Blockchain-based swarm robot identity authentication and task supervision fusion method

Abstract

The present application relates to a kind of blockchain-based group robot identity authentication and task supervision fusion method, comprising: generating legal identity information and writing in identity authentication center based on the identity authentication center designed by blockchain smart contract, while legal identity information is written into robot;Each robot private chain state initialization, sends identity authentication request and identity information;Identity authentication center receives the identity information of robot, carries out identity authentication;System manages robot according to the identity authentication result of robot, and the robot that passes identity authentication joins cluster;Cluster robot starts to execute task and regularly uploads location information and task execution information to the task supervision center based on the task supervision center designed by blockchain smart contract;Task supervision center scores robot according to the location information and task execution information of robot, and determines byzantine robot based on score.This method can reduce the time of system identifying byzantine robot, improve the byzantine fault tolerance of group robot system.

Description

Technical Field

[0001] This invention relates to the field of swarm robot technology, and more specifically to a blockchain-based method for integrating swarm robot identity authentication and task supervision. Background Technology

[0002] In recent years, with the development of swarm robotics technology, swarm robots have been widely used in warehousing and logistics, industry, exploration, and medical fields. However, the safety of swarm robots is a crucial factor to consider when applying them to real-world scenarios. Safety has been a neglected topic in cutting-edge robotics research, primarily due to the complex and heterogeneous characteristics of swarm robot systems, such as robot autonomy, decentralized control, large numbers of members, and collective behavior. The Byzantine robot problem is a safety issue originating from within swarm robots; Byzantine robots exhibit behavior inconsistent with benign robots. In practical systems, the presence of Byzantine robots often prevents swarms of robots from completing tasks as expected.

[0003] In swarm robots, the presence of Byzantine robots (i.e., faulty or malicious robots) can lead to discrepancies between the swarm's expected and actual behavior. The term "Byzantine robot" refers to a robot that exhibits behavior inconsistent with its pre-defined behavior, regardless of the underlying cause. A robot exhibits Byzantine behavior if it deviates from its pre-defined behavior and acts arbitrarily.

[0004] Blockchain is a cutting-edge technology for solving the security problems of distributed systems. As a new fault-tolerance method, it has been successfully applied in Bitcoin and the financial field. In recent years, blockchain has also been widely used in other fields such as the Internet of Things (IoT). Due to the interconnection and heterogeneity of swarm robots, traditional privacy, security, and trust issues in the IoT space are inherited. Combining swarm robots with permissionless public blockchain technology provides a solution to these problems. Blockchain can not only provide a reliable peer-to-peer communication channel for swarm robots, but also a method to overcome potential threats, vulnerabilities, and attacks.

[0005] Traditional blockchain-based Byzantine bot identification methods, such as Reputation Management Systems (RMS), can identify Byzantine bots within a swarm of bots in consensus scenarios, ensuring consensus is reached. However, RMS has a drawback: the system spends a considerable amount of time identifying Byzantine bots. In consensus scenarios, longer Byzantine bot identification times lead to longer consensus times for the swarm, but this doesn't fatally impact the consensus process, as the swarm eventually reaches a consensus. In task execution scenarios, however, longer Byzantine bot identification times pose greater security risks and may even cause task execution failures for the swarm. Summary of the Invention

[0006] The purpose of this invention is to provide a blockchain-based method for integrating swarm robot identity authentication and task supervision. This method can reduce the time required for the system to identify Byzantine robots and improve the Byzantine fault tolerance capability of the swarm robot system.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a blockchain-based method for integrating group robot identity authentication and task supervision, comprising the following steps:

[0008] (1) Generate legitimate identity information and write it into the identity authentication center designed based on blockchain smart contracts, and simultaneously write the legitimate identity information into the robot through a secure channel;

[0009] (2) Initialize the private chain status of each robot and send an identity authentication request and identity information;

[0010] (3) The identity authentication center receives identity information from other robots and performs identity authentication;

[0011] (4) The system manages robots based on the robot identity authentication results. Robots that pass the identity authentication are added to the cluster, while robots that fail the identity authentication cannot be added to the cluster.

[0012] (5) The cluster robots begin to execute tasks and periodically upload location information and task execution information to the task monitoring center designed based on blockchain smart contracts;

[0013] (6) The task supervision center scores the robot based on its location information and task execution information, and determines the Byzantine robot based on the score.

[0014] Furthermore, in a blockchain-based swarm robot system, the robot's legitimate identity information includes the hash value of the robot's legitimate identity identifier. The legitimate blockchain address A corresponding to the robot legal The hash value of a legitimate identity identifier With legitimate blockchain address A legal One-to-one matching: the identity authentication requires that the robot's identity identifier and blockchain address are both valid and match.

[0015] Furthermore, before robot i begins to move, it sends an identity authentication request and its identity identifier ID to the identity authentication center. i The identity authentication center receives the identity ID of robot i. i At the same time, the robot's blockchain address A is obtained. i The identity authentication center performs identity authentication to confirm the robot's identity ID. i and blockchain address A i Whether it is legal and matches, and identify robots that fail to pass identity verification as Byzantine robots.

[0016] Furthermore, during the execution of tasks, the robot periodically uploads its own location information and task execution information to the task monitoring center. The task monitoring center scores the robot every fixed time interval Δt based on the robot's location information and task execution status, and the robot's score reflects the robot's task completion rate. For robots with low scores, the task monitoring center classifies them as Byzantine robots.

[0017] Furthermore, a blockchain-based system for integrating swarm robot identity authentication and task supervision will be constructed, including:

[0018] The identity management center is used to store and manage the robot's legitimate identity information, and to send the robot's legitimate identity information to the identity authentication center during the blockchain initialization phase;

[0019] The identity authentication center is used to receive robot identity authentication requests and identity information, and to determine whether the robot's identity is legitimate.

[0020] The task monitoring center is used to supervise and score the task execution status of swarm robots;

[0021] In a swarm of robots, each robot primarily consists of a control module and a communication module. The control module is used for environmental perception and robot movement control, while the communication module enables communication between robots and interaction with the blockchain platform.

[0022] The two-layer communication network includes a real-time communication network and an asynchronous communication network. The real-time communication network does not use the blockchain and is used to enable real-time data interaction between robots, while the asynchronous communication network uses the blockchain to enable asynchronous data interaction between robots and the blockchain.

[0023] Furthermore, the identity management center stores the hash values ​​of the legitimate identity identifiers of n legitimate robots. The legitimate blockchain address A corresponding to the robot legal The hash value of a legitimate identity identifier With legitimate blockchain address A legal One-to-one matching; robot's legitimate identity identifier ID legal The hash value is as shown in equation (1):

[0024]

[0025] Where h(·) is the hash function;

[0026] Define a hash value that identifies the legitimate identities of n robots. and legitimate blockchain address A legal The set Where n is the number of robots in the system, and i is the i-th robot in the system;

[0027] During the blockchain network initialization phase, the identity management center will assign the hash value of the robot's legitimate identity identifier. and legitimate blockchain address A legali The set Write to the identity authentication center; define two mappings Θ and Ξ in the smart contract as follows:

[0028]

[0029] Where G1 is an additive cyclic group of order q primes, N is a constant, {0,1} * A bit string consisting of 0s and 1s of arbitrary length;

[0030] Let n1, n2 ∈ N, and use the hash value of the robot's legitimate identity identifier. and legitimate blockchain address A legal Binding, as in equation (3):

[0031]

[0032] Each robot in the system has an identity ID and a blockchain address A; before robot i starts moving, it sends an identity authentication request and its identity ID to the identity authentication center. i The identity authentication center receives the identity ID of robot i. i , for ID i Perform hashing as shown in equation (4):

[0033]

[0034] At the same time, the identity authentication center obtains the blockchain address A of robot i. iThen the identity authentication center confirms the robot's identity ID. i and blockchain address A i Is it valid and does it match?

[0035]

[0036] The identity authentication result is defined as follows:

[0037]

[0038] Only robot i's authentication result is Γ i =1, only then will the robot pass the identity verification and be allowed to join the group of robots to perform tasks.

[0039] Furthermore, before each robot joins the formation to perform a task, the task monitoring center assigns each robot an initial score r based on its identity authentication results. i,0 , i = 1, 2, ..., n:

[0040]

[0041] Among them, Γ i κ is the authentication result of robot i, and κ is the initial score of the robot that has passed the authentication.

[0042] Every fixed time interval Δt, the task monitoring center performs a round of scoring on the robot's task execution; the scoring process for robot i in the m-th round is as shown in equations (8) to (14):

[0043] definition As a task variable, Configure the robot; ρ is related to the σ function, ρ i The task variable represents robot i. The task function of robot i is defined as follows:

[0044] ρ i =f(σ i (8)

[0045] In the m-th round of scoring, the target point of robot i is related to the robot's task execution state function;

[0046] P i,m =λ(inf i (9)

[0047] Among them, inf i This is the task execution information for robot i, where λ(·) represents P. i,m and inf i The functional relationship between them;

[0048] The tracking error between robot i's current position and the target point is defined as follows:

[0049] d i,m =||ρ i,m -P i,m || (10)

[0050] Where ||·|| represents the distance between two points, ρ i,m Indicates the robot's current position;

[0051] The task monitoring center's score r for robot i in round m. i,m By s i,m and q i,m Composition, q i,m with inf i Function-related; Task supervision introduces a penalty factor to severely punish robots that exhibit Byzantine behavior due to long-term malfunctions or malicious intent.

[0052] s i,m The definition is as follows:

[0053]

[0054] Where α1, α2, and α3 are all scoring factors, and different scoring factors are set based on the distance between the robot and the target point, d last It is the distance d between the robot and the target point in the previous round of scoring;

[0055] q i,m The definition is as follows:

[0056] q i,m =Ξ(inf i (12)

[0057] Where Ξ(·) represents q i,m and inf i The functional relationship between them; the score r of robot i in the mth round. i,m The definition is as follows:

[0058]

[0059] Where, r i,m-1 Let be the score of robot i in the m-1th score;

[0060] After each round of scoring, the task monitoring center updates d. last For use in the next round of scoring:

[0061] d last =d i,m (14)

[0062] The score obtained by the robot reflects the degree to which it has completed the task; based on the robot's task completion rate, Byzantine robots with abnormal task execution can be identified; a threshold τ is set, when r i,m <τ, the task monitoring center determined that robot i was a Byzantine robot and issued an alarm;

[0063] Calculate the average score of the legitimate robot. As in equation (15):

[0064]

[0065] Where N is the number of legal robots;

[0066] Define the score r of robot i i,m Average score of swarm robots The difference Δ between them is shown in equation (16):

[0067]

[0068] Set a threshold γ. If Δ≥γ, it also indicates that the robot task execution is abnormal, and the task monitoring center will also determine that robot i is a Byzantine robot.

[0069] After robot i is identified as a Byzantine robot at time t, the task monitoring center gives it a score of 0, i.e., s. i,t+n =0,q i,t+n =0, where n = 1, 2, ..., ∞; For the swarm robot score, by formula (15), Byzantine robots are excluded to prevent Byzantine robots from having an adverse effect on the identification of Byzantine robots in the subsequent swarm robot system.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] (1) Blockchain is introduced into the swarm robot to identify Byzantine robots, which can ensure trust between multiple entities without a third party. Blockchain avoids single point of failure and centralized corruption, and ensures the transparency and traceability of transactions.

[0072] (2) An improvement was made to the traditional RMS method, and a blockchain decentralized task supervision technology suitable for swarm robot task execution scenarios was proposed, which can quickly and accurately identify Byzantine robots in swarm robots.

[0073] (3) A blockchain-based decentralized identity authentication technology was designed using blockchain smart contracts, which can effectively reduce the time required to identify Byzantine robots when the robot's identity is illegal.

[0074] (4) A two-layer communication network was designed within the blockchain-based blockchain swarm robot identity authentication and task supervision fusion framework, which solved the communication delay problem caused by introducing blockchain into the swarm robot system. Attached Figure Description

[0075] Figure 1 This is an architecture diagram of a blockchain-based swarm robot identity authentication and task supervision fusion system according to an embodiment of the present invention;

[0076] Figure 2 This is a schematic diagram of a two-layer communication network in an embodiment of the present invention;

[0077] Figure 3 This is a list of simulation parameters in the embodiments of the present invention;

[0078] Figure 4 This invention relates to robot task setting and simulation scenario design in embodiments of the present invention.

[0079] Figure 5 The following are simulation results in this embodiment of the invention when there are no Byzantine robots in the swarm of robots; where (a) to (f) are the task execution process of the swarm of robots, (g) is the trajectory of the swarm of robots, and (h) is the robot's score;

[0080] Figure 6 The following are the authentication results in this embodiment of the invention; where (a) indicates that all robot identities are valid and all robots have passed the authentication, and (b) indicates that robot 3's identity is invalid and robot 3 has failed the authentication.

[0081] Figure 7 This is a comparison of simulation results using RMS and blockchain-based identity authentication and task supervision fusion technology when robot 3 is set as a faulty robot in this embodiment of the invention; where (a) is the robot 3 stopping due to fault in 20s, (b) is the trajectory of the group of robots, (c) is the robot score when using RMS, and (d) is the robot score when using blockchain-based identity authentication and task supervision fusion technology.

[0082] Figure 8 This invention presents a comparison of simulation results using RMS and blockchain-based identity authentication and task supervision fusion technology when robot 3 is set as a legitimate malicious Byzantine robot. Among them, (a) shows robot 3 delivering goods to the wrong unloading point (i.e., a non-preset unloading point); (b) shows the trajectory of the swarm of robots; (c) shows the robot score when using RMS; and (d) shows the robot score when using blockchain-based identity authentication and task supervision fusion technology.

[0083] Figure 9This invention presents a comparison of simulation results using RMS and blockchain-based identity authentication and task supervision fusion technology when robot 3 is set as a malicious Byzantine robot with an illegitimate identity. (a) shows robot 3 delivering goods to the wrong unloading point (i.e., a non-preset unloading point) when using RMS; (b) shows robot 3 failing to join the cluster to execute tasks when using blockchain-based identity authentication and task supervision fusion technology; (c) shows the robot score when using RMS; and (d) shows the robot score when using blockchain-based identity authentication and task supervision fusion technology. Detailed Implementation

[0084] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0085] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, 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 pertains.

[0086] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0087] This embodiment provides a blockchain-based method for integrating group robot identity authentication and task supervision, including the following steps:

[0088] (1) Generate legitimate identity information and write it into the identity authentication center designed based on blockchain smart contracts, and write the legitimate identity information into the robot through a secure channel.

[0089] (2) Initialize the private chain status of each robot and send identity authentication request and identity information.

[0090] (3) The identity authentication center receives identity information from other robots and performs identity authentication.

[0091] (4) The system manages robots based on the robot identity authentication results. Robots that pass the identity authentication are added to the cluster, while robots that fail the identity authentication cannot be added to the cluster.

[0092] (5) The cluster robots begin to execute tasks and periodically upload location information and task execution information to the task monitoring center designed based on blockchain smart contracts.

[0093] (6) The task supervision center scores the robot based on its location information and task execution information, and determines the Byzantine robot based on the score.

[0094] In a blockchain-based swarm robot system, the robot's legitimate identity information includes the hash value of the robot's legitimate identity identifier. The legitimate blockchain address A corresponding to the robot legal The hash value of a legitimate identity identifier With legitimate blockchain address A legal One-to-one matching: the identity authentication requires that the robot's identity identifier and blockchain address are both valid and match.

[0095] Before robot i begins to move, it sends an identity authentication request and identity ID to the identity authentication center. i The identity authentication center receives the identity ID of robot i. i At the same time, the robot's blockchain address A is obtained. i The identity authentication center performs identity authentication to confirm the robot's identity ID. i and blockchain address A i Whether it is legal and matches, and identify robots that fail to pass identity verification as Byzantine robots.

[0096] During the execution of a task, the robot periodically uploads its own location information and task execution information to the task monitoring center. Every fixed time interval Δt, the task monitoring center scores the robot based on its location information and task execution status. The robot's score reflects its task completion rate. Robots with low scores are classified as Byzantine robots by the task monitoring center.

[0097] like Figure 1 As shown, this embodiment also constructs a blockchain-based swarm robot identity authentication and task supervision fusion system based on blockchain smart contracts, namely, a blockchain-based swarm robot identity authentication and task supervision fusion technology framework. This system specifically includes:

[0098] (1) Identity Management Center (IMC), such as Figure 1 As shown in ①, this part is responsible for storing and managing the robot's legitimate identity information. During the blockchain initialization phase, the IMC sends the robot's legitimate identity information to the identity authentication center. The system administrator, as... Figure 1 As shown in section ④, the system has permissions to add, modify, and delete identity information. Upon detecting the Byzantine robot, the system sends an alert to the system administrator.

[0099] (2) Identity Authentication Center, such as Figure 1 As shown in Figure ②, the role of the identity authentication center is to receive robot identity authentication requests and identity information, and to determine whether the robot's identity is legitimate. Robots with illegitimate identities will be considered Byzantine robots.

[0100] (3) Task Supervision Center, such as Figure 1 As shown in Figure ③, the role of the task monitoring center is to supervise and score the task execution status of the swarm of robots. The robot's score reflects its task completion rate. Robots with low task completion rates will be considered Byzantine robots.

[0101] (4) Robots in a swarm of robots, such as Figure 1 As shown in Figure ⑤, the robot mainly consists of a control module and a communication module, such as... Figure 1 As shown in Figure 6, the control module is responsible for sensing the environment and controlling the robot's movement, while the communication module is responsible for enabling communication between robots and interacting with the blockchain through the blockchain platform.

[0102] (5) A two-layer communication network, including a real-time communication network and an asynchronous communication network; wherein, the real-time communication network is as follows: Figure 1 As shown in Figure ⑦, this communication network does not rely on blockchain and is used to achieve real-time data interaction between robots; the asynchronous communication network is as follows: Figure 1 As shown in Figure ⑧, this communication network uses blockchain to enable asynchronous data interaction between the robot and the blockchain. The two-layer communication network ensures that the real-time performance of the swarm robot system is not affected after the introduction of blockchain.

[0103] I. Identity Authentication within a Blockchain-Based Framework for Integrating Identity Authentication and Task Supervision for Group Robots

[0104] This embodiment uses blockchain smart contracts to design the system's identity authentication center, implementing an identity authentication process that verifies the robot's identity through its unique identifier and its address on the blockchain. The authentication process consists of two steps: First, the IMC creates valid identity information and sends it to the smart contract; then, the robot connects to the blockchain network and begins identity authentication. The detailed process is as follows:

[0105] (1) Create legitimate identity information

[0106] This phase takes place in the IMC. The IMC stores the hash values ​​of the legitimate identities of n legitimate robots. The legitimate blockchain address A corresponding to the robot legal The hash value of a legitimate identity identifier With legitimate blockchain address A legal One-to-one matching. Take the hash function h(·), which is the robot's legitimate identity ID. legal The hash value is as shown in equation (1):

[0107]

[0108] Define a hash value that identifies the legitimate identities of n robots. and legitimate blockchain address A legali The set Where n is the number of robots in the system, and i is the i-th robot in the system.

[0109] During the blockchain network initialization phase, IMC will use the hash value of the robot's legitimate identity identifier. and legitimate blockchain address A legali The set Write to the identity authentication center. Define two mappings Θ and Ξ in the smart contract, as follows:

[0110]

[0111] Where G1 is an additive cyclic group of order q primes, M is a constant, {0,1} * A bit string consisting of 0s and 1s of arbitrary length.

[0112] Let n1, n2 ∈ N, and use the hash value of the robot's legitimate identity identifier. and legitimate blockchain address A legal Binding, as in equation (3):

[0113]

[0114] (2) Robot Identity Authentication

[0115] Each robot in the system possesses an identity ID and a blockchain address A. Before robot i begins movement, it needs to send an identity authentication request and its identity ID to the identity authentication center. The identity authentication center receives robot i's identity ID. i , for ID i Perform hashing as shown in equation (4):

[0116]

[0117] At the same time, the identity authentication center will obtain the blockchain address A of robot i. i Next, the identity verification center will confirm the robot's identity ID. i and blockchain address A i Is it valid and does it match?

[0118]

[0119] The identity authentication result is defined as follows:

[0120]

[0121] Only robot i's authentication result is Γ i =1, the robot is only allowed to join the group of robots to perform tasks after it has passed identity verification.

[0122] II. Task Supervision within a Blockchain-Based Framework for the Integration of Identity Authentication and Task Supervision

[0123] The RMS method of this invention utilizes blockchain smart contracts to design a robot task monitoring center. After identity authentication, the swarm of robots begins executing preset tasks. Every fixed time interval Δt, the task monitoring center evaluates and scores the robots' task execution. The score reflects the task completion rate of each robot. The task monitoring center scores each robot based on its distance from the target point and its task execution performance. The specific process is as follows:

[0124] Before each robot joins the formation to perform a task, the task monitoring center assigns each robot an initial score r based on their identity verification results. i,0 (i = 1, 2, ..., n):

[0125]

[0126] Among them, Γ i is the authentication result of robot i, and k is the initial score set by the system administrator for robots that have passed authentication.

[0127] Every fixed time interval Δt, the task monitoring center evaluates the robot's task performance. The evaluation process for robot i in the m-th round is shown in equations (8) to (14):

[0128] definition As a task variable, Configure the robot. ρ is related to the σ function, ρ i (i = 1, 2, ..., n) represents the task variable of robot i. The task function of the robot is defined as follows:

[0129] ρ i =f(σ i (8)

[0130] In the m-th round of scoring, the target point of robot i is related to the robot's task execution state function.

[0131] P i,m =λ(inf i (9)

[0132] Among them, inf i This is the task execution information for robot i, where λ(·) represents P. i,m and inf i The functional relationship between them.

[0133] The tracking error between robot i's current position and the target point is defined as follows:

[0134] d i,m =||ρ i,m -P i,m || (10)

[0135] Where ||·|| represents the distance between two points, ρ i,m This indicates the robot's current position.

[0136] The task supervisor scores robot i with r in the m-th round of scoring. i,m By s i,m and q i,m Composition, q i,m with inf i Functional dependence. Unlike RMS, the task supervision of this invention introduces a penalty factor to more severely punish robots that exhibit Byzantine behavior due to prolonged malfunctions or malicious intent. i,m The definition is as follows:

[0137]

[0138] Where α1, α2, and α3 are scoring factors, and different scoring factors can be set based on the distance between the robot and the target point, d last d is the distance d between the robot and the target point in the previous scoring round. The setting of the penalty factor ζ is described in the simulation section.

[0139] q i,m The definition is as follows:

[0140] q i,m =Ξ(inf i (12)

[0141] Where Ξ(·) represents q i,m and inf i The functional relationship between them. The score r of robot i in round m. i,m The definition is as follows:

[0142]

[0143] Where r i,m-1Let be the score of robot i in the m-1th rating.

[0144] After each round of scoring, the task monitoring center updates d. last For use in the next round of scoring:

[0145] d last =d i,m (14)

[0146] The score a robot receives reflects its degree of task completion. By analyzing task completion rates, Byzantine robots exhibiting abnormal performance can be identified, such as those malfunctioning and unable to move. After a period of time, robots with significantly lower task completion rates than benign robots will have their scores (r) reduced. i,m It will be lower than a benign robot. Set a threshold τ, when r i,m <τ, the task monitoring center will determine that robot i is a Byzantine robot and send an alert to the system administrator.

[0147] In addition, the average score of the legitimate robot is calculated. As in equation (15):

[0148]

[0149] Where N is the number of legal robots.

[0150] Define the score r of robot i i,m Average score of swarm robots The difference Δ between them is shown in equation (16):

[0151]

[0152] A threshold γ is set; if Δ ≥ γ, it also indicates that the robot's task execution is abnormal. This is determined by comparing the average score with that of the group of robots. In comparison, even if the Byzantine robot does not participate in the task supervision center's evaluation of robot task execution for a long time, the score of the benign robot will continue to rise, and there will be a large difference between the score of the Byzantine robot and the score of the benign robot, so the system can still identify the Byzantine robot.

[0153] After robot i is identified as a Byzantine robot at time t, the task monitoring center gives it a score of 0, i.e., s. i,t+n =0,q i,t+n =0, where n = 1, 2, ..., ∞. For the scoring of swarm robots, as shown in equation (15), excluding Byzantine robot i can prevent Byzantine robot i from having an adverse effect on the identification of Byzantine robots in the subsequent swarm robot system.

[0154] III. Two-layer communication network

[0155] To address the communication latency issue caused by introducing blockchain into swarm robots, this embodiment designs a two-layer communication network, such as... Figure 2 As shown. A blockchain communication network is designed for communication tasks with low real-time requirements. For example, in this invention, robot nodes send identity authentication requests and information to the identity authentication center (smart contract) via a blockchain communication node for authentication. The completion of identity authentication does not have high real-time requirements, and communication latency will not adversely affect robot identity authentication. A high-speed communication network, on the other hand, is designed for communication tasks with high real-time requirements. High-speed communication networks use the communication networks commonly used by robots, such as the TCP / UDP-based network communication in ROS (Robot Operating System). The advantage of this type of communication network is its high transmission speed, but its disadvantage is the lack of data security protection measures. Cryptography can solve the security problems on high-speed communication networks by using symmetric or asymmetric encryption to encrypt and decrypt communication data between nodes, as follows:

[0156]

[0157] Where EM represents encrypted information and PM represents plaintext information. Ψ and Ψ(·) are the encryption and decryption functions, respectively, Key En and Key De These are the encryption key and the decryption key. When node A sends a message to node B, it uses the encryption key to encrypt the plaintext PM, obtaining the ciphertext EM. After receiving the ciphertext EM, node B uses the decryption key to decrypt the ciphertext EM, obtaining the plaintext PM. For symmetric encryption, the encryption key and the decryption key are the same; for asymmetric encryption, different keys are used for encryption and decryption.

[0158] Simulation Comparison and Analysis

[0159] The simulation considers three mobile robots operating on a plane. The goal of the swarm robot system is to form a convoy, reach a designated pickup point to retrieve goods, and transport the goods to a designated unloading point. Simulation parameters are as follows: Figure 3 As shown, the simulation uses the Ethereum blockchain platform and the SHA256 hash function.

[0160] In the simulation, the penalty factor ζ is set as follows:

[0161]

[0162] Where s i,m-1 Let be the score of robot i in the (m-1)th round of scoring.

[0163] The simulation sets two thresholds, τ and r-γ, to detect Byzantine robots. When robot i scores r... i,m If the value is below the Byzantine robot recognition threshold, the system identifies the robot as a Byzantine robot.

[0164] In the simulation, the swarm robot formation method adopts a leader-follower approach. Robot 1 is set as the leader, and robots 2 and 3 are the followers. The robot task design and scenario setup are as follows. Figure 4 As shown in the diagram. The simulation includes three types of Byzantine robots: damaged robots, malicious robots with legitimate identities, and robots with illegitimate identities, along with benign robots. The task settings for the benign robots are as follows. Figure 4 As shown in (a), the target unloading point for the benign robot is set to P2. The task settings for the malicious Byzantine robot (regardless of its legitimacy) are as follows: Figure 4 As shown in (b), the target unloading point for the malicious robot is set to P3. The task settings for a damaged Byzantine robot (i.e., a robot with a faulty actuator or sensor) are the same as those for a benign robot, but this type of Byzantine robot will be unable to complete the set task due to actuator or sensor failure.

[0165] The following table shows a comparison of the performance of the present invention with that of the traditional RMS method, obtained through simulation experiments:

[0166]

[0167]

[0168] Without Byzantine robots in the swarm of robots, the simulation results are as follows: Figure 5 As shown. When there are no Byzantine robots in the swarm, as... Figure 5 (a)~ Figure 5 As shown in (g), all robots perform preset tasks, and the task monitoring center's score for robot task performance continues to rise, as... Figure 5 As shown in (h), since no robot performed a non-preset task and the robot's score was not lower than the task monitoring center's identification threshold for Byzantine robots, no robot was identified as a Byzantine robot.

[0169] With Robot 3 configured as a faulty Byzantine robot, simulation results of RMS deployment are compared with those of deployment using a blockchain-based identity authentication and task supervision fusion technology. The task presets for benign robots—Robot 1 and Robot 2—are as follows: Figure 4 (a) Robot 3 is set to stop moving after 20 seconds due to a wheel malfunction. In this simulation, all robots have legitimate identities, such as... Figure 3The parameters in the code indicate the robot's legitimate identity; all robots will successfully pass identity verification, such as... Figure 6 As shown in (a). The simulation results are as follows. Figure 7 As shown. The Byzantine robot's score when deploying RMS is shown when robot 3's actuator (wheels) malfunctions. Figure 7 As shown in (c), because the score is less than the recognition threshold 2, robot 3 is identified as a Byzantine machine at 42.5s; the robot's score when deploying blockchain-based identity authentication and task supervision fusion technology is as follows: Figure 7 As shown in (d), because its score was below the recognition threshold of 2, robot 3 was identified as a Byzantine robot at 35 seconds. Robot 3 stopped moving at 20 seconds due to a faulty wheel, as... Figure 7 (a) Both RMS and the blockchain-based identity authentication and task supervision fusion technology detected abnormal behavior in robot 3, and robot 3's score began to decline. Because the blockchain-based identity authentication and task supervision fusion technology introduces a penalty factor, when the robot engages in Byzantine behavior for an extended period, the task supervision center's score for the robot drops rapidly. The blockchain-based identity authentication and task supervision fusion technology can identify faulty Byzantine robots faster than RMS.

[0170] When Robot 3 is a malicious Byzantine robot with a legitimate identity, the simulation results of deploying RMS are compared with those of deploying a blockchain-based identity authentication and task supervision fusion technology. Robot 3 is set as a malicious Byzantine robot with a legitimate identity, while Robots 1 and 2 are both benign robots. Their identity markers are as follows: Figure 3 The robots displayed their legal identity tags. All robots successfully passed the identity verification. The preset tasks for Robot 1 and Robot 2 are as follows: Figure 4 (a) The preset task for robot 3 is... Figure 4 (b) Simulation results are as follows Figure 8 As shown. If robot 3 is set as a malicious Byzantine robot with a legitimate identity, then robot 3 will pass identity authentication and join the swarm to perform the task. Approximately 40 seconds later, the swarm of robots arrives at the pickup point to collect the goods. Subsequently, robot 3 begins transporting the goods to the incorrect unloading point, as shown... Figure 8 As shown in (a), the system detected Byzantine behavior in robot 3, and robot 3's score began to decrease. When RMS was deployed, at 70 seconds, robot 3's score fell below the Byzantine robot identification threshold and was identified as a Byzantine robot, as... Figure 8 As shown in (c); when deploying blockchain-based identity authentication and task supervision fusion technology, at 52.5 seconds, robot 3's score is below the Byzantine robot recognition threshold, and it is identified as a Byzantine robot, as... Figure 8As shown in (d), similar to the result of setting robot 3 as a faulty robot, the blockchain-based identity authentication and task supervision fusion technology can identify Byzantine robots faster than RMS due to the introduction of a penalty factor.

[0171] When Robot 3 is configured as a malicious Byzantine robot with an illegitimate identity, the simulation results of deploying RMS are compared with those of deploying a blockchain-based identity authentication and task supervision fusion technology. Robot 3's identity identifiers are as follows: Figure 3 The illegal identification marks of robots, the identification marks of robot 1 and robot 2 are as follows: Figure 3 The robots' legal identity markers. The pre-set tasks for Robot 1 and Robot 2 are... Figure 4 (a) The preset task for robot 3 is... Figure 4 (b) Simulation results are as follows Figure 9 As shown. In the case where robot 3 is a malicious Byzantine robot with an illegal identity, the trajectories and scores of the swarm of robots are as follows: Figure 9 (a) and Figure 9 (b); The trajectories and scores of the swarm robots are as follows: Figure 9 (c) and Figure 9 (d) During RMS deployment, from 0s to approximately 40s, robot 3 behaves like a benign robot until it picks up goods and sends them to the wrong unloading point, thus RMS scores all robots consistently. Then, RMS detects robot 3's Byzantine behavior (picking up goods and sending them to the wrong unloading point), robot 3's score drops, and at 70s, it is identified as a Byzantine robot because its score falls below the identification threshold 2. When deploying blockchain-based identity authentication and task supervision integration technology, robots need to send authentication requests and identity information to the identity authentication center for authentication before joining the formation to perform tasks. Robots 1 and 2, with valid identity information, join the formation to perform tasks after successful authentication. Robot 3, due to failing identity authentication, such as... Figure 6 (b) Robot 3 was refused entry into the formation, and its initial score was set to r. 2,0 =0, because the score is below the Byzantine robot recognition threshold. Robot 3 was identified as a Byzantine robot in the first round of scoring at the task monitoring center, which took 2.5 seconds.

[0172] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0174] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0175] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0176] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A blockchain-based method for integrating swarm robot identity authentication and task supervision, characterized in that, Includes the following steps: (1) Generate legitimate identity information and write it into the identity authentication center designed based on blockchain smart contracts, and write the legitimate identity information into the robot through a secure channel; (2) Initialize the private chain status of each robot and send an identity authentication request and identity information; (3) The identity authentication center receives identity information from other robots and performs identity authentication; (4) The system manages robots based on the robot identity authentication results. Robots that pass the identity authentication are added to the cluster, while robots that fail the identity authentication cannot be added to the cluster. (5) The cluster robots begin to execute tasks and periodically upload location information and task execution information to the task monitoring center designed based on blockchain smart contracts; (6) The task supervision center scores the robot based on its location information and task execution information, and determines the Byzantine robot based on the score; Construct a blockchain-based system integrating swarm robot identity authentication and task supervision, including: The identity management center is used to store and manage the robot's legitimate identity information, and to send the robot's legitimate identity information to the identity authentication center during the blockchain initialization phase; The identity authentication center is used to receive robot identity authentication requests and identity information, and to determine whether the robot's identity is legitimate. The task monitoring center is used to supervise and score the task execution status of swarm robots; The robots in a swarm of robots consist of a control module and a communication module. The control module is used for environmental perception and robot movement control, while the communication module enables communication between robots and interaction with the blockchain platform. The two-layer communication network includes a real-time communication network and an asynchronous communication network. The real-time communication network does not use the blockchain and is used to realize real-time data interaction between robots, while the asynchronous communication network uses the blockchain and is used to realize asynchronous data interaction between robots and the blockchain. Before each robot joins the formation to perform a task, the task monitoring center assigns an initial score to each robot based on its identity authentication results. : in, It's a robot. Identity authentication results The initial score for robots that have passed identity verification; Every fixed time The task monitoring center will conduct a round of scoring on the robot's task execution; Wheel robot The scoring process is as shown in equations (9) to (14): In the In the round of scoring, the robot The target point is related to the robot's task execution state function; in, It's a robot. Task execution information, express and The functional relationship between them; robot The tracking error between the current position and the target point is defined as follows: in, Indicates the distance between two points. Indicates the robot's current position; Mission Oversight Center in In the round of scoring, the robot rating Depend on and composition, and Function-related; Task supervision introduces a penalty factor to severely punish robots that exhibit Byzantine behavior due to long-term malfunctions or malicious intent. The definition is as follows: in, These are all scoring factors, with different scoring factors set based on the distance between the robot and the target point. It is the distance between the robot and the target point in the previous round of scoring. ; The definition is as follows: in, express and Functional relationship between them; robot In the Wheel rating The definition is as follows: in, For robots In the The score in the rating; After each round of scoring, the Task Monitoring Center updates... For use in the next round of scoring: The score obtained by the robot reflects the degree to which it has completed the task; based on the robot's task completion rate, Byzantine robots with abnormal task performance can be identified; thresholds can be set. ,when The task monitoring center judges the robot For Byzantine robots, and alert the police; Calculate the average score of the legitimate robot. As in equation (15): in, It is the number of legal robots; Define Robot Score Average score of swarm robots The difference between As in equation (16): Set threshold ,if This also indicates that the robot's task execution is abnormal, and the task monitoring center also judged the robot... For Byzantine robots; robot exist Once identified as a Byzantine robot, the mission monitoring center gave it a score of 0, meaning... ,in For the swarm robot score, the Byzantine robot is excluded by Equation (15) to prevent the Byzantine robot from having an adverse effect on the identification of the Byzantine robot in the subsequent swarm robot system.

2. The blockchain-based method for integrating group robot identity authentication and task supervision as described in claim 1, characterized in that, In a blockchain-based swarm robot system, the robot's legitimate identity information includes the hash value of the robot's legitimate identity identifier. The legitimate blockchain address corresponding to the robot The hash value of a legitimate identity identifier With legitimate blockchain addresses One-to-one matching: the identity authentication requires that the robot's identity identifier and blockchain address are both valid and match.

3. The blockchain-based method for integrating group robot identity authentication and task supervision as described in claim 1, characterized in that, In the robot Before starting the exercise, send an identity authentication request and identity identifier to the identity authentication center. ; The identity authentication center received the robot Identity identifier At the same time, obtain the robot's blockchain address. The identity authentication center performs identity authentication to confirm the robot's identity. and blockchain address Whether it is legal and matches, and identify robots that fail to pass identity verification as Byzantine robots.

4. The blockchain-based method for integrating group robot identity authentication and task supervision as described in claim 1, characterized in that, During task execution, the robot periodically uploads its location information and task execution information to the task monitoring center; the task monitoring center updates the information at fixed intervals. The robot is scored based on its location information and task performance. The score reflects the robot's task completion rate. Robots with low scores are classified as Byzantine robots by the task monitoring center.

5. The blockchain-based method for integrating group robot identity authentication and task supervision according to claim 1, characterized in that, Identity Management Center Storage The hash value of the legitimate identity identifier of a legitimate robot. The legitimate blockchain address corresponding to the robot The hash value of a legitimate identity identifier With legitimate blockchain addresses One-to-one matching; robot legal identity identification The hash value is as shown in equation (1): in, For hash functions; Definition by The hash value of a robot's legitimate identity identifier and legitimate blockchain addresses The set , where n is the number of robots in the system, and i is the i-th robot in the system; During the blockchain network initialization phase, the identity management center will assign the hash value of the robot's legitimate identity identifier. and legitimate blockchain addresses The set Write to the identity authentication center; define two mappings Θ and Ξ in the smart contract as follows: in, It is a prime number Additive cyclic group of order 1 It is a constant. A bit string consisting of 0s and 1s of arbitrary length; Take constant The hash value of the robot's legitimate identity identifier and legitimate blockchain addresses Binding, as in equation (3): Each robot in the system has an identification identifier. and a blockchain address In the robot Before starting the exercise, send an identity authentication request and identity identifier to the identity authentication center. The identity authentication center received the robot. Identity identifier ,right Perform hashing as shown in equation (4): Meanwhile, the identity authentication center obtains robots. blockchain address Then the identity verification center confirms the robot's identity. and blockchain address Is it valid and does it match? The identity authentication result is defined as follows: Only robots The identity authentication result is Only after the robot passes identity verification is it allowed to join the group of robots to perform tasks.

Images