CN110162035B - Cooperative motion method of cluster robot in scene with obstacle - Google Patents
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Abstract
The invention relates to the technical field of robots, in particular to a cooperative motion method of a robot cluster in a scene with an obstacle. The method provided by the invention is used for realizing formation change, dynamic and static obstacle avoidance and cooperative motion of multiple robots in a scene with obstacles. According to the method, cooperative control of the clustered robots in a complex environment is defined as a distributed model predictive control problem, each robot independently solves an optimization equation according to information of local neighbors, and the method has good robustness and flexibility. Meanwhile, the method provides a formation diagram concept, the formation of the robot is represented by a directed graph, and on the premise of ensuring the connectivity of the graph, the local transformation of the expected formation is realized by changing the edges and the weights of the formation diagram, so that the connectivity and the flexibility of the cluster can be ensured simultaneously. In addition, the method provided by the patent has a hierarchical structure, and by adopting the hierarchical structure, the coupling between modules can be reduced, and the algorithm design and expansion are facilitated.
Description
Technical Field
The invention relates to the technical field of swarm intelligence and robot control, in particular to a coordinated movement method of a swarm robot in a scene with an obstacle.
Background
With the continuous development of robot technology, automated robot systems play an important role in more and more fields, such as industrial robots, household robots, service robots, and the like. Therefore, automatic navigation of robots in structured environments has been a popular area of research. Compared with the conventional human power system, the robot system has various advantages such as economy, time and safety. Compared with a single robot system, the multi-robot system has the advantages of being capable of cooperatively executing tasks and wider application scenarios, such as exploration in dangerous environments, monitoring in agriculture and military tasks under extreme conditions. With the same complex task, the multi-robot system can be completed more efficiently and with lower cost than a single robot, and has better fault tolerance (the operation of the whole system cannot be influenced when one robot fails), self-adaptation and flexibility. Because of these advantages of multi-robot systems and the ever-increasing computing power of contemporary processors, multi-mobile robot systems have attracted increasing attention from both domestic and foreign learners in recent years.
Formation control refers to the control problem that a team of multiple robots maintain a predetermined geometry (i.e., formation) with respect to each other while moving towards a specific target or direction, while accommodating environmental constraints (e.g., avoiding obstacles). The multi-robot formation control needs to solve the following problems:
(1) how the robot determines its desired position in the formation;
(2) how the robot determines the actual position of the robot in the formation;
(3) how the robot moves to maintain formation;
(4) how the robot can cope when encountering an obstacle.
In the prior art, many methods are proposed to solve the above problems, including a navigator-follower algorithm, a virtual structure method, a behavior-based method, an artificial potential field method, and an optimization-based method. However, in the scene with obstacles, the robot cluster cannot move in a fixed formation, and the method is not fully applicable. In a constrained environment, the robot cluster should adaptively change the desired formation according to the environmental information detected by the sensor, and how to select a new formation and how to implement formation switching is a challenging problem, which is more complicated for the cluster with a distributed architecture.
Disclosure of Invention
The invention provides a coordinated movement method of a cluster robot in a scene with obstacles to overcome at least one defect in the prior art, and provides a local formation transformation method for controlling the coordinated movement of the cluster robot in the scene with obstacles.
In order to solve the technical problems, the invention adopts the technical scheme that: a coordinated movement method of clustered robots in a scene with obstacles comprises the following steps:
s1, before a robot cluster starts to move, manually setting a key path point or drawing an expected path through a planning and calculation rule for a navigator of the cluster to follow, realizing cooperative movement of the whole cluster by a follower through the following of the navigator, and expressing an expected formation and a relationship between robots by using a formation graph;
s2, detecting and sensing obstacles in the surrounding environment and state information of a non-neighbor robot in real time by using an airborne sensor in the moving process of the robot;
s3, each robot issues own position and speed information through a local area network, meanwhile, the position and speed information of the adjacent robot is obtained, and the weight values of the edges and the edges in the formation diagram are changed according to the state of the adjacent robot so as to implement mutual collision avoidance between the robots;
s4, using the information obtained in the steps S2 and S3 as the input of a distributed MPC (model predictive control) algorithm, solving an optimization problem to obtain a control sequence and a state sequence, selecting a first control quantity as the optimal control quantity of the robot at the current moment, inputting the optimal control quantity to act on the robot, and driving the robot to reach an expected target point;
s5, repeating the steps S2 to S4 until reaching the end point.
Further, in the step S1, the robot cluster is modeled by using graph theory, and for the cluster with n robots, a formation graph is used
To represent the cluster relationships,
is a set of vertexes representing robots, is a set of directed edges representing the direction of information flow between robots,
indicating the desired distance between the robots,
representing the desired relative azimuth of the robot.
In the coordinated movement task of the clustered robots, each robot needs to determine its desired position according to the positions of neighbors. According to the navigator-follower algorithm, the follower can determine its position in the fleet relative to other robots by two methods, the first being a distance-orientation controller and the second being a distance-distance controller. For a cluster of n robots, a team chart is used in the invention
To represent the cluster relationships,
is a set of vertexes representing robots, is a set of directed edges representing the direction of information flow between robots,
indicating the desired distance between the robots,
representing the desired relative azimuth of the robot. The formation graph is a directed graph and comprises three elements of nodes, edges and distances, wherein the nodes represent the robot individuals, the edges represent the neighbor relations, namely the information flow direction, and the distances represent the expected distances among the neighbors.
Further, the weight change of the edge in the step S3 specifically includes the following steps:
s311, adding a residual error item of the distance between the robots and the expected distance in the model prediction controller to realize formation maintenance among the robots, wherein the residual error item is expressed as:
fixed weight ωijThe robot can have good distance keeping performance, but the robot can lack flexibility when passing through an obstacle environment, so the invention proposes to adaptively change the formation weight omegaijA method of a parameter;
s312, for the ith robot and the jth robot, defining the safety distance margin as follows:
lij＝||pi-pj||-2r
wherein p isiAnd pjRespectively representing the ith and jth robot positions, r being the radius of the robot due to hard constraints
Can guarantee lijAnd (3) more than or equal to 0, and deriving the time by the formula to obtain the distance change rate of the ith robot and the jth robot as follows:
therefore, for the jth robot, the collision time with the ith robot can be obtained as follows:
tij＝lij/lij
time of collision tijDescribing the time urgency of the collision of the two robots, and the size and the direction of the collision time can be used for determining the collision condition between the two robots; when t isijWhen 0, it means lijWhen the collision between the two robots is more likely to happen, the situation that we should avoid is shown as 0; when t isij> 0, means lijWhen the distance between the two robots is larger than 0, the distance between the two robots is gradually larger, the two robots are far away, and t isijThe larger the distance between the two robots is. When t isij< 0, meaning lij< 0, this time indicating the distance margin between the two robotsAt a gradual decrease, two robots are approaching, tijThe smaller the absolute value of (a), the faster the two robots approach. So the time of collision tijDescribing the time urgency of a collision of two robots;
s313, if the ith robot and the jth robot are in a formation graph
Are adjacent and the collision time t of the two robotsijWhen < 0 and the absolute value is small, the corresponding weight parameter omegaijShould be increased; ω can be represented by a zero-mean Gaussian density functionijThe change of (2):
where k > 0 is the peak of the Gaussian density function, σ2The smaller the degree of sensitivity to collision time, the smaller the2Such that the weight parameter omegaijThe more sensitive the change in.
Further, in order to maintain the stability of the formation and reduce the frequency of weight adjustment, the adaptive weight change is triggered only when the neighbor enters the dangerous range of the robot.
Further, in the step S3, the method for local formation transformation specifically includes the following steps:
s321, defining the distortion coefficient of the distance of the jth robot as follows:
wherein s isij∈SfIs the expected distance of the jth robot relative to the ith robot, dijis the actual distance between the jth robot and the ith robot, when etaijwhen the distance between the two robots is equal to 0, the distance between the two robots is exactly equal to the expected distance, and when eta is equal to 0ijwhen the current position is greater than 0, the ith robot has traction to the jth robot, and when eta is greater than 0ijIf < 0, the i-th device is describedThe robot will have a repulsive force to the jth robot;
s322. if in the formation graph, there is a directed edge (v)i,vj) And (v)k,vj) meaning that the jth robot is required to maintain a desired distance from both the ith and kth robots, when ηij< 0 and ηkj＞ηthresholdIn this case, the jth robot needs to delete the directed edge (v)k,vj) The requirement of keeping a desired distance from the kth robot is removed, and the motion constraint on the jth robot is relaxed, so that the jth robot can find a feasible solution in a constraint environment more easily;
s323, when the jth robot observes a non-neighbor robot in a perception range through a sensor, if the jth robot observes the jth robot and the jth robot is not a neighbor member of the jth robot in the formation graph, the collision time t obtained through observation calculation is setuj< 0, which means that for the jth robot, which was not previously a neighbor to it, there is a robot approaching it, in which case the jth robot is kept a distance from the uth robot to avoid collision, there will be a directed edge (v)u,vj) Adding the data to the original formation topological structure chart and correspondingly setting the expected distance sujAnd adding the distance data into the original expected distance set, thereby changing the original formation diagram of the clustered robots.
The method provided by the invention is used for realizing formation change, dynamic and static obstacle avoidance and cooperative motion of multiple robots in a scene with obstacles. According to the method, cooperative control of the clustered robots in a complex environment is defined as a distributed model predictive control problem, each robot independently solves an optimization equation according to information of local neighbors, and the method has good robustness and flexibility. Meanwhile, the method provides a formation diagram concept, the formation of the robot is represented by a directed graph, and on the premise of ensuring the connectivity of the graph, the local transformation of the expected formation is realized by changing the edges and the weights of the formation diagram, so that the connectivity and the flexibility of the cluster can be ensured simultaneously. In addition, the method proposed in this patent has a hierarchical structure: the path planning layer, the formation decision layer and the formation keeping layer adopt the hierarchical structure, can reduce the coupling among the modules, and are convenient for algorithm design and expansion
Compared with the prior art, the beneficial effects are:
1. expanding a single robot path planning algorithm to a multi-robot system; in the robot cluster, a navigator is defined, the navigator executes the functions of path planning and navigation, and the other robots (followers) execute the functions of formation keeping and obstacle avoidance, so that the path planning and navigation of the robot cluster are realized. Therefore, the path planning algorithm is decoupled from the formation control algorithm, so that various algorithms for path planning of the single robot can be adopted;
2. a hierarchical system structure is designed; under a constrained environment, the robot cluster needs to adaptively change the formation according to the environment; the coupling among the modules can be reduced, and the algorithm design and expansion are facilitated;
3. providing a self-adaptive formation transformation algorithm; the robot dynamically updates the edges and the weights of the edges in the formation graph in real time according to the state information of the local neighbors, so that the local transformation of the formation of the robot is realized, and the transformation of the whole expected formation is not involved.
Drawings
FIG. 1 is an overall process flow diagram of the present invention.
Fig. 2 is a schematic diagram of a formation diagram of the present invention.
Fig. 3 is a flow chart of the partial formation transformation method of the present invention.
Fig. 4 is an effect diagram of the partial formation transformation method of the present invention.
Detailed Description
The drawings are for illustration purposes only and are not to be construed as limiting the invention; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the invention.
As shown in fig. 1, a coordinated movement method of clustered robots in a complex scene includes the following steps:
In the coordinated movement task of the clustered robots, each robot needs to determine its desired position according to the positions of neighbors. Modeling the robot cluster by using a graph theory method; for a cluster of n robots, using a formation graph
To represent the cluster relationships,
is a set of vertexes representing robots, is a set of directed edges representing the direction of information flow between robots,
indicating the desired distance between the robots,
representing the desired relative azimuth of the robot as shown in figure 2.
wherein, the weight change of the edge specifically comprises the following steps:
s311, adding a residual error item of the distance between the robots and the expected distance in the model prediction controller to realize formation maintenance among the robots, wherein the residual error item is expressed as:
fixed weight ωijThe robot can have good distance keeping performance, but the robot can lack flexibility when passing through an obstacle environment, so the invention proposes to adaptively change the formation weight omegaijA method of a parameter;
s312, for the ith robot and the jth robot, defining the safety distance margin as follows:
lij＝||pi-pj||-2r
wherein p isiAnd pjRespectively representing the ith and jth robot positions, r being the radius of the robot due to hard constraints
Can guarantee lijAnd (3) more than or equal to 0, and deriving the time by the formula to obtain the distance change rate of the ith robot and the jth robot as follows:
therefore, for the jth robot, the collision time with the ith robot can be obtained as follows:
tij＝lij/lij
time of collision tijDescribing the time urgency of the collision of the two robots, and the size and the direction of the collision time can be used for determining the collision condition between the two robots; when t isijWhen 0, it means lijWhen the collision between the two robots is more likely to happen, the situation that we should avoid is shown as 0; when t isij> 0, means lijIs greater than 0, which indicates that the distance margin between the two robots is gradually increased,two robots are moving away, tijThe larger the distance between the two robots is. When t isij< 0, meaning lij< 0, this time it means that the distance margin between the two robots is gradually decreasing, the two robots are approaching, tijThe smaller the absolute value of (a), the faster the two robots approach. So the time of collision tijDescribing the time urgency of a collision of two robots;
s313, if the ith robot and the jth robot are in a formation graph
Are adjacent and the collision time t of the two robotsijWhen < 0 and the absolute value is small, the corresponding weight parameter omegaijShould be increased; ω can be represented by a zero-mean Gaussian density functionijThe change of (2):
where k > 0 is the peak of the Gaussian density function, σ2The smaller the degree of sensitivity to collision time, the smaller the2Such that the weight parameter omegaijThe more sensitive the change in.
In order to maintain the stability of the formation and reduce the frequency of weight adjustment, a trigger condition is set for the adaptive weight transformation, and when a neighbor enters a dangerous range (dangerous range > safe range > radius) of the robot, the adaptive weight is triggered to change.
In addition, the method for local formation transformation specifically comprises the following steps:
s321, defining the distortion coefficient of the distance of the jth robot as follows:
wherein s isij∈SfIs the expected distance of the jth robot relative to the ith robot, dijIs the j robot and the i machineactual distance of the robot, whenijwhen the distance between the two robots is equal to 0, the distance between the two robots is exactly equal to the expected distance, and when eta is equal to 0ijwhen the current position is greater than 0, the ith robot has traction to the jth robot, and when eta is greater than 0ijIf the number is less than 0, the ith robot can have repulsive force to the jth robot;
s322. if in the formation graph, there is a directed edge (v)i,vj) And (v)k,vj) meaning that the jth robot is required to maintain a desired distance from both the ith and kth robots, when ηij< 0 and ηkj＞ηthresholdIn this case, the jth robot needs to delete the directed edge (v)k,vj) The requirement of keeping a desired distance from the kth robot is removed, and the motion constraint on the jth robot is relaxed, so that the jth robot can find a feasible solution in a constraint environment more easily;
s323, when the jth robot observes a non-neighbor robot in a perception range through a sensor, if the jth robot observes the jth robot and the jth robot is not a neighbor member of the jth robot in the formation graph, the collision time t obtained through observation calculation is setuj< 0, which means that for the jth robot, which was not previously a neighbor to it, there is a robot approaching it, in which case the jth robot is kept a distance from the uth robot to avoid collision, there will be a directed edge (v)u,vj) Adding the data to the original formation topological structure chart and correspondingly setting the expected distance sujAnd adding the distance data into the original expected distance set, thereby changing the original formation diagram of the clustered robots.
And 4, taking the information obtained in the step 2 and the step 3 as the input of a distributed MPC algorithm, obtaining a control sequence and a state sequence by solving an optimization problem, selecting a first control quantity as the optimal control quantity of the robot at the current moment, inputting the optimal control quantity to act on the robot, and driving the robot to reach an expected target point. The overall flow is shown in fig. 3, and the specific implementation effect is shown in fig. 4.
And 5, repeating the steps 2 to 4 until the end point is reached.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (4)
1. A coordinated movement method of clustered robots in a scene with obstacles is characterized by comprising the following steps:
s1, before a robot cluster starts to move, manually setting a key path point or drawing an expected path through a planning and calculation rule for a navigator of the cluster to follow, realizing cooperative movement of the whole cluster by a follower through the following of the navigator, and expressing an expected formation and a relationship between robots by using a formation graph;
s2, detecting and sensing obstacles in the surrounding environment and state information of a non-neighbor robot in real time by using an airborne sensor in the moving process of the robot;
s3, each robot issues own position and speed information through a local area network, meanwhile, the position and speed information of the adjacent robot is obtained, and the weight values of the edges and the edges in the formation diagram are changed according to the state of the adjacent robot so as to implement mutual collision avoidance between the robots; wherein the weight change specifically comprises the following steps:
s311, adding a residual error item of the distance between the robots and the expected distance in the model prediction controller to realize formation maintenance among the robots, wherein the residual error item is expressed as:
s312, for the ith robot and the jth robot, defining the safety distance margin as follows:
lij＝||pi-pj||-2r
wherein p isiAnd pjRespectively representing the ith and jth robot positions, r being the radius of the robot due to hard constraints
Can guarantee lijAnd (3) more than or equal to 0, and deriving the time by the formula to obtain the distance change rate of the ith robot and the jth robot as follows:
therefore, for the jth robot, the collision time with the ith robot can be obtained as follows:
time of collision tijDescribing the time urgency of the collision of the two robots, and the size and the direction of the collision time can be used for determining the collision condition between the two robots;
s313, if the ith robot and the jth robot are in a formation graph
Are adjacent and the collision time t of the two robotsijWhen < 0 and the absolute value is small, the corresponding weight parameter omegaijShould be increased; ω can be represented by a zero-mean Gaussian density functionijThe change of (2):
where k > 0 is the peak of the Gaussian density function, σ2Indicating sensitivity to time of impactDegree, smaller σ2Such that the weight parameter omegaijThe more sensitive the change in (c);
s4, using the information obtained in the steps S2 and S3 as input of a distributed MPC algorithm, obtaining a control sequence and a state sequence by solving an optimization problem, selecting a first control quantity as an optimal control quantity of the robot at the current moment, inputting the optimal control quantity and acting the optimal control quantity on the robot, and driving the robot to reach an expected target point;
s5, repeating the steps S2 to S4 until reaching the end point.
2. The method as claimed in claim 1, wherein the step S1 is performed by modeling the robot cluster using graph theory, and for the cluster of n robots, using a formation graph
To represent the cluster relationships,
is a set of vertexes representing robots, is a set of directed edges representing the direction of information flow between robots,
indicating the desired distance between the robots,
representing the desired relative azimuth of the robot.
3. The cooperative movement method of clustered robots in a scene with obstacles as claimed in claim 2, wherein in order to maintain the stability of the formation of the queue, the frequency of weight adjustment is reduced, and the adaptive weight change is triggered only when the neighbor enters into the dangerous range of the robots.
4. The coordinated movement method of clustered robots in a scene with obstacles according to claim 3, wherein the step of S3, changing the edges in the formation graph according to the states of the neighbors specifically comprises the following steps:
s321, defining the distortion coefficient of the distance of the jth robot as follows:
wherein s isij∈SfIs the expected distance of the jth robot relative to the ith robot, dijis the actual distance between the jth robot and the ith robot, when etaijwhen the distance between the two robots is equal to 0, the distance between the two robots is exactly equal to the expected distance, and when eta is equal to 0ijwhen the current position is greater than 0, the ith robot has traction to the jth robot, and when eta is greater than 0ijIf the number is less than 0, the ith robot can have repulsive force to the jth robot;
s322. if in the formation graph, there is a directed edge (v)i,vj) And (v)k,vj) meaning that the jth robot is required to maintain a desired distance from both the ith and kth robots, when ηij< 0 and ηkj＞ηthresholdIn this case, the jth robot needs to delete the directed edge (v)k,vj) The requirement of keeping a desired distance from the kth robot is removed, and the motion constraint on the jth robot is relaxed, so that the jth robot can find a feasible solution in a constraint environment more easily;
s323, when the jth robot observes a non-neighbor robot in a perception range through a sensor, if the jth robot observes the jth robot and the jth robot is not a neighbor member of the jth robot in the formation graph, the collision time t obtained through observation calculation is setuj< 0, this means that for the jth robot, a robot that was not previously a neighbor member of the jth robot is approaching, in which case the jth robot is kept away from the uth robot to avoid thisCollision, will have an edge (v)u,vj) Adding the data to the original formation topological structure chart and correspondingly setting the expected distance sujAnd adding the distance data into the original expected distance set, thereby changing the original formation diagram of the clustered robots.
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| CN110737263B (en) * | 2019-11-21 | 2023-04-07 | 中科探海(苏州)海洋科技有限责任公司 | Multi-robot formation control method based on artificial immunity |
| CN110908384B (en) * | 2019-12-05 | 2022-09-23 | 中山大学 | Formation navigation method for distributed multi-robot collaborative unknown random maze |
| CN111113417B (en) * | 2019-12-25 | 2021-10-29 | 广东省智能制造研究所 | Distributed multi-robot cooperative motion control method and system |
| CN111830982A (en) * | 2020-07-16 | 2020-10-27 | 陕西理工大学 | Mobile robot formation and obstacle avoidance control method |
| CN111844038B (en) * | 2020-07-23 | 2022-01-07 | 炬星科技（深圳）有限公司 | Robot motion information identification method, obstacle avoidance robot and obstacle avoidance system |
| CN114578842B (en) * | 2020-11-30 | 2023-04-21 | 南京理工大学 | Collaborative path planning method for unmanned vehicle-mounted rotor unmanned aerial vehicle cluster reconnaissance |
| CN112965482B (en) * | 2021-02-01 | 2023-03-10 | 广东省科学院智能制造研究所 | Multi-robot motion collision avoidance control method and system |
| CN113110429B (en) * | 2021-04-02 | 2022-07-05 | 北京理工大学 | Minimum lasting formation generation and control method of multi-robot system under visual field constraint |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104898663A (en) * | 2015-04-08 | 2015-09-09 | 华东交通大学 | Distributed multi-robot containment collision prevention control method |
| KR20170016684A (en) * | 2015-08-04 | 2017-02-14 | 창원대학교 산학협력단 | The unmanned air vehicle for castaway tracking |
| CN107807671A (en) * | 2017-11-27 | 2018-03-16 | 中国人民解放军陆军工程大学 | Unmanned plane cluster danger bypassing method |
| CN108549407A (en) * | 2018-05-23 | 2018-09-18 | 哈尔滨工业大学（威海） | A kind of control algolithm of multiple no-manned plane collaboration formation avoidance |
| CN108919835A (en) * | 2018-09-25 | 2018-11-30 | 北京航空航天大学 | Control method, device and the controller that unmanned vehicle is formed into columns |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103995539B (en) * | 2014-05-15 | 2016-04-20 | 北京航空航天大学 | A kind of unmanned plane autonomous formation evaluation index and MPC formation control method |
-
2019
- 2019-03-21 CN CN201910218602.7A patent/CN110162035B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104898663A (en) * | 2015-04-08 | 2015-09-09 | 华东交通大学 | Distributed multi-robot containment collision prevention control method |
| KR20170016684A (en) * | 2015-08-04 | 2017-02-14 | 창원대학교 산학협력단 | The unmanned air vehicle for castaway tracking |
| CN107807671A (en) * | 2017-11-27 | 2018-03-16 | 中国人民解放军陆军工程大学 | Unmanned plane cluster danger bypassing method |
| CN108549407A (en) * | 2018-05-23 | 2018-09-18 | 哈尔滨工业大学（威海） | A kind of control algolithm of multiple no-manned plane collaboration formation avoidance |
| CN108919835A (en) * | 2018-09-25 | 2018-11-30 | 北京航空航天大学 | Control method, device and the controller that unmanned vehicle is formed into columns |
Non-Patent Citations (3)
| Title |
|---|
| 基于四旋翼飞行器的无人机编队飞行控制研究;李卉;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;中国学术期刊（光盘版）电子杂志社;20180515(第5期);第43-49页 * |
| 基于领导者_跟随者策略的多轮式移动机器人编队控制方法;贾瑞明;《中国优秀硕士学位论文全文数据库 信息科技辑》;中国学术期刊（光盘版）电子杂志社;20190215(第2期);第1-57页 * |
| 多无人机编队路径规划与队形控制技术研究;邵壮;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;中国学术期刊（光盘版）电子杂志社;20190115(第1期);第1-167页 * |
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