiting time exceeds a given time, a dynamic response threshold model is used for calculating a foraging probability, and based on the foraging probability, the robots use a roulette selection method to determine whether to start foraging or not, namely, the robots are switched from the waiting state to a searching state;
calculating a foraging probability using a dynamic response threshold model, wherein the dynamic response threshold model is:
in the formula, S (t) is an external stimulus value, theta is a dynamically-changing threshold value, n determines the slope of a probability function, for the same external stimulus value and traffic flow density, different n values can enable the robots to respond differently, and in the swarm robot foraging task, the n value of each robot is an integer randomly generated in a given interval;
in the dynamic response threshold value model, the traffic flow density, namely the average obstacle avoidance times of the robots in a period of time and the density of foraging robots in the environment are used for quantitatively measuring the traffic conditions of the movement of the robots in the environment, and the traffic flow density is used as a dynamically changing threshold value (theta) so that swarm robots can make appropriate response to the change of the environment to generate self-organized task allocation.
2. The method for assigning self-organizing tasks based on dynamic response thresholds in foraging by swarm robots of claim 1, wherein in the search state, when a robot finds a food, the robot switches to the return state, and if no food is found within a given search time, the robot also switches to the return state;
after the robot in the return state reaches the nest and places food, the robot is switched to a waiting state;
if the robot finds food in the return state, it grabs the food and enters the return state.
3. The method for assigning self-organizing tasks in foraging by swarm robots based on dynamic response thresholds according to claim 1, wherein S (t) is used to measure the difference between the number of food in the nest and the expected number, defined as:
S(t)＝F d -F(t)
wherein F (t) represents the quantity of food in the nest at time t, F d Is the desired amount of food to be maintained in the nest;
using the traffic flow density as the dynamically changing threshold value and crossingThrough-flow density T f Is defined as:
T f ＝γ*k+η*M A
wherein k is the foraging robot density in the environment, M A Represents a time period T 3 The average obstacle avoidance times, gamma and eta, are adjustment factors.
4. The method for assigning self-organizing tasks based on dynamic response thresholds in foraging by swarm robots of claim 3, wherein a period of time T is used 3 Average obstacle avoidance times M of inner single robot A To estimate traffic congestion conditions:
wherein,
representing a time period T 3 Total number of obstacle avoidance times, N T Representing the total number of robots in the foraging task.
5. The method for assigning self-organizing tasks in foraging by swarm robots based on dynamic response thresholds according to claim 3, wherein the density of foraging robots in the environment is defined as:
wherein L and L represent the area of the foraging region, N f The number of the foraging robots is represented, k represents the density of the foraging robots, namely the number of the foraging robots in a unit area, and the number of the robots can be used for measuring traffic jam conditions.
6. The swarm robot foraging-based self-organizing task allocation method based on dynamic response threshold value of claim 2, wherein the robot uses the attractor selection model to control the robot's motion in the search state and the return state, which is defined as follows:
wherein x represents the pose of the robot,
representing the motion of the robot, f (x) is an attractor related to a specific motion, A (t) represents the fitting degree of the current state of the robot and the environment, A (t) = {0,1}, and epsilon represents system noise.
7. The method for assigning self-organizing tasks based on dynamic response thresholds in foraging by swarm robots of claim 6, wherein when a robot starts to search for food, A (t) =0, epsilon controls the motion of the robot, and the robot searches for food by random walking;
after the robot finds food, A (t) =1,f (x) × A (t) controls the motion of the robot, the robot moves straight to approach the food, after the food is grabbed, the robot enters a return state, at the moment, A (t) =0, epsilon controls the motion of the robot, and the robot searches for a light source through random walking;
when the robot detects light by using the light sensor, A (t) =1,f (x) × A (t) controls the motion of the robot, the robot approaches the nest through the tracking light source, and after the food is put down, the robot enters a waiting state.
8. The self-organizing task allocation system based on the dynamic response threshold value in foraging of the swarm robots is characterized by comprising a control center, wherein the control center is in local communication with the robots in the nests through a communication module, and the motion state of the swarm robots is controlled by the self-organizing task allocation method based on the dynamic response threshold value in foraging of the swarm robots according to any one of claims 1 to 7.
9. A swarm robot configured to move in accordance with the swarm robot foraging dynamic response threshold based self-organizing task allocation method of any of claims 1-7.
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