Autonomous clustering and time-sharing coordination control method for unmanned vehicle groups under speed constraints
By constructing kinematic models and establishing terminal time range estimation, combining communication network and acceleration control, autonomous clustering and time-sharing coordination of unmanned vehicles under speed constraints is achieved, and the terminal time coordination time in unmanned vehicles is solved The problem of unconstrained speed has the potential to apply in warehousing and transportation scenarios.
Patent Information
- Application Number
- CN202410376369.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-29
- Publication Date
- 2024-12-27
- Estimated Expiration
- 2044-03-29
AI Technical Summary
In the prior art, there are fewer methods for autonomous clustering and time-sharing coordination of multi-integrated groups, especially in unmanned vehicle groups. The speed magnitude is not constrained when terminal time coordination is coordinated, making it difficult to achieve effective time-sharing coordination and bursts. Condition handling.
A method of autonomous clustering and time-sharing coordination control for unmanned vehicles under speed constraints is proposed. By constructing kinematic models, establishing terminal time range estimation, building communication networks, performing autonomous clustering and acceleration control, ensuring that unmanned vehicles are in Pass through the target position in sequence within the specified speed range.
It realizes autonomous clustering and time-sharing coordination of unmanned vehicle groups to ensure that the speed is within the specified range, has application potential in warehousing and traffic scenarios, and can effectively deal with emergencies.
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Figure CN118192578B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of multi-agent coordinated control, and in particular relates to an autonomous clustering and time-sharing coordinated control method for a group of unmanned vehicles under speed constraints. Background Art
[0002] In a multi-moving body system, terminal time coordination control refers to coordinating the time for each moving body to pass or arrive at a key position. Time-sharing coordination control is a type of terminal time coordination control, which means coordinating the time of each moving body to have a certain time interval. In the field of multi-unmanned vehicle coordination control, time-sharing coordination control is a very critical issue. For example, when a group of unmanned vehicles performs a transportation task, the group of unmanned vehicles needs to pass through in time when the road section at the target location is relatively narrow. Existing research has proposed a series of coordination control methods based on the information interaction of multiple unmanned workshops. However, in some scenarios, due to the constraints of the initial speed, angle, etc. of the unmanned vehicle itself, it is difficult for the group of unmanned vehicles to achieve effective time-sharing coordination; or the unmanned vehicle has paid a lot of maneuverability when achieving the coordination task. When facing emergencies, such as obstacles and communication failures, the unmanned vehicle does not have enough adjustment ability to avoid these problems in time. Therefore, in a practical environment, a group of multi-unmanned vehicles needs to be autonomously clustered to ensure the effective completion of the time-sharing coordination task and retain a certain degree of maneuverability to deal with emergencies.
[0003] Currently, there is relatively little research on autonomous clustering of unmanned vehicle groups and time-sharing coordinated control within a cluster. The clustering problem usually focuses on the problem of coordinated attack on targets by drone swarms, and mainly considers constraints such as weapon consumption to establish a multi-target allocation model, such as the prior art [1] (see Zhao Y, Song Y, Zhang J, et al. Fuzzy game decision-making of unmanned aerial vehicles air-to-ground attack based on the particle swarm optimization integrating multiply strategies [J]. Control Theory Appl, 2019, 36 (10): 1644-1652.); the terminal time coordination problem usually uses the proportional guidance method to establish the remaining time estimation formula for the missile to hit the target and the cooperative error based on this, and achieves saturated target hit by controlling the cooperative error to zero, such as the prior art [2] (see Jialing Zhou and Jianying Yang. Distributed Guidance Law Design for Cooperative Simultaneous Attacks with Multiple Missiles [J]. Journal of Guidance, Control, and Dynamics, 2016, 39 (10): 2439-2447.). Most of the existing related multi-agent group clustering coordination technologies are studied for multi-target allocation, and no research on clustering of unmanned vehicle groups for a single target has been found. In terms of multi-agent terminal time coordination control, most of the existing technologies are aimed at achieving simultaneous arrival of the target location within the group, and research on the time-sharing passage of unmanned vehicle groups through the target location is still to be carried out. In addition, in existing research, the controlled object has no speed constraints during terminal time coordination, while in actual situations, unmanned vehicles are subject to their own state and speed constraints when performing terminal time coordination.
[0004] The present invention proposes a terminal time coordination control method for a speed-constrained unmanned vehicle group to autonomously cluster and for the unmanned vehicles in the cluster to pass the target in turn in time. There is no need to specify the cluster to which each unmanned vehicle in the unmanned vehicle group belongs, and the clustering control algorithm autonomously clusters according to the unmanned vehicle's own state and speed limit range; in addition, the method studies speed-constrained unmanned vehicles, and the speed cannot change suddenly and is specified within a certain range. For example, in a warehousing environment, a minimum speed for unmanned vehicles is specified to ensure efficiency, and unmanned vehicles have an upper speed limit due to their own limitations. Therefore, the method can be applied to tasks such as autonomous clustering and coordinated time-sharing of cargo robots in warehouses through stations, and batches of unmanned vehicles through narrow roads in traffic scenarios. It has good research value and application potential. Summary of the invention
[0005] The purpose of the present invention is to provide an autonomous clustering and time-sharing coordination control method for unmanned vehicle groups under speed constraints, so as to solve the problems existing in the prior art regarding autonomous clustering of multi-intelligent groups and time-sharing coordination within clusters, especially the problem that there are few control methods taking unmanned vehicles as the research object, and the problem that there is no speed constraint when coordinating the terminal time within the group in the prior art.
[0006] To achieve the above object, the present invention provides a method for autonomous clustering and time-sharing coordination control of an unmanned vehicle group under speed constraints, comprising the following steps:
[0007] Step 1: construct a kinematic model based on the relative motion relationship between the unmanned vehicle and the target position;
[0008] Step 2: Establish the terminal time range estimation of each unmanned vehicle based on the initial state and speed constraints;
[0009] Step 3: Build a communication network for the unmanned vehicle group so that each unmanned vehicle can obtain the terminal time range estimate of all unmanned vehicles in the group through data interaction;
[0010] Step 4: According to the lower bound of the terminal time range estimates of all unmanned vehicles in step 3, the unmanned vehicle group is assigned to pass through the target location sequence to obtain the adjusted terminal time range estimate of the unmanned vehicle;
[0011] Step 5: Based on the estimated time range of the adjusted terminal in step 4, autonomous clustering of the unmanned vehicle group is performed to obtain multiple unmanned vehicle clusters and to construct a communication network within each cluster;
[0012] Step 6: Based on the kinematic model in step 1, non-smooth control is used in each cluster to design the acceleration component control of the unmanned vehicle perpendicular to the line of sight.
[0013] Step 7, according to the kinematic model in step 1, the sequence of the unmanned vehicle group passing the target position in step 4, and the intra-cluster communication network in step 5, the time-sharing consistency error is calculated, and the acceleration component control of the unmanned vehicle along the line of sight is designed using non-smooth control;
[0014] Step 8: According to the acceleration component control perpendicular to the line of sight and along the line of sight designed in Step 6 and Step 7, the acceleration control of the unmanned vehicle in the tangential and normal directions is constructed;
[0015] Step 9: Update the position and speed information of the unmanned vehicle in real time, and adjust the acceleration control of the unmanned vehicle and the data interaction relationship with neighbors according to the real-time position relationship between the unmanned vehicle and the target position.
[0016] Preferably, the specific process of step 1 is as follows:
[0017] Assume that there is a group of n unmanned vehicles in the inertial coordinate system, and the kinematic model of unmanned vehicle i, i=1,···,n is as follows:
[0018]
[0019] Where (x i ,y i ) is the coordinate of the center of mass of unmanned vehicle i, γ i represents the heading angle of the unmanned vehicle i (i.e. the angle between the velocity direction and the x-axis, with the counterclockwise direction being positive), v i represents the speed of unmanned vehicle i, and They represent the tangential acceleration and normal acceleration of the unmanned vehicle i (i.e., the acceleration component of the unmanned vehicle in the velocity direction and the acceleration component perpendicular to the velocity direction);
[0020] Assume that the target position coordinates are (x * ,y * ), establish the relative kinematic model between the unmanned vehicle i and the target:
[0021]
[0022] in represents the relative distance between the unmanned vehicle i and the target position, θ i =arctan((y * -y i ) / (x * -x i )) represents the sight angle of unmanned vehicle i (i.e., the angle between the sight direction and the x-axis, the sight direction is the direction of the line connecting unmanned vehicle i and the target position, with the counterclockwise direction as positive), φ irepresents the leading angle (i.e. the angle between the speed direction of the unmanned vehicle i and the line of sight, with the counterclockwise direction being positive), represents the velocity component perpendicular to the line of sight, represents the velocity component along the line of sight;
[0023] According to the relative kinematic model between the unmanned vehicle and the target, the relationship model between the velocity component of the unmanned vehicle along the line of sight, the velocity component perpendicular to the line of sight and the acceleration control input is established:
[0024]
[0025] in represents the acceleration component control of the unmanned vehicle i along the line of sight, It is the acceleration component control of the unmanned vehicle i perpendicular to the line of sight.
[0026] Preferably, the specific process of establishing the terminal time range estimation of each unmanned vehicle based on the initial state and speed constraint in step 2 is as follows:
[0027] S21, numbering each unmanned vehicle in the unmanned vehicle group in a random order 1, 2, ···, n;
[0028] S22. Estimate the terminal time when each unmanned vehicle in the group passes the target location:
[0029]
[0030] The terminal time is the time when the unmanned vehicle passes the target position, the subscript i∈{1,2,···,n} represents the unmanned vehicle numbered i, and t0 represents the initial time;
[0031] S23, assuming that the speed range limit of the unmanned vehicle is [v min ,v max ], according to the speed constraint of the unmanned vehicle, the terminal time range estimate of the unmanned vehicle passing the target position at the initial moment is obtained:
[0032]
[0033] The speed constraint of each unmanned vehicle in the unmanned vehicle group is [v min ,v max ], and They are respectively the shortest time estimate and the longest time estimate required for the unmanned vehicle to reach the target position from the initial position.
[0034] Preferably, in step 3, the specific process of each unmanned vehicle obtaining the terminal time range estimation of all unmanned vehicles in the group through data interaction is as follows:
[0035] S31. Constructing an undirected topological graph The undirected topological graph is a connected graph; the undirected topological graph includes a vertex set Each vertex represents an unmanned vehicle, and also includes an edge set Used to describe the data exchange relationship between unmanned vehicles;
[0036] S32. Each unmanned vehicle obtains the terminal time range estimate of all unmanned vehicles in the group after performing n-1 data interactions with its neighbors based on the undirected topological graph, wherein in each data interaction, each unmanned vehicle receives the terminal range estimate of its neighbor and sends its own terminal time range estimate to the neighbor.
[0037] Preferably, in step 4, according to the lower bound of the terminal time range estimates of all unmanned vehicles, the specific process of allocating the sequence of the unmanned vehicle group passing through the target position and obtaining the adjusted terminal time range estimate of the unmanned vehicle is as follows:
[0038] S41, arrange the lower limits of the time ranges of the unmanned vehicle terminals in ascending order and renumber them 1, 2, ···, n so that the numbering satisfies Assume that the order in which the unmanned vehicle group passes the target position is the order after renumbering;
[0039] S42, according to the set time interval δ between adjacent unmanned vehicles passing the target location and the order in which the unmanned vehicle group passes the target location, based on the unmanned vehicle terminal time range estimate, an adjusted terminal time range estimate is obtained:
[0040]
[0041] and They are respectively used to adjust the lower bound of the terminal time range estimate and the upper bound of the time range estimate.
[0042] Preferably, in step 5, according to the estimation of the terminal time range, autonomous clustering of the unmanned vehicle group is performed to obtain multiple unmanned vehicle clusters and construct a communication network within each cluster as follows:
[0043] S51, let s = 1, indicating that the unmanned vehicle 1 is divided into the sth cluster, let p s =1, indicating the number of unmanned vehicles in the sth cluster, let m s =1, indicating the number of the first unmanned vehicle assigned to the sth cluster, let i = 2, and estimate the adjustment terminal time range of unmanned vehicle 1 Adjusted terminal time range estimation with unmanned vehicle i Take the intersection;
[0044] S52. If the intersection is not an empty set, the unmanned vehicle i is divided into the sth cluster and p is updated. s =p s+1, and update τ to adjust the intersection of the terminal time range estimate; if the intersection is an empty set, update s = s + 1, and update m s =i, and divide the unmanned vehicle i into the sth cluster, update p s = 1, and update τ to the estimated adjusted terminal time range of unmanned vehicle i;
[0045] S53: If all the unmanned vehicles in the group have not been traversed, then update i=i+1 and estimate the adjustment terminal time range of unmanned vehicle i The intersection τ of the estimated time range of the adjusted terminal is taken, and S52 is repeated until all the unmanned vehicles in the group are traversed;
[0046] S54, after clustering, each cluster is re-networked to construct a new communication network for each cluster, and the new communication network contains a directed spanning tree.
[0047] Preferably, in step 6, non-smooth control is used in each cluster to design the acceleration component control of the unmanned vehicle i perpendicular to the line of sight, so that the line of sight angular rate or lead angle of the unmanned vehicle converges to zero within a specified time. The specific expression is as follows:
[0048]
[0049] where κ i >0 is a constant, τ is a function of time, and the function τ(t) is
[0050]
[0051] Where ρ>2 is a constant, T is the specified convergence time, t1=t0+T∈(t0,∞) is a constant, is the first-order derivative function of τ(t), specifically:
[0052]
[0053] Preferably, the specific process of calculating the time-sharing consistency error in step 7 and using non-smooth control to design the acceleration component control of the unmanned vehicle along the line of sight direction is as follows:
[0054] S71. If the unmanned vehicle i does not pass the target position, the remaining time to pass the target position is estimated according to the current state of the unmanned vehicle i:
[0055]
[0056] The remaining time is the time interval from the current time to the time of passing the target position. If the unmanned vehicle i passes the target position, this moment is recorded as the passing time T i ; If t>T iWhen the unmanned vehicle i passes the target position, the remaining time is a negative number, and its absolute value represents the time after passing the target position, which is:
[0057]
[0058] S72, based on the intra-cluster communication network and the sequence of the unmanned vehicle group passing the target position, calculate the time-sharing passing consistency error ξ within the sth cluster s,i for
[0059]
[0060] where a ij represents the elements in the connection matrix in the communication topology. If the unmanned vehicle i can receive the information sent by the unmanned vehicle j, then a ij =1, otherwise a ij = 0, in particular, a ii =0, represents the remaining time estimate of autonomous vehicle j;
[0061] S73. Design the acceleration component control of the unmanned vehicle i along the line of sight as follows:
[0062]
[0063] where α i ,β i >0 is a constant, 0<k i <1<μ i is a constant, ξ s,i represents the time-sharing consistency error of the unmanned vehicle i in the sth cluster,
[0064] in The function is defined as
[0065]
[0066] is the component that limits the lower bound of the speed, specifically:
[0067]
[0068] in
[0069]
[0070] in is a constant, is the component that limits the upper limit of the speed, specifically:
[0071]
[0072] in
[0073]
[0074] in is a constant.
[0075] Preferably, the acceleration control of the unmanned vehicle in the tangential and normal directions in step 8 is constructed as follows:
[0076]
[0077] in
[0078]
[0079] Preferably, the specific process of adjusting the acceleration control of the unmanned vehicle and the data interaction relationship with the neighbors according to the real-time position relationship between the unmanned vehicle and the target position in step 9 is as follows:
[0080] S91, update the location and speed of each unmanned vehicle in real time to ensure the real-time information of the unmanned vehicle is transmitted during data interaction;
[0081] S92: According to the relationship between the real-time position of the unmanned vehicle i and the target position, if the unmanned vehicle i passes the target position, the data interaction with the neighbors in the new communication network of the cluster is stopped. And stop the designed acceleration control on the unmanned vehicle i; if the unmanned vehicle i does not pass the target position, return to step 7 and update the acceleration control.
[0082] Therefore, the present invention adopts the autonomous clustering and time-sharing coordination control method of the unmanned vehicle group under the above speed constraint, which has the following beneficial effects:
[0083] (1) The present invention can achieve a given target passing position and the time interval for unmanned vehicles in a group to pass through, without specifying in advance the cluster to which each unmanned vehicle belongs and the order in which the unmanned vehicles pass through the target position. The unmanned vehicle group can be autonomously clustered and the unmanned vehicles in the cluster can pass through the target position in turn at different times.
[0084] (2) The present invention proposes a control method to ensure that the speed of the unmanned vehicle does not exceed the speed constraint during its movement, so that the speed of the unmanned vehicle always remains within the specified speed range.
[0085] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0086] Figure 1 It is an overall flow chart of the autonomous clustering and time-sharing coordination control method of the unmanned vehicle group under speed constraints of the present invention;
[0087] Figure 2is a schematic diagram of the angle relationship between the unmanned vehicle of the present invention and the target position;
[0088] Figure 3 It is a topological structure diagram of the unmanned vehicle group communication network of the present invention;
[0089] Figure 4 It is a cluster sorting and cluster visualization diagram of the unmanned vehicle group simulated by the present invention;
[0090] Figure 5 It is the network topology structure of the unmanned vehicle cluster 1 simulated by the present invention;
[0091] Figure 6 It is the network topology structure of the unmanned vehicle cluster 2 simulated by the present invention;
[0092] Figure 7 It is a schematic diagram of the motion trajectory change of the unmanned vehicle group simulated by the present invention;
[0093] Figure 8 It is a schematic diagram of the distance between each unmanned vehicle and the target position point changing over time in the simulation of the present invention;
[0094] Fig. 9 It is a schematic diagram of the variation of the lead angle of each unmanned vehicle simulated by the present invention over time;
[0095] Fig.10 It is a schematic diagram of the speed of each unmanned vehicle simulated by the present invention changing with time;
[0096] Fig.11 It is a schematic diagram of the change of the speed of each unmanned vehicle along the line of sight with time simulated by the present invention;
[0097] Fig.12 It is a schematic diagram of the change of the speed of each unmanned vehicle in the vertical line of sight direction over time simulated by the present invention;
[0098] Fig.13 It is a schematic diagram of the change of the tangential acceleration of each unmanned vehicle simulated by the present invention over time;
[0099] Fig.14 It is a schematic diagram of the change of normal acceleration of each unmanned vehicle simulated by the present invention over time. DETAILED DESCRIPTION
[0100] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0101] See also Figure 1-14 , the autonomous clustering time-sharing coordination control method of unmanned vehicle groups under speed constraints, first of all, needs to initialize the problem scenario. Assume that the problem is to realize the autonomous clustering time-sharing coordination control task of n speed-constrained unmanned vehicle groups, set the time interval of passing adjacent unmanned vehicles in the same cluster to δ and the lead angle to specify the convergence time T. The specific process of the whole control method is as follows:
[0102] Step 1: Construct a kinematic model based on the relative motion relationship between the unmanned vehicle and the target position; the specific process is as follows:
[0103] like Figure 2 As shown in the figure, it is assumed that there is a group of n unmanned vehicles in the inertial coordinate system, and the kinematic model of unmanned vehicle i, i=1,···,n is specifically:
[0104]
[0105] Where (x i ,y i ) is the coordinate of the center of mass of unmanned vehicle i, γ i represents the heading angle of the unmanned vehicle i (i.e. the angle between the velocity direction and the x-axis, with the counterclockwise direction being positive), v i represents the speed of unmanned vehicle i, and They represent the tangential acceleration and normal acceleration of the unmanned vehicle i (i.e., the acceleration component of the unmanned vehicle in the velocity direction and the acceleration component perpendicular to the velocity direction);
[0106] Assume that the target position coordinates are (x * ,y * ), establish the relative kinematic model between the unmanned vehicle i and the target:
[0107]
[0108] in represents the relative distance between the unmanned vehicle i and the target position, θ i =arctan((y * -y i ) / (x * -x i )) represents the sight angle of unmanned vehicle i (i.e., the angle between the sight direction and the x-axis, the sight direction is the direction of the line connecting unmanned vehicle i and the target position, with the counterclockwise direction as positive), φ i represents the leading angle (i.e. the angle between the speed direction of the unmanned vehicle i and the line of sight, with the counterclockwise direction being positive), represents the velocity component perpendicular to the line of sight, represents the velocity component along the line of sight;
[0109] According to the relative kinematic model between the unmanned vehicle and the target, a relationship model between the velocity component of the unmanned vehicle along the line of sight, the velocity component perpendicular to the line of sight, and the acceleration control input is established (this model is established to clarify the relationship between the first-order derivative of the velocity component and the acceleration component):
[0110]
[0111] in represents the acceleration component control of the unmanned vehicle i along the line of sight, Control of the acceleration component of the unmanned vehicle i perpendicular to the line of sight;
[0112] Step 2: Establish the terminal time range estimation of each unmanned vehicle based on the initial state and speed constraints. The specific process is as follows:
[0113] S21, numbering each unmanned vehicle in the unmanned vehicle group in a random order 1, 2, ···, n;
[0114] S22. Estimate the terminal time when each unmanned vehicle in the group passes the target location:
[0115]
[0116] The terminal time is the time when the unmanned vehicle passes the target position, the subscript i∈{1,2,···,n} represents the unmanned vehicle numbered i, and t0 represents the initial time;
[0117] S23, assuming that the speed range limit of the unmanned vehicle is [v min ,v max ], according to the speed constraint of the unmanned vehicle, the terminal time range estimate of the unmanned vehicle passing the target position at the initial moment is obtained:
[0118]
[0119] The speed constraint of each unmanned vehicle in the unmanned vehicle group is [v min ,v max ], and They are the shortest and longest time estimates for the unmanned vehicle to reach the target location from the initial location;
[0120] Step 3: Build a communication network for the unmanned vehicle group so that each unmanned vehicle can obtain the terminal time range estimate of all unmanned vehicles in the group through data interaction. The specific process is as follows:
[0121] S31. Constructing an undirected topological graph The undirected topological graph is a connected graph; the undirected topological graph includes a vertex set Each vertex represents an unmanned vehicle, and also includes an edge set Used to describe the data exchange relationship between unmanned vehicles;
[0122] S32, each unmanned vehicle exchanges data with its neighbors n-1 times based on the undirected topology graph to obtain terminal time range estimates for all unmanned vehicles in the group, wherein in each data exchange, each unmanned vehicle receives the terminal range estimate of its neighbor and sends its own terminal time range estimate to the neighbor;
[0123] Step 4: According to the lower bound of the terminal time range estimates of all unmanned vehicles in step 3, the unmanned vehicle group is assigned to pass the target position sequence to obtain the adjusted terminal time range estimate of the unmanned vehicle. The specific process is as follows:
[0124] S41, arrange the lower limits of the time ranges of the unmanned vehicle terminals in ascending order and renumber them 1, 2, ···, n so that the numbering satisfies Assume that the order in which the unmanned vehicle group passes the target position is the order after renumbering;
[0125] S42, according to the set time interval δ between adjacent unmanned vehicles passing the target location and the order in which the unmanned vehicle group passes the target location, based on the unmanned vehicle terminal time range estimate, an adjusted terminal time range estimate is obtained:
[0126]
[0127] and They are respectively for adjusting the lower bound of the terminal time range estimate and adjusting the upper bound of the time range estimate;
[0128] Step 5: According to the estimated time range of the adjusted terminal in step 4, autonomous clustering of the unmanned vehicle group is performed to obtain multiple unmanned vehicle clusters and build a communication network within each cluster. The specific process is as follows:
[0129] S51, let s = 1, indicating that the unmanned vehicle 1 is divided into the sth cluster, let p s =1, indicating the number of unmanned vehicles in the sth cluster, let m s =1, indicating the number of the first unmanned vehicle assigned to the sth cluster, let i = 2, and estimate the adjustment terminal time range of unmanned vehicle 1 Adjusted terminal time range estimation with unmanned vehicle i Take the intersection;
[0130] S52. If the intersection is not an empty set, the unmanned vehicle i is divided into the sth cluster and p is updated. s =p s +1, and update τ to adjust the intersection of the terminal time range estimate; if the intersection is an empty set, update s = s + 1, and update m s =i, and divide the unmanned vehicle i into the sth cluster, update ps = 1, and update τ to the estimated adjusted terminal time range of unmanned vehicle i;
[0131] S53: If all the unmanned vehicles in the group have not been traversed, then update i=i+1 and estimate the adjustment terminal time range of unmanned vehicle i The intersection τ of the estimated time range of the adjusted terminal is taken, and S52 is repeated until all the unmanned vehicles in the group are traversed;
[0132] S54, after clustering, each cluster is re-networked to build a new communication network for each cluster, and the new communication network contains a directed spanning tree;
[0133] Step 6: According to the kinematic model in step 1, non-smooth control is used in each cluster to design the acceleration component control of the unmanned vehicle perpendicular to the line of sight, so that the line of sight angular rate or lead angle of the unmanned vehicle converges to zero within a specified time. The specific expression is as follows:
[0134]
[0135] where κ i >0 is a constant, τ is a function of time, and the function τ(t) is
[0136]
[0137] Where ρ>2 is a constant, T is the specified convergence time, t1=t0+T∈(t0,∞) is a constant, is the first-order derivative function of τ(t), specifically:
[0138]
[0139] Step 7: According to the kinematic model in step 1, the sequence of the unmanned vehicle group passing the target position in step 4, and the intra-cluster communication network in step 5, the time-sharing consistency error is calculated, and the acceleration component control of the unmanned vehicle along the line of sight is designed using non-smooth control. The specific process is as follows:
[0140] S71. If the unmanned vehicle i does not pass the target position, the remaining time to pass the target position is estimated according to the current state of the unmanned vehicle i:
[0141]
[0142] The remaining time is the time interval from the current time to the time of passing the target position. If the unmanned vehicle i passes the target position, this moment is recorded as the passing time T i ; If t>T i When the unmanned vehicle i passes the target position, the remaining time is a negative number, and its absolute value represents the time after passing the target position, which is:
[0143]
[0144] S72, based on the intra-cluster communication network and the sequence of the unmanned vehicle group passing the target position, calculate the time-sharing passing consistency error ξ within the sth cluster s,i for
[0145]
[0146] where a ij represents the elements in the connection matrix in the communication topology. If the unmanned vehicle i can receive the information sent by the unmanned vehicle j, then a ij =1, otherwise a ij = 0, in particular, a ii =0, represents the remaining time estimate of autonomous vehicle j;
[0147] S73. Design the acceleration component control of the unmanned vehicle i along the line of sight as follows:
[0148]
[0149] where α i ,β i >0 is a constant, 0<k i <1<μ i is a constant, ξ s,i represents the time-sharing consistency error of the unmanned vehicle i in the sth cluster,
[0150] in The function is defined as
[0151]
[0152] is the component that limits the lower bound of the speed, specifically:
[0153]
[0154] in
[0155]
[0156] in is a constant, is the component that limits the upper limit of the speed, specifically:
[0157]
[0158] in
[0159]
[0160] in is a constant;
[0161] Step 8: Based on the acceleration component control perpendicular to the line of sight and along the line of sight designed in steps 6 and 7, the acceleration control of the unmanned vehicle in the tangential and normal directions is constructed as follows:
[0162]
[0163] in
[0164]
[0165] Step 9: Update the position and speed information of the unmanned vehicle in real time, and adjust the acceleration control of the unmanned vehicle and the data interaction relationship with the neighbors according to the real-time position relationship between the unmanned vehicle and the target position. The specific process is as follows:
[0166] S91, update the location and speed of each unmanned vehicle in real time to ensure the real-time information of the unmanned vehicle is transmitted during data interaction;
[0167] S92: According to the relationship between the real-time position of the unmanned vehicle i and the target position, if the unmanned vehicle i passes the target position, the data interaction with the neighbors in the new communication network of the cluster is stopped. And stop the designed acceleration control on the unmanned vehicle i; if the unmanned vehicle i does not pass the target position, return to step 7 and update the acceleration control.
[0168] Example
[0169] The method designed by the present invention is verified by Matlab software simulation.
[0170] The virtual simulation simulates the autonomous clustering and time-sharing passing of 8 unmanned vehicles within the cluster. The initial state of each unmanned vehicle is shown in Table 1:
[0171] Table 1
[0172]
[0173] The target position, initial time, time interval of time-sharing tasks and speed constraints of the unmanned vehicle group that need to pass are shown in Table 2:
[0174] Table 2
[0175]
[0176] The topology diagram of the unmanned vehicle group communication network is as follows: Figure 3 As shown, the network topology satisfies the condition of being an undirected connected graph.
[0177] In the virtual simulation experiment, the terminal time range estimation obtained according to the initial state and speed constraint of the unmanned vehicle, as well as the cluster sequence number and the adjusted terminal time range estimation obtained based on this are shown in Table 3:
[0178] Table 3
[0179]
[0180] After the autonomous clustering of the unmanned vehicle group, two clusters are obtained. The process of cluster sorting and clustering according to the clustering time range is as follows: Figure 4 As shown, the communication network structure of cluster 1 is as follows Figure 5 As shown, the communication network structure of cluster 2 is as follows Figure 6 As shown, the network topologies of the two clusters are both directed topologies, and both have directed spanning trees.
[0181] In addition, the parameters of the acceleration component control law in the experiment are as follows: κ i =0.3,ρ=2.5,α i =0.5,β i =0.5, k i =0.6, μ i =1.2, where the specified convergence time in the vertical line of sight direction for cluster one is T=3.5S, and the specified convergence time in the vertical line of sight direction for cluster two is T=5s.
[0182] The simulation results of autonomous clustering and time-sharing coordinated control of unmanned vehicle groups with speed constraints are shown in Figure 2. Figures 7 to 13 shown.
[0183] Figure 7 It indicates that the unmanned vehicle group successfully passed the target position. Figure 8 It can be seen from Table 4 that the unmanned vehicle group is successfully divided into two clusters, and the unmanned vehicles in the cluster pass the target position at the specified time interval, and the error of passing the target position in the cluster does not exceed 0.01s. According to the virtual simulation results, the control method designed in this patent can effectively achieve the design requirements and perform well in completing the task.
[0184] Table 4
[0185]
[0186] according to Fig. 9 It can be seen that the consensus of the lead angle of the unmanned vehicle in cluster 1 and cluster 2 converges to zero at the specified time and remains until it passes the target position. Fig.10 It can be seen that the total speed of the unmanned vehicle does not exceed the speed constraint and quickly converges to a stable constant speed. Fig.11 This indicates that the speed along the line of sight quickly converges to a stable constant speed and is maintained until it passes the target position. Fig.12It means that the speed in the vertical line of sight direction converges to zero within a specified time, and it is not easy to collide with the target position when passing through the target position. Fig.13 and Fig.14 This shows that the acceleration components along the velocity direction and the direction perpendicular to the velocity are bounded and converge to zero quickly.
[0187] Summarizing the above virtual simulation experiments, the autonomous clustering and time-sharing coordinated control method of the unmanned vehicle group under speed constraints of the present invention has a good control effect.
[0188] Therefore, the present invention adopts the above-mentioned method of autonomous clustering and time-sharing coordinated control of unmanned vehicle groups under speed constraints. There is no need to specify the cluster to which each unmanned vehicle in the unmanned vehicle group belongs. The clustering control algorithm performs autonomous clustering according to the unmanned vehicle's own state and speed limit range. In addition, this method studies speed-constrained unmanned vehicles, and the speed cannot change suddenly and is specified within a certain range. In addition, in order to ensure efficiency, the minimum speed of the unmanned vehicle is specified, and the unmanned vehicle has an upper speed limit due to its own limitations. It can be applied to tasks such as autonomous clustering and coordinated time-sharing of cargo robots in warehouses to pass through stations, and unmanned vehicles passing through narrow roads in batches in traffic scenarios. It has good research value and application potential.
[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.
Claims
1. The autonomous clustering and time-sharing coordination control method of unmanned vehicle groups under speed constraints is characterized by: The following steps are involved: Step 1: construct a kinematic model based on the relative motion relationship between the unmanned vehicle and the target position; Step 2: Establish the terminal time range estimation of each unmanned vehicle based on the initial state and speed constraints; Step 3: Build a communication network for the unmanned vehicle group so that each unmanned vehicle can obtain the terminal time range estimate of all unmanned vehicles in the group through data interaction; Step 4: According to the lower bound of the terminal time range estimates of all unmanned vehicles in step 3, the unmanned vehicle group is assigned to pass through the target location sequence to obtain the adjusted terminal time range estimate of the unmanned vehicle; Step 5: Based on the estimated time range of the adjusted terminal in step 4, autonomous clustering of the unmanned vehicle group is performed to obtain multiple unmanned vehicle clusters and to construct a communication network within each cluster; Step 6: Based on the kinematic model in step 1, non-smooth control is used in each cluster to design the acceleration component control of the unmanned vehicle perpendicular to the line of sight. Step 7, according to the kinematic model in step 1, the sequence of the unmanned vehicle group passing the target position in step 4, and the intra-cluster communication network in step 5, the time-sharing consistency error is calculated, and the acceleration component control of the unmanned vehicle along the line of sight is designed using non-smooth control; Step 8: According to the acceleration component control perpendicular to the line of sight and along the line of sight designed in Step 6 and Step 7, the acceleration control of the unmanned vehicle in the tangential and normal directions is constructed; Step 9: Update the position and speed information of the unmanned vehicle in real time, and adjust the acceleration control of the unmanned vehicle and the data interaction relationship with neighbors according to the real-time position relationship between the unmanned vehicle and the target position; The specific process of step 1 is as follows: Assume that there is a group of n unmanned vehicles in the inertial coordinate system. , The specific kinematic model is: ； in For driverless cars The coordinates of the center of mass position, Indicates unmanned vehicle The heading angle, Indicates unmanned vehicle The speed size, and Respectively represent unmanned vehicles The tangential acceleration and normal acceleration of Assume that the target position coordinates are , building an unmanned vehicle Relative kinematic model with the target: ； in Indicates unmanned vehicle The relative distance to the target location, Indicates unmanned vehicle The sight angle, represents the leading angle, represents the velocity component perpendicular to the line of sight, represents the velocity component along the line of sight; The specific process of establishing the terminal time range estimation of each unmanned vehicle based on the initial state and speed constraints in step 2 is as follows: S21. Number each unmanned vehicle in the unmanned vehicle group in random order ; S22. Estimate the terminal time when each unmanned vehicle in the group passes the target location: ； The terminal time is the time when the unmanned vehicle passes the target position. represents the unmanned vehicle numbered i, represents the initial moment; S23, assuming that the speed range limit of the unmanned vehicle is , according to the speed constraint of the unmanned vehicle, the terminal time range estimate of the unmanned vehicle passing the target position at the initial moment is obtained: ； The speed constraint of each unmanned vehicle in the unmanned vehicle group is , and They are the shortest and longest time estimates for the unmanned vehicle to reach the target location from the initial location; In step 5, according to the estimated time range of the adjusted terminal, the unmanned vehicle group is autonomously clustered to obtain multiple unmanned vehicle clusters and to construct the communication network within each cluster. The specific process is as follows: S51, let s = 1, indicating that the unmanned vehicle 1 is divided into the sth cluster, let , represents the number of unmanned vehicles in the sth cluster, let , represents the number of the first unmanned vehicle classified into the sth cluster, let i=2, and estimate the adjusted terminal time range of unmanned vehicle 1 Adjusted terminal time range estimation with unmanned vehicle i Take the intersection; S52. If the intersection is not an empty set, the unmanned vehicle i is divided into the sth cluster and the update , while updating To adjust the intersection of the terminal time range estimates; if the intersection is an empty set, update s=s+1, update , and will unmanned vehicles Divide into cluster s, update , while updating For driverless cars Adjustment of terminal time range estimates; S53, if all the unmanned vehicles in the group have not been traversed, then update i=i+1 and move the unmanned vehicle Adjustment of terminal time range estimates Intersection with adjustment of terminal time range estimate Take the intersection and repeat S52 until all the unmanned vehicles in the group are traversed; S54, after clustering, each cluster is re-networked to build a new communication network for each cluster, and the new communication network contains a directed spanning tree; In step 6, each cluster uses non-smooth control to design the unmanned vehicle The acceleration component perpendicular to the line of sight is controlled so that the line of sight angular rate or lead angle of the unmanned vehicle converges to zero within a specified time. The specific expression is as follows: ； in is a constant, is a function of time, for ； in is a constant, is the specified convergence time set. is a constant, for The first-order derivative function is: ； The specific process of calculating the time-sharing consistency error in step 7 and using non-smooth control to design the acceleration component control of the unmanned vehicle along the line of sight is as follows: S71. If there is no one in the car Failed to pass the target location, according to the unmanned vehicle The remaining time from the current state to the target position is expressed as: ； The remaining time is the time interval from the current time to the time of passing the target location. Passing the target position, record this moment as passing time ;like Remember the driverless car After passing the target position, the remaining time is a negative number, and its absolute value indicates the time after passing the target position, specifically: ； S72, based on the intra-cluster communication network and the sequence of the unmanned vehicle group passing the target position, calculate the time-sharing passing consistency error within the sth cluster for ； in Represents the elements in the connection matrix in the communication topology. If the unmanned vehicle After receiving the information sent by unmanned vehicle j, ,otherwise , in particular, , represents the remaining time estimate of autonomous vehicle j; S73. Design of driverless cars The acceleration component along the line of sight is controlled as: ； in is a constant, is a constant, Indicates the number of unmanned vehicles in the sth cluster i The time-sharing consistency error is in The function is defined as ； is the component that limits the lower bound of the speed, specifically: ； in ； in is a constant, is the component that limits the upper limit of the speed, specifically: ； in ； in is a constant; In step 8, the acceleration control of the unmanned vehicle in the tangential and normal directions is constructed as follows: ； in ； The specific process of adjusting the acceleration control of the unmanned vehicle and the data interaction relationship with the neighbors according to the real-time position relationship between the unmanned vehicle and the target position in step 9 is as follows: S91, update the location and speed of each unmanned vehicle in real time to ensure the real-time information of the unmanned vehicle is transmitted during data interaction; S92, based on unmanned vehicles The relationship between the real-time position and the target position is judged. If the unmanned vehicle After passing the target location, the data interaction with the neighbors in the new communication network of the cluster stops. , and stop the acceleration control designed for the unmanned vehicle If the driverless car If the target position is not passed, return to step 7 to update the acceleration control.
2. The autonomous clustering and time-sharing coordination control method for unmanned vehicle groups under speed constraints according to claim 1 is characterized in that: According to the relative kinematic model between the unmanned vehicle and the target, the relationship model between the velocity component of the unmanned vehicle along the line of sight, the velocity component perpendicular to the line of sight and the acceleration control input is established: ； in Indicates unmanned vehicle Control of the acceleration component along the line of sight, For driverless cars Control of the acceleration component perpendicular to the line of sight.
3. The autonomous clustering and time-sharing coordination control method for unmanned vehicle groups under speed constraints according to claim 2 is characterized in that: In step 3, the specific process of each unmanned vehicle obtaining the terminal time range estimate of all unmanned vehicles in the group through data interaction is as follows: S31. Constructing an undirected topological graph , the undirected topological graph is a connected graph; the undirected topological graph includes a vertex set , where each vertex represents an unmanned vehicle and also includes an edge set , used to describe the data exchange relationship between unmanned vehicles; S32, each unmanned vehicle communicates with its neighbors based on the undirected topological graph After data interactions, the terminal time range estimates of all unmanned vehicles in the group are obtained. In each data interaction, each unmanned vehicle receives the terminal range estimate of its neighbor and sends its own terminal time range estimate to the neighbor.
4. The autonomous clustering and time-sharing coordination control method for unmanned vehicle groups under speed constraints according to claim 3 is characterized in that: In step 4, according to the lower bound of the terminal time range estimates of all unmanned vehicles, the specific process of allocating the sequence of the unmanned vehicle group passing through the target position and obtaining the adjusted terminal time range estimate of the unmanned vehicle is as follows: S41. Arrange the lower limits of the time ranges of the unmanned vehicle terminals in ascending order and renumber them , so that the numbering is satisfied , let the order in which the unmanned vehicle group passes the target position be the order after renumbering; S42: according to the set time interval between adjacent unmanned vehicles passing the target location The unmanned vehicle group obtains the adjusted terminal time range estimate based on the unmanned vehicle terminal time range estimate through the target position sequence: ； and They are respectively used to adjust the lower bound of the terminal time range estimate and the upper bound of the time range estimate.