A heterogeneous mobile agent cooperative control and evolution method

Abstract

The application discloses a heterogeneous mobile intelligent agent cooperative control and evolution method, which comprises embedding an emotion cognition module in an artificial mobile intelligent agent, acquiring emotion clues of different granularities to construct evolution rules of the artificial mobile intelligent agent, including acceleration rules, deceleration rules, random slowing rules of introducing a random slowing probability function and position updating rules; constructing automatic mobile intelligent agent evolution rules based on an improved interaction potential energy field function, including acceleration rules, deceleration rules and position updating rules; the acceleration rules are used for adjusting speed; the deceleration rules are used for avoiding collision; the random slowing rules are used for random deceleration of the artificial mobile intelligent agent; and the position updating rules are used for determining position; and the motion path of the heterogeneous mobile intelligent agent is controlled and planned through a field force planning method. The application solves the cooperative control problem of the heterogeneous mobile intelligent agent in a complex dynamic environment, enhances the cooperative ability between the heterogeneous intelligent agents and improves collision avoidance safety.

Description

Technical Field

[0001] The present invention relates to a method for cooperative control and evolution of heterogeneous mobile intelligent bodies, belonging to the technical field of formation and cooperative control of mobile intelligent bodies. Background Art

[0002] The concept of a mobile agent system is to integrate the operational characteristics of both artificial and automated mobile agents when achieving desired formation motion, aiming to achieve integrated collaborative control and formation management. Due to its widespread application in fields such as traffic flow control, aerial vehicle formations, and space exploration, mobile agent systems have garnered significant attention. However, most previous studies have failed to fully consider the impact of emotional factors of artificial mobile agents and environmental uncertainty on the operation of multi-agent collaborative control systems. In practical applications, individual emotional factors and environmental uncertainty can not only cause individual agents to deviate from their desired motion paths but can also adversely affect the coordinated operation of multi-agent formations. Therefore, research on multi-agent collaborative control in uncertain environments is of great theoretical and practical value.

[0003] The information disclosed in this background section is only intended to enhance understanding of the overall background of the invention and should not be considered as an admission or any form of suggestion that the information constitutes the prior art already known to a person of ordinary skill in the art. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that the existing technology does not fully consider the individual emotional factors of artificial mobile agents and the impact of uncertain factors in the local environment on the collaborative control of multiple agents, resulting in the collaborative control and evolution of heterogeneous mobile agent systems being unable to approach the real situation.

[0005] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0006] The present invention provides a method for collaborative control and evolution of heterogeneous mobile agents, comprising:

[0007] Using continuous cellular automata to mix artificial mobile agent cells and automatic mobile agent cells in a preset ratio, and generate artificial mobile agents and automatic mobile agents through an iterative process;

[0008] An emotion recognition module is embedded in the artificial mobile agent to obtain emotion clues of different granularities based on fuzzy theory;

[0009] According to the emotional clues of different granularities, an evolution rule of the artificial mobile agent is constructed to update the state of the artificial mobile agent;

[0010] The evolution rules of the artificial mobile agent include an acceleration rule, a deceleration rule, a random slowing-down rule introducing a random slowing-down probability function, and a position update rule;

[0011] Obtain the position, velocity, and acceleration of the autonomous mobile agent, construct the autonomous mobile agent evolution rules based on the improved interaction potential energy field function, and update the state of the autonomous mobile agent;

[0012] The automatic mobile agent evolution rules include acceleration rules, deceleration rules and position update rules;

[0013] The acceleration rule is used to adjust the speed of the automatic mobile agent or the artificial mobile agent;

[0014] The deceleration rule is used to avoid collisions between the autonomous mobile agent and the artificial mobile agent;

[0015] The random slowing-down rule that introduces the random slowing-down probability function is used to randomly slow down the artificial mobile agent based on the emotion influence factor and the random slowing-down probability function;

[0016] The position update rule is used to determine the position of the automatic mobile agent or the artificial mobile agent;

[0017] The movement and movement path of the automatic mobile agent are controlled by the field force planning method, and the movement path of the artificial mobile agent is planned by utilizing the perception information sharing mechanism of the automatic mobile agent, combining the movement and movement path of the automatic mobile agent and the emotional clues of different granularities in the operation scenario of the artificial mobile agent.

[0018] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, the emotion recognition module includes an emotion perception layer and a feature fusion network. The emotion perception layer adopts a dual-channel fusion attention mechanism network structure. Based on fuzzy theory, the methods for obtaining emotion clues of different granularities include:

[0019] Utilizing the emotion perception layer to capture and extract character emotion clues and scene emotion clues in the running scene through the character channel and the scene channel respectively to form a preliminary feature vector;

[0020] The emotion values ​​of the continuous dimensions in the preliminary feature vector are quantified using fuzzy theory to obtain the emotion impact factors.

[0021] The emotion influencing factors are input into the feature fusion network for fusion processing to obtain emotion clues of different granularities.

[0022] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, the method of quantifying the emotion values ​​of the continuous dimensions in the preliminary feature vector using fuzzy theory to obtain the emotion influencing factors includes:

[0023] Based on the Mamdani model, the emotional pleasure, emotional arousal and emotional dominance are taken as input variables, and the emotional influencing factor is taken as the output variable to construct a three-value input and single-value output emotional fuzzy reasoning model.

[0024] According to the emotion measurement values ​​of emotional pleasure, emotional arousal and emotional dominance, input variable fuzzy sets are constructed respectively, including emotional pleasure fuzzy set 1, emotional pleasure fuzzy set 2 and emotional pleasure fuzzy set 3, emotional arousal fuzzy set 1, emotional arousal fuzzy set 2 and emotional arousal fuzzy set 3, and emotional dominance fuzzy set 1, emotional dominance fuzzy set 2 and emotional dominance fuzzy set 3;

[0025] Constructing output variable fuzzy sets, including emotion factor fuzzy set 1, emotion factor fuzzy set 2 and emotion factor fuzzy set 3;

[0026] Using Gaussian membership function, the fuzzy sets of input variables and output variables are fuzzified;

[0027] Analyze the data samples of emotional pleasure, emotional arousal, and emotional dominance in the Emotic dataset to obtain an artificial experience rule library that can reflect the relationship between input variables and output variables;

[0028] According to the artificial experience rule base, the emotion representation fuzzy rule matrix is ​​constructed;

[0029] The continuous sentiment values ​​in the preliminary feature vector are converted into membership of the input set fuzzy set through fuzzification;

[0030] According to the emotion representation fuzzy rule matrix, the fuzzy reasoner is used to map the input fuzzy set to the output fuzzy set, and the calculation is performed to obtain the final output fuzzy set;

[0031] The final output fuzzy set is defuzzified using the centroid method to obtain the emotion impact factor.

[0032] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, an emotion representation fuzzy rule matrix is ​​constructed according to the artificial experience rule base. The constructed emotion representation fuzzy rule matrix is ​​expressed as:

[0033] (1);

[0034] Among them, the first three columns of the emotion representation fuzzy rule matrix represent the fuzzy set indexes corresponding to the input variables of emotional pleasure, emotional arousal, and emotional dominance, respectively; the fourth column represents the fuzzy set index corresponding to the output variable of emotional influence factor; the fifth column represents the rule weight of the emotion representation fuzzy rule matrix; the sixth column represents the connectives of the rules of the emotion representation fuzzy rule matrix; when the rule weight is 1, the logical connective is "and", and when the rule weight is 2, the logical connective is "or";

[0035] The rules in the first row of the emotion representation fuzzy rule matrix are: when the input emotion pleasure belongs to the emotion pleasure fuzzy set 1, the input emotion arousal belongs to the emotion arousal fuzzy set 3, and the input emotion dominance belongs to the emotion dominance fuzzy set 3, the output emotion factor fuzzy set 1, the rule weight in the first row is 1, and the logical connective is "and";

[0036] The rule in the second row of the fuzzy rule matrix is: when the input emotional pleasure belongs to the emotional pleasure fuzzy set 2, the input emotional arousal belongs to the emotional arousal fuzzy set 1, and the input emotional dominance belongs to the emotional dominance fuzzy set 3, the output emotional factor fuzzy set 2, the rule weight in the second row is 1, and the logical connective is "and";

[0037] The rule in the third row of the fuzzy rule matrix is: when the input emotional pleasantness belongs to the emotional pleasantness fuzzy set one, the input emotional arousal belongs to the emotional arousal fuzzy set two, and the input emotional dominance belongs to the emotional dominance fuzzy set one, the output emotional factor fuzzy set three. The rule weight in the third row is 1, and the logical connective is "and".

[0038] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, when the operation scenario is road traffic flow, the continuous cellular automaton uses vehicles as cells and generates manually driven vehicles and automatically connected vehicles through an iterative process. The state information of the continuous cellular automaton , expressed as:

[0039] (2);

[0040] in, 、 and Respectively represent this vehicle The position, velocity and acceleration at the current time t, Indicates this vehicle The ideal state vector of the vehicle, Indicates this vehicle The driver's emotional state vector, 、 、 Respectively represent this vehicle The expected speed of the vehicle, the vehicle The expected following distance of the vehicle and the vehicle Vehicle safety time interval, 、 and Respectively indicate at the current moment This vehicle Measures of drivers' emotional pleasure, emotional arousal, and emotional dominance.

[0041] The continuous cellular automaton uses vehicles as cells and generates manually driven vehicles and automatically connected vehicles through an iterative process. Expressed as:

[0042] (3);

[0043] in, Indicates the maximum acceleration of the vehicle, represents the vehicle acceleration index, and Respectively indicate the front vehicle and this car The relative distance and relative speed, Indicates the absolute value of the vehicle's comfortable deceleration. Indicates the static safety distance, Indicates the vehicle ahead The velocity at the current time t.

[0044] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, the centroid method is used to defuzzify the final output fuzzy set to obtain the emotion influence factor. The obtained emotion influence factor k is expressed as:

[0045] (4);

[0046] in, 、 and Respectively indicate at the current moment This vehicle Measures of driver emotional pleasure, emotional arousal, and emotional dominance.

[0047] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, the evolution rules of the artificial mobile agent include acceleration rules, deceleration rules, random slowing-down rules that introduce random slowing-down probability functions, and position update rules. The acceleration rule of the artificial mobile agent is expressed as:

[0048] (5);

[0049] in, Indicates this vehicle The velocity at the current time t, Indicates this vehicle The velocity at the current time t, Indicates this vehicle The acceleration at the current time t, Indicates the maximum speed limit for the road.

[0050] The deceleration rule of the artificial mobile agent is expressed as:

[0051] (6);

[0052] in, Indicates safe following distance. Indicates the vehicle ahead and this car The relative distance, Indicates the stationary safety distance.

[0053] Based on this car Driver's emotional impact factor and random slowdown probability function, the vehicle The driver has The probability of random deceleration is, where represents the random slowing probability of vehicle n. The random slowing rule with the random slowing probability function can be expressed as:

[0054] (7);

[0055] in, Indicates this vehicle The velocity at the current time t, Indicates this vehicle The velocity at the current time t, Indicates the deceleration value;

[0056] The position of vehicle n at the current time t and speed Together determine the position of vehicle n at the next moment t+1 ;

[0057] The expression of the position update rule of the artificial mobile agent is:

[0058] (8);

[0059] in, Indicates that the vehicle The position at the next moment (t+1), Indicates this vehicle At the current time t, Indicates this vehicle The velocity at the current time t.

[0060] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, the evolution rules of the automatic mobile agent include acceleration rules, deceleration rules and position update rules;

[0061] Among them, the acceleration rule of the automatic mobile agent is expressed as:

[0062] (9);

[0063] in, Indicates this vehicle The velocity at the current time t, Indicates this vehicle The velocity at the current time t, Indicates the maximum speed limit of the road. The vehicle is calculated based on the double integral motion equation The acceleration of Indicates this vehicle quality, represents the safe following distance, and F represents the total virtual force.

[0064] The deceleration rule of the autonomous mobile agent is: when the repulsive force generated by the virtual potential field of the preceding vehicle n+1 is greater than the attractive force, the total virtual force on vehicle n is expressed as a repulsive force;

[0065] The position update rules of the automatic mobile agent are consistent with those of the artificial mobile agent.

[0066] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, a method for controlling the motion and motion path of an autonomous mobile agent through a field force planning method includes:

[0067] The position, velocity and acceleration of the autonomous mobile agent are acquired in real time through the autonomous mobile agent's sensors, and the interaction potential energy field function is defined;

[0068] The position virtual force, velocity virtual force and acceleration virtual force are introduced to improve the interaction potential energy field function;

[0069] According to the improved interaction potential energy field function, the virtual forces exerted on each autonomous mobile agent from other agents are calculated, including position virtual force, velocity virtual force and acceleration virtual force;

[0070] Perform vector synthesis of all virtual forces on each autonomous mobile agent to obtain the total virtual force of each autonomous mobile agent;

[0071] Using the total virtual force as a control signal, the speed and direction of the autonomous mobile agent are adjusted through the control algorithm.

[0072] Use the potential energy field virtual force to plan the driving path of the autonomous mobile agent.

[0073] Based on the aforementioned heterogeneous mobile agent collaborative control and evolution method, the position virtual force includes position virtual attraction and position virtual repulsion, the velocity virtual force includes velocity virtual attraction and velocity virtual repulsion, and the acceleration virtual force includes acceleration virtual attraction and acceleration virtual repulsion;

[0074] The virtual repulsion of the position Expressed as:

[0075] (10);

[0076] in, represents the potential energy coefficient of the virtual repulsive force at the position, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the distance vector from the autonomous connected vehicle to the target point, || || represents the modulus of the distance vector from the autonomous connected vehicle to the target point.

[0077] The virtual gravity of the position Expressed as:

[0078] (11);

[0079] in, represents the virtual gravitational potential energy coefficient of the position, Indicates the distance from the autonomous connected vehicle to the target point.

[0080] The velocity virtual repulsion Expressed as:

[0081] (12);

[0082] in, represents the velocity virtual repulsive potential energy coefficient, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the speed difference vector between the preceding vehicle and the vehicle itself, || || represents the modulus of the speed difference vector between the preceding vehicle and the vehicle itself.

[0083] The velocity is virtual gravity Expressed as:

[0084] (13);

[0085] in, represents the velocity virtual gravitational potential energy coefficient, Indicates the speed difference between the desired speed and the current speed, represents the distance from the autonomous networked vehicle to the target point, tanh represents the hyperbolic tangent function, || || represents the modulus of the speed difference vector between the desired speed and the current speed.

[0086] The acceleration virtual repulsion Expressed as:

[0087] (14);

[0088] in, represents the acceleration virtual repulsive potential energy coefficient, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the acceleration difference vector between the preceding vehicle and the vehicle itself, || || represents the modulus of the acceleration difference vector between the preceding vehicle and the vehicle itself.

[0089] The acceleration is virtual gravity Expressed as:

[0090] ; (15);

[0091] in, represents the acceleration virtual gravitational potential energy coefficient, Indicates the difference between the expected acceleration and the current acceleration. represents the distance from the autonomous connected vehicle to the target point, || || represents the modulus of the difference vector between the expected acceleration and the current acceleration.

[0092] By introducing emotion influencing factors, the present invention accurately quantifies the continuous dimension emotion values ​​and integrates them into the random slowing-down probability function, thereby improving the authenticity and accuracy of the collaborative control of heterogeneous mobile agents and making the behavior of artificial mobile agents closer to reality.

[0093] At the same time, the method based on potential energy field virtual force converts the position, velocity and acceleration information of the autonomous mobile agent into virtual field force, effectively controls its movement through field force planning, and enhances the autonomous decision-making and path planning capabilities of the autonomous mobile agent.

[0094] This invention addresses the challenge of collaborative control in complex environments, and significantly improves the adaptability and overall intelligence of collaborative control of heterogeneous mobile agents through emotion recognition, deceleration rules, and potential field optimization.

[0095] Compared with the prior art, the present invention has the following beneficial effects:

[0096] The present invention embeds an emotion recognition module in an artificial mobile agent and uses fuzzy theory to process emotion clues of different granularities, so that the agent can perceive and understand emotional information in complex environments. The evolution rules constructed based on emotion clues of different granularities make the behavior of the artificial mobile agent more flexible and changeable. In particular, the introduction of random slowing-down probability function and emotion influencing factor can dynamically adjust the movement speed according to the emotional state, which not only increases the diversity of the agent's behavior, but also improves the agent's survivability and adaptability in complex environments.

[0097] At the same time, the present invention effectively achieves dynamic collision avoidance and coordinated motion between agents by modeling virtual repulsive fields and virtual gravitational fields, and constructing the evolution rules of autonomous mobile agents based on improved interaction potential energy field functions. The present invention also uses field force planning methods to control the motion and path of autonomous mobile agents, and combines the perception information sharing mechanism and emotional cues to plan a more reasonable and efficient motion path for artificial mobile agents. This solves the problem that the existing technology does not fully consider the individual emotional factors of artificial mobile agents and the impact of uncertain factors in the local environment on the collaborative control of multiple agents, resulting in the collaborative control and evolution of heterogeneous mobile agent systems being unable to approach the real situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 is a schematic diagram of the structure of a heterogeneous mobile agent provided by an embodiment of the present invention;

[0099] Figure 2 This is a schematic diagram of the process of constructing an emotional fuzzy reasoning model provided by an embodiment of the present invention;

[0100] Figure 3 It is a simulation diagram of the virtual force model of the potential energy field in the operation scenario of the collaborative control of heterogeneous mobile intelligent bodies provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0101] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0102] Example 1

[0103] like Figure 1 As shown, this embodiment introduces a method for collaborative control and evolution of heterogeneous mobile agents, including:

[0104] Step 1: Use continuous cellular automata to mix artificial mobile agent cells and automatic mobile agent cells according to a preset ratio, and generate artificial mobile agents and automatic mobile agents through an iterative process;

[0105] Through the continuous cellular automaton model, artificial mobile agents and automatic mobile agents are mixed in a preset proportion to simulate heterogeneous mobile agents in a real interactive environment, providing a basic simulation environment for subsequent collaborative control research, making the experimental results closer to actual application scenarios, and enhancing the practicality and reliability of the research.

[0106] Step 2: embedding an emotion recognition module in the artificial mobile agent to obtain emotion clues of different granularities based on fuzzy theory;

[0107] By embedding an emotion recognition module and using fuzzy theory to obtain and quantify the emotional cues of artificial mobile agents, which will affect the movement of artificial mobile agents, considering emotional cues of different granularities can make the behavioral decisions of artificial mobile agents more in line with human reality, improve the authenticity and accuracy of the simulation model, and also introduce a new dimension for the collaborative control of heterogeneous agents - emotional factors.

[0108] Step 3: Based on the emotional clues of different granularities, construct an evolution rule for the artificial mobile agent and update the state of the artificial mobile agent;

[0109] The evolution rules of the artificial mobile agent include an acceleration rule, a deceleration rule, a random slowing-down rule introducing a random slowing-down probability function, and a position update rule;

[0110] Step 4: Construct the evolution rules of the artificial mobile agent including acceleration rules, deceleration rules, random slowing rules and position update rules according to the emotional clues to update the state of the artificial mobile agent.

[0111] These rules enable the artificial mobile agent's behavior to dynamically adjust based on its emotions, increasing the complexity and realism of the simulation environment. Meanwhile, the randomized slowing-down rule introduces uncertainty, making behavior even more difficult to predict.

[0112] Step 5: Obtain the position, velocity, and acceleration of the autonomous mobile agent, construct the autonomous mobile agent evolution rule based on the improved interaction potential energy field function, and update the state of the autonomous mobile agent;

[0113] Based on the improved interaction potential energy field function, the acceleration rules, deceleration rules and position update rules of the autonomous mobile agent are constructed to optimize the motion path and obstacle avoidance ability of the autonomous mobile agent, improve the adaptability and safety of the autonomous mobile agent in complex environments, and enable the autonomous mobile agent to respond to various situations more intelligently, such as avoiding pedestrians and adjusting speed.

[0114] The automatic mobile agent evolution rules include acceleration rules, deceleration rules and position update rules;

[0115] The acceleration rule is used to adjust the speed of the automatic mobile agent or the artificial mobile agent.

[0116] The deceleration rule is used to avoid collision between the automatic mobile agent and the artificial mobile agent.

[0117] The acceleration rule is used to adjust the speed of the intelligent agent to match its movement requirements, and the deceleration rule is used to avoid collisions and ensure safety. Together, they maintain the stability and safety of the simulation environment, reduce the occurrence of collision events, and improve the efficiency of the intelligent agent's movement.

[0118] The random deceleration rule, which introduces a random deceleration probability function, is used to randomly decelerate the artificial mobile agent based on the emotion influencing factor and the random deceleration probability function. By using the emotion influencing factor and the random deceleration probability function, the artificial mobile agent is randomly decelerated, thereby increasing the realism and unpredictability of the simulation environment and simulating the random behavior patterns of humans or organisms under the influence of emotions.

[0119] The position update rules determine the position of either an autonomous or artificially mobile agent. By updating the agent's position based on speed and direction, the agent moves within the simulation environment, enabling continuous movement and interaction within the virtual space and providing a foundation for subsequent path planning and decision-making.

[0120] Step 6: Control the movement and movement path of the automatic mobile agent through the field force planning method, and use the perception information sharing mechanism of the automatic mobile agent to combine the movement and movement path of the automatic mobile agent and the emotional clues of different granularities in the operation scenario of the artificial mobile agent to plan the movement path of the artificial mobile agent.

[0121] By modeling virtual repulsive and gravitational fields to control the motion paths of autonomous mobile agents, and leveraging a perceptual information sharing mechanism combined with emotional cues of varying granularity to plan the motion paths of artificial mobile agents, this approach improves the path planning efficiency and obstacle avoidance capabilities of autonomous mobile agents, while also making the motion paths of artificial mobile agents more reasonable and safer.

[0122] Example 2

[0123] Based on the same inventive concept as Example 1, this example introduces a specific application example of a method for collaborative control and evolution of heterogeneous mobile agents.

[0124] This embodiment uses traffic flow control as an example. Road traffic is composed of a mix of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), sharing road resources. Compared to HDVs, CAVs have shorter reaction delays. During driving, CAVs automatically maintain a headway with the vehicle ahead. CAVs also possess environmental awareness and autonomous driving capabilities, enabling more flexible and intelligent decision-making. Embedding an emotion recognition module in HDVs generates a driving strategy that incorporates driver emotion. The combination of HDVs and CAVs can simulate realistic mixed traffic flow characteristics.

[0125] When the operating scenario is road traffic flow, the continuous cellular automaton uses vehicles as cells and uses the continuous cellular automaton to mix the human-driven vehicle HDV cells and the automatic connected vehicle CAV cells in a preset proportion, generating human-driven vehicles and automatic connected vehicles through an iterative process.

[0126] The state information of the continuous cellular automaton , expressed as:

[0127] ;

[0128] in, 、 and Respectively represent this vehicle The position, velocity and acceleration at the current time t, Indicates this vehicle The ideal state vector of the vehicle, Indicates this vehicle The driver's emotional state vector, 、 、 Respectively represent this vehicle The expected speed of the vehicle, the vehicle The expected following distance of the vehicle and the vehicle Vehicle safety time interval, 、 and Respectively indicate at the current moment This vehicle Measures of drivers' emotional pleasure, emotional arousal, and emotional dominance.

[0129] Among them, the car At the current moment t, the acceleration Expressed as:

[0130] ;

[0131] in, Indicates the maximum acceleration of the vehicle, represents the vehicle acceleration index, and Respectively indicate the front vehicle and this car The relative distance and relative speed, Indicates the absolute value of the vehicle's comfortable deceleration. Indicates the static safety distance, Indicates the vehicle ahead The velocity at the current time t.

[0132] Among them, the car It refers to a manually driven vehicle (HDV) or an autonomous networked vehicle (CAV) operating in mixed traffic flow.

[0133] An emotion recognition module is embedded in the HDV to obtain emotion clues of different granularities based on fuzzy theory. The specific steps include:

[0134] (1) The emotion perception layer is used to capture and extract character emotion clues and scene emotion clues in the running scene through the character channel and the scene channel respectively to form a preliminary feature vector.

[0135] (2) Use fuzzy theory to quantify the emotion values ​​of the continuous dimensions in the preliminary feature vector to obtain the emotion influencing factors, such as Figure 2 As shown, the specific steps include:

[0136] Based on the Mamdani model, the emotional pleasure, emotional arousal and emotional dominance are taken as input variables, and the emotional influencing factor is taken as the output variable to construct a three-value input and single-value output emotional fuzzy reasoning model.

[0137] According to the emotion measurement values ​​of emotional pleasure, emotional arousal and emotional dominance, input variable fuzzy sets are constructed respectively, including emotional pleasure fuzzy set 1, emotional pleasure fuzzy set 2 and emotional pleasure fuzzy set 3, emotional arousal fuzzy set 1, emotional arousal fuzzy set 2 and emotional arousal fuzzy set 3, and emotional dominance fuzzy set 1, emotional dominance fuzzy set 2 and emotional dominance fuzzy set 3;

[0138] Constructing output variable fuzzy sets, including emotion factor fuzzy set 1, emotion factor fuzzy set 2 and emotion factor fuzzy set 3;

[0139] Using Gaussian membership function, the fuzzy sets of input variables and output variables are fuzzified;

[0140] Analyze the data samples of emotional pleasure, emotional arousal, and emotional dominance in the Emotic dataset to obtain an artificial experience rule library that can reflect the relationship between input variables and output variables;

[0141] According to the artificial experience rule base, the emotion representation fuzzy rule matrix is ​​constructed, and the constructed emotion representation fuzzy rule matrix is ​​expressed as:

[0142] ;

[0143] Among them, the first three columns of the emotion representation fuzzy rule matrix represent the fuzzy set indexes corresponding to the input variables of emotional pleasure, emotional arousal, and emotional dominance, respectively; the fourth column represents the fuzzy set index corresponding to the output variable of emotional influence factor; the fifth column represents the rule weight of the emotion representation fuzzy rule matrix; the sixth column represents the connectives of the rules of the emotion representation fuzzy rule matrix; when the rule weight is 1, the logical connective is "and", and when the rule weight is 2, the logical connective is "or";

[0144] The rules in the first row of the emotion representation fuzzy rule matrix are: when the input emotion pleasure belongs to the emotion pleasure fuzzy set 1, the input emotion arousal belongs to the emotion arousal fuzzy set 3, and the input emotion dominance belongs to the emotion dominance fuzzy set 3, the output emotion factor fuzzy set 1, the rule weight in the first row is 1, and the logical connective is "and";

[0145] The rule in the second row of the fuzzy rule matrix is: when the input emotional pleasure belongs to the emotional pleasure fuzzy set 2, the input emotional arousal belongs to the emotional arousal fuzzy set 1, and the input emotional dominance belongs to the emotional dominance fuzzy set 3, the output emotional factor fuzzy set 2, the rule weight in the second row is 1, and the logical connective is "and";

[0146] The rule in the third row of the fuzzy rule matrix is: when the input emotional pleasantness belongs to the emotional pleasantness fuzzy set one, the input emotional arousal belongs to the emotional arousal fuzzy set two, and the input emotional dominance belongs to the emotional dominance fuzzy set one, the output emotional factor fuzzy set three. The rule weight in the third row is 1, and the logical connective is "and".

[0147] The continuous sentiment values ​​in the preliminary feature vector are converted into membership of the input set fuzzy set through fuzzification;

[0148] According to the emotion representation fuzzy rule matrix, the fuzzy reasoner is used to map the input fuzzy set to the output fuzzy set, and the calculation is performed to obtain the final output fuzzy set;

[0149] The final output fuzzy set is defuzzified using the centroid method to obtain the emotional impact factor, which is expressed as:

[0150] ;

[0151] Among them, k represents the emotional influence factor, 、 and Respectively indicate at the current moment This vehicle Measures of driver emotional pleasure, emotional arousal, and emotional dominance.

[0152] (3) Inputting the emotion influencing factors into the feature fusion network for fusion processing to obtain emotion clues of different granularities.

[0153] According to the emotional clues of different granularities, an evolution rule of the artificial mobile agent is constructed and the state of the artificial mobile agent is updated.

[0154] The evolution rules of the artificial mobile agent include an acceleration rule, a deceleration rule, a random slowing-down rule introducing a random slowing-down probability function, and a position updating rule.

[0155] The acceleration rule of the constructed artificial mobile agent is expressed as:

[0156] ;

[0157] in, Indicates this vehicle The speed at time t, Indicates this vehicle The speed at time t, Indicates this vehicle The acceleration at time t is Indicates the maximum speed limit for the road.

[0158] The deceleration rule of the constructed artificial mobile agent is expressed as:

[0159] ;

[0160] in, Indicates safe following distance. Indicates the vehicle ahead and this car The relative distance, Indicates the stationary safety distance.

[0161] Considering that the driver's emotional factors will affect the operation of HDV, based on this vehicle Driver's emotional impact factor and random slowdown probability function, the vehicle The driver has The probability of random deceleration is, where represents the random slowing-down probability of vehicle n. The random slowing-down rule constructed by introducing the random slowing-down probability function is expressed as:

[0162] ;

[0163] in, Indicates this vehicle The speed at time t, Indicates this vehicle The speed at time t, Indicates the deceleration value;

[0164] The position of vehicle n at the current time t and speed Together determine the position of vehicle n at the next moment t+1 , the expression of the position update rule of the constructed artificial mobile agent is:

[0165] ;

[0166] in, Indicates that the vehicle The position at the next moment (t+1), Indicates this vehicle At the current time t, Indicates this vehicle The velocity at the current time t.

[0167] Obtain the position, velocity, and acceleration of the autonomous mobile agent, construct the autonomous mobile agent evolution rules based on the improved interaction potential energy field function, and update the state of the autonomous mobile agent. The specific steps include:

[0168] A virtual potential energy field is defined, and the movement and movement path of the automatic mobile intelligent body are controlled by a field force planning method. The virtual potential energy field contains a potential energy field virtual force, which is generated by the potential energy gradient in the potential energy field.

[0169] The virtual potential energy field usually consists of two parts: the attraction field generated by the target point and the repulsion field generated by the obstacle.

[0170] like Figure 3As shown, the potential energy field virtual force can be divided into position virtual force, velocity virtual force and acceleration virtual force according to the generation mechanism; the potential energy field virtual force can be divided into position virtual attraction and position virtual repulsion, velocity virtual attraction and velocity virtual repulsion, and acceleration virtual attraction and acceleration virtual repulsion according to the action relationship.

[0171] Virtual Position Repulsion Field Expressed as:

[0172] ;

[0173] Velocity repulsive field Expressed as:

[0174] ;

[0175] The acceleration repulsive field Expressed as:

[0176] ;

[0177] in, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, Indicates the speed difference between the preceding vehicle and the vehicle itself. Indicates the acceleration difference between the preceding vehicle and the vehicle itself. represents the potential energy coefficient of the virtual repulsive force at the position, represents the velocity virtual repulsive potential energy coefficient, Represents the acceleration virtual repulsive potential energy coefficient.

[0178] Since the vehicle always expects to travel at the maximum speed, if it does not reach the maximum expected speed, it will accelerate until it reaches the speed and move at a uniform speed.

[0179] So the gravitational field of the virtual position Expressed as:

[0180] ;

[0181] So the virtual velocity gravitational field Expressed as:

[0182] ;

[0183] So the gravitational field of the virtual acceleration Expressed as:

[0184] ;

[0185] in, Indicates the distance from the autonomous networked vehicle to the target point, Indicates the speed difference between the desired speed and the current speed, Indicates the difference between the expected acceleration and the current acceleration, represents the virtual gravitational potential energy coefficient of the position, represents the velocity virtual gravitational potential energy coefficient, Represents the virtual gravitational potential energy coefficient of acceleration.

[0186] Since the motion planning of an autonomous connected vehicle in a potential energy field is related to the potential energy virtual force, the relationship between the potential energy field virtual force and the potential energy field is expressed as:

[0187] ;

[0188] in, Represents potential energy field The generated virtual force is indexed as i, and the negative sign indicates that the direction of the virtual force of the potential energy field extends from the high potential field energy to the low potential field energy; Represents the gradient function.

[0189] The position virtual force includes position virtual attraction and position virtual repulsion, the velocity virtual force includes velocity virtual attraction and velocity virtual repulsion, and the acceleration virtual force includes acceleration virtual attraction and acceleration virtual repulsion.

[0190] According to the relationship between the potential energy field virtual force and the potential energy field, the position virtual repulsion Expressed as:

[0191] (10);

[0192] in, represents the potential energy coefficient of the virtual repulsive force at the position, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the distance vector from the autonomous connected vehicle to the target point, || || represents the modulus of the distance vector from the autonomous connected vehicle to the target point.

[0193] The virtual gravity of the position Expressed as:

[0194] (11);

[0195] in, represents the virtual gravitational potential energy coefficient of the position, Indicates the distance from the autonomous connected vehicle to the target point.

[0196] The velocity virtual repulsion Expressed as:

[0197] (12);

[0198] in, represents the velocity virtual repulsive potential energy coefficient, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the speed difference vector between the preceding vehicle and the vehicle itself, || || represents the modulus of the speed difference vector between the preceding vehicle and the vehicle itself.

[0199] The velocity is virtual gravity Expressed as:

[0200] (13);

[0201] in, represents the velocity virtual gravitational potential energy coefficient, Indicates the speed difference between the desired speed and the current speed, represents the distance from the autonomous networked vehicle to the target point, tanh represents the hyperbolic tangent function, || || represents the modulus of the speed difference vector between the desired speed and the current speed.

[0202] The acceleration virtual repulsion Expressed as:

[0203] (14);

[0204] in, represents the acceleration virtual repulsive potential energy coefficient, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the acceleration difference vector between the preceding vehicle and the vehicle itself, || || represents the modulus of the acceleration difference vector between the preceding vehicle and the vehicle itself.

[0205] The acceleration is virtual gravity Expressed as:

[0206] ; (15);

[0207] in, represents the acceleration virtual gravitational potential energy coefficient, Indicates the difference between the expected acceleration and the current acceleration. represents the distance from the autonomous connected vehicle to the target point, || || represents the modulus of the difference vector between the expected acceleration and the current acceleration.

[0208] The position, velocity and acceleration of the autonomous mobile agent are acquired in real time through the autonomous mobile agent's sensors, and the interaction potential energy field function is defined;

[0209] Position virtual force, velocity virtual force and acceleration virtual force are introduced to improve the interaction potential energy field function.

[0210] According to the improved interaction potential energy field function, the evolution rules of the automatic mobile agent are constructed and the state of the automatic mobile agent is updated.

[0211] The evolution rules of the autonomous mobile agent include acceleration rules, deceleration rules and position update rules;

[0212] Among them, the acceleration rule of the automatic mobile agent is expressed as:

[0213] ;

[0214] in, Indicates this vehicle The speed at time t, Indicates this vehicle The speed at time t, Indicates the maximum speed limit of the road. The vehicle is calculated based on the double integral motion equation The acceleration of Indicates this vehicle quality, represents the safe following distance, and F represents the total virtual force.

[0215] The deceleration rule of the autonomous mobile agent is: when the repulsive force generated by the virtual potential field of the preceding vehicle n+1 is greater than the attractive force, the total virtual force on vehicle n is expressed as a repulsive force;

[0216] The position update rules of the automatic mobile agent are consistent with those of the artificial mobile agent.

[0217] The movement and movement path of the automatic mobile agent are controlled by the field force planning method, and the movement path of the artificial mobile agent is planned by utilizing the perception information sharing mechanism of the automatic mobile agent, combining the movement and movement path of the automatic mobile agent and the emotional clues of different granularities in the operation scenario of the artificial mobile agent.

[0218] Among them, the method of controlling the movement and movement path of the automatic mobile intelligent body through the field force planning method includes:

[0219] According to the improved interaction potential energy field function, the virtual forces exerted on each autonomous mobile agent from other agents are calculated, including position virtual force, velocity virtual force and acceleration virtual force;

[0220] Perform vector synthesis of all virtual forces on each autonomous mobile agent to obtain the total virtual force of each autonomous mobile agent;

[0221] Using the total virtual force as a control signal, the speed and direction of the autonomous mobile agent are adjusted through the control algorithm.

[0222] Use the potential energy field virtual force to plan the driving path of the autonomous mobile agent.

[0223] In summary, the present invention improves the authenticity and accuracy of the collaborative control of heterogeneous mobile agents by introducing emotional influencing factors, accurately quantifying the continuous dimension emotional values ​​and integrating them into the random deceleration probability function, making the behavior of artificial mobile agents closer to reality. At the same time, the method based on the potential energy field virtual force converts the position, velocity and acceleration information of the automatic mobile agent into a virtual field force, effectively controls its movement through field force planning, and enhances the autonomous decision-making and path planning capabilities of the automatic mobile agent. In response to the difficult problems of collaborative control in complex environments, the present invention significantly improves the adaptability and overall intelligence of the collaborative control of heterogeneous mobile agents through emotional recognition, deceleration rules and potential energy field optimization.

[0224] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0225] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0226] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0227] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0228] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A method for collaborative control and evolution of heterogeneous mobile agents, characterized in that: include: Using continuous cellular automata to mix artificial mobile agent cells and automatic mobile agent cells in a preset ratio, and generate artificial mobile agents and automatic mobile agents through an iterative process; An emotion recognition module is embedded in the artificial mobile agent to obtain emotion clues of different granularities based on fuzzy theory; According to the emotional clues of different granularities, an evolution rule of the artificial mobile agent is constructed to update the state of the artificial mobile agent; The evolution rules of the artificial mobile agent include an acceleration rule, a deceleration rule, a random slowing-down rule introducing a random slowing-down probability function, and a position update rule; Obtain the position, velocity, and acceleration of the autonomous mobile agent, model it as a virtual repulsive field and a virtual gravitational field, obtain the position, velocity, and acceleration of the autonomous mobile agent, construct the autonomous mobile agent evolution rules based on the improved interaction potential energy field function, and update the state of the autonomous mobile agent; The automatic mobile agent evolution rules include acceleration rules, deceleration rules and position update rules; The acceleration rule is used to adjust the speed of the automatic mobile agent or the artificial mobile agent; The deceleration rule is used to avoid collisions between the autonomous mobile agent and the artificial mobile agent; The random slowing-down rule that introduces the random slowing-down probability function is used to randomly slow down the artificial mobile agent based on the emotion influence factor and the random slowing-down probability function; The position update rule is used to determine the position of the automatic mobile agent or the artificial mobile agent; The movement and movement path of the automatic mobile agent are controlled by the field force planning method, and the movement path of the artificial mobile agent is planned by utilizing the perception information sharing mechanism of the automatic mobile agent, combining the movement and movement path of the automatic mobile agent and the emotional clues of different granularities in the operation scenario of the artificial mobile agent.

2. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 1, characterized in that: The emotion recognition module includes an emotion perception layer and a feature fusion network. The emotion perception layer adopts a dual-channel fusion attention mechanism network structure. According to fuzzy theory, the method of obtaining emotion clues of different granularities includes: Utilizing the emotion perception layer to capture and extract character emotion clues and scene emotion clues in the running scene through the character channel and the scene channel respectively to form a preliminary feature vector; The emotion values ​​of the continuous dimensions in the preliminary feature vector are quantified using fuzzy theory to obtain the emotion impact factors. The emotion influencing factors are input into the feature fusion network for fusion processing to obtain emotion clues of different granularities.

3. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 2, characterized in that: The method of quantifying the emotion values ​​of the continuous dimensions in the preliminary feature vector using fuzzy theory to obtain the emotion influencing factors includes: Based on the Mamdani model, the emotional pleasure, emotional arousal and emotional dominance are taken as input variables, and the emotional influencing factor is taken as the output variable to construct a three-value input and single-value output emotional fuzzy reasoning model. According to the emotion measurement values ​​of emotional pleasure, emotional arousal and emotional dominance, input variable fuzzy sets are constructed respectively, including emotional pleasure fuzzy set 1, emotional pleasure fuzzy set 2 and emotional pleasure fuzzy set 3, emotional arousal fuzzy set 1, emotional arousal fuzzy set 2 and emotional arousal fuzzy set 3, and emotional dominance fuzzy set 1, emotional dominance fuzzy set 2 and emotional dominance fuzzy set 3; Constructing output variable fuzzy sets, including emotion factor fuzzy set 1, emotion factor fuzzy set 2 and emotion factor fuzzy set 3; Using Gaussian membership function, the fuzzy sets of input variables and output variables are fuzzified; Analyze the data samples of emotional pleasure, emotional arousal, and emotional dominance in the Emotic dataset to obtain an artificial experience rule library that can reflect the relationship between input variables and output variables; According to the artificial experience rule base, the emotion representation fuzzy rule matrix is ​​constructed; The continuous sentiment values ​​in the preliminary feature vector are converted into membership of the input set fuzzy set through fuzzification; According to the emotion representation fuzzy rule matrix, the fuzzy reasoner is used to map the input fuzzy set to the output fuzzy set, and the calculation is performed to obtain the final output fuzzy set; The final output fuzzy set is defuzzified using the centroid method to obtain the emotion impact factor.

4. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 3 is characterized in that: According to the artificial experience rule base, the emotion representation fuzzy rule matrix is ​​constructed, and the constructed emotion representation fuzzy rule matrix is ​​expressed as: （1）； Among them, the first three columns of the emotion representation fuzzy rule matrix represent the fuzzy set indexes corresponding to the input variables of emotional pleasure, emotional arousal, and emotional dominance, respectively. The fourth column represents the fuzzy set index corresponding to the output variable of emotional influence factor. The fifth column represents the rule weight of the emotion representation fuzzy rule matrix. The sixth column represents the connectives of the rules of the emotion representation fuzzy rule matrix. When the rule weight is 1, the logical connective is "and", and when the rule weight is 2, the logical connective is "or". The rules in the first row of the emotion representation fuzzy rule matrix are: when the input emotion pleasure belongs to the emotion pleasure fuzzy set 1, the input emotion arousal belongs to the emotion arousal fuzzy set 3, and the input emotion dominance belongs to the emotion dominance fuzzy set 3, the output emotion factor fuzzy set 1, the rule weight in the first row is 1, and the logical connective is "and"; The rule in the second row of the fuzzy rule matrix is: when the input emotional pleasure belongs to the emotional pleasure fuzzy set 2, the input emotional arousal belongs to the emotional arousal fuzzy set 1, and the input emotional dominance belongs to the emotional dominance fuzzy set 3, the output emotional factor fuzzy set 2, the rule weight in the second row is 1, and the logical connective is "and"; The rule in the third row of the fuzzy rule matrix is: when the input emotional pleasure belongs to the emotional pleasure fuzzy set 1, the input emotional arousal belongs to the emotional arousal fuzzy set 2, and the input emotional dominance belongs to the emotional dominance fuzzy set 1, the output emotional factor fuzzy set 3. The rule weight in the third row is 1, and the logical connective is "and".

5. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 1, characterized in that: When the operation scenario is road traffic flow, the continuous cellular automaton uses vehicles as cells and generates manually driven vehicles and automatically connected vehicles through an iterative process. The state information of the continuous cellular automaton , expressed as: （2）； in, 、 and Respectively represent this vehicle The position, velocity and acceleration at the current time t, Indicates this vehicle The ideal state vector of the vehicle, Indicates this vehicle The driver's emotional state vector, 、 、 Respectively represent this vehicle The expected speed of the vehicle, the vehicle The expected following distance of the vehicle and the vehicle Vehicle safety time interval, 、 and Respectively indicate at the current moment This vehicle measures of drivers’ emotional pleasure, emotional arousal, and emotional dominance; The continuous cellular automaton uses vehicles as cells and generates manually driven vehicles and automatically connected vehicles through an iterative process. Expressed as: （3）； in, Indicates the maximum acceleration of the vehicle, represents the vehicle acceleration index, and Respectively indicate the front vehicle and this car The relative distance and relative speed, Indicates the absolute value of the vehicle's comfortable deceleration. Indicates the static safety distance, Indicates the vehicle ahead The velocity at the current time t.

6. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 5, characterized in that: The final output fuzzy set is defuzzified using the centroid method to obtain the emotional impact factor, which is expressed as: （4）； in, 、 and Respectively indicate at the current moment This vehicle Measures of driver emotional pleasure, emotional arousal, and emotional dominance.

7. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 6, characterized in that: The evolution rules of the artificial mobile agent include an acceleration rule, a deceleration rule, a random slowing-down rule that introduces a random slowing-down probability function, and a position update rule. The acceleration rule of the artificial mobile agent is expressed as: （5）； in, Indicates this vehicle The velocity at the current time t, Indicates this vehicle The velocity at the current time t, Indicates this vehicle The acceleration at the current time t, Indicates the maximum speed limit of the road; The deceleration rule of the artificial mobile agent is expressed as: （6）； in, Indicates safe following distance. Indicates the vehicle ahead and this car The relative distance, Indicates the static safety distance; Based on this car Driver's emotional impact factor and random slowdown probability function, the vehicle The driver has The probability of random deceleration is, where represents the random slowing probability of vehicle n. The random slowing rule with the random slowing probability function can be expressed as: （7）； in, Indicates this vehicle The velocity at the current time t, Indicates this vehicle The velocity at the current time t, Indicates the deceleration value; The position of vehicle n at the current time t and speed Together determine the position of vehicle n at the next moment t+1 ; The expression of the position update rule of the artificial mobile agent is: （8）； in, Indicates that the vehicle The position at the next moment (t+1), Indicates this vehicle At the current time t, Indicates this vehicle The velocity at the current time t.

8. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 5, characterized in that: The evolution rules of the autonomous mobile agent include acceleration rules, deceleration rules and position update rules; Among them, the acceleration rule of the automatic mobile agent is expressed as: （9）； in, Indicates this vehicle The velocity at the current time t, Indicates this vehicle The velocity at the current time t, Indicates the maximum speed limit of the road. The vehicle is calculated based on the double integral motion equation The acceleration of Indicates this vehicle quality, represents the safe following distance, and F represents the total virtual force; The deceleration rule of the autonomous mobile agent is: when the repulsive force generated by the virtual potential field of the preceding vehicle n+1 is greater than the attractive force, the total virtual force on vehicle n is expressed as a repulsive force; The position update rules of the automatic mobile agent are consistent with those of the artificial mobile agent.

9. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 1, characterized in that: The method for controlling the motion and motion path of an autonomous mobile intelligent body by a field force planning method includes: According to the improved interaction potential energy field function, the virtual forces exerted on each autonomous mobile agent from other agents are calculated, including position virtual force, velocity virtual force and acceleration virtual force; Perform vector synthesis of all virtual forces on each autonomous mobile agent to obtain the total virtual force of each autonomous mobile agent; Using the total virtual force as a control signal, the speed and direction of the autonomous mobile agent are adjusted through the control algorithm. Use the potential energy field virtual force to plan the driving path of the autonomous mobile agent.

10. The method for cooperative control and evolution of heterogeneous mobile agents according to claim 9, characterized in that: The position virtual force includes position virtual attraction and position virtual repulsion, the velocity virtual force includes velocity virtual attraction and velocity virtual repulsion, and the acceleration virtual force includes acceleration virtual attraction and acceleration virtual repulsion; The virtual repulsion of the position Expressed as: （10）； in, represents the potential energy coefficient of the virtual repulsive force at the position, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the distance vector from the autonomous connected vehicle to the target point, || || represents the modulus of the distance vector from the autonomous networked vehicle to the target point; The virtual gravity of the position Expressed as: （11）； in, represents the virtual gravitational potential energy coefficient of the position, Indicates the distance from the autonomous connected vehicle to the target point; The velocity virtual repulsion Expressed as: （12）； in, represents the velocity virtual repulsive potential energy coefficient, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the speed difference vector between the preceding vehicle and the vehicle itself, || || represents the modulus of the speed difference vector between the preceding vehicle and the vehicle itself; The velocity is virtual gravity Expressed as: （13）； in, represents the velocity virtual gravitational potential energy coefficient, Indicates the speed difference between the desired speed and the current speed, represents the distance from the autonomous networked vehicle to the target point, tanh represents the hyperbolic tangent function, || || represents the modulus of the speed difference vector between the desired speed and the current speed; The acceleration virtual repulsion Expressed as: （14）； in, represents the acceleration virtual repulsive potential energy coefficient, Indicates the distance from the autonomous networked vehicle to the target point, represents the influence range of the repulsive potential field, represents the acceleration difference vector between the preceding vehicle and the vehicle itself, || || represents the modulus of the acceleration difference vector between the preceding vehicle and the vehicle itself; The acceleration is virtual gravity Expressed as: （15）； in, represents the acceleration virtual gravitational potential energy coefficient, Indicates the difference between the expected acceleration and the current acceleration. represents the distance from the autonomous connected vehicle to the target point, || || represents the modulus of the difference vector between the expected acceleration and the current acceleration.

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