CN114779829A - Behavior control method and system for micro flapping wing flying robot - Google Patents
Behavior control method and system for micro flapping wing flying robot Download PDFInfo
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Abstract
The invention belongs to the field of robot control, in particular to a behavior control method and a behavior control system of a miniature flapping wing flying robot, which comprises a main-end controller and a plurality of robot motion controllers, wherein the robot motion controllers have an intelligent control function; the master end controller sends robot behavior commands, and the robot motion controller at the slave end analyzes and executes the behavior commands after receiving the behavior level commands, so that the robot behavior control is realized. The invention saves the workload of an operator and controls the stable and smooth track. The control channel proportion of the behavior control mode is below 1%, and the communication volume and the control channel proportion are greatly reduced.
Description
Technical Field
The invention belongs to the field of robot control, and particularly relates to a behavior control method and a behavior control system of a micro flapping wing flying robot.
Technical Field
Due to the characteristics of small size, light weight and good concealment, the flapping wing flying robot has wide application prospect in the field of unmanned aerial vehicles. Meanwhile, the flapping wing flying robot can finish operation tasks such as environment detection and the like in a high efficiency and large range. The micro flapping wing flying robot cluster has great research value and significance in military and civil fields.
However, the conventional manual control method is to send speed and angle control instructions of the flying robot in real time, the ratio of control channels is high, and problems such as poor signals, signal interruption, communication delay, signal blockage and the like are easily caused due to the fact that wireless communication is easily interfered, the flying robot cannot receive the control instructions, so that series of accidents such as collision of the flying robot, obstacle collision, airplane out of control and the like are easily caused, and the problems to be solved urgently by the conventional flapping-wing flying robot and the conventional rotary-wing robot are also solved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a behavior control method and a control system for a miniature flapping wing flying robot, which solve the problems of high channel occupation ratio and large workload of operators in the prior art, and reduce the adverse effects on the control system caused by the problems of poor wireless communication, signal interruption, communication delay, signal blockage and the like.
The realization process of the invention is as follows:
a micro flapping wing flying robot behavior control system, comprising:
the robot motion controller at the slave end analyzes and executes the behavior command to realize the behavior control of the robot, and the master end controller monitors the state of the robot in real time through robot vision and a sensor and adjusts the state of the robot through the behavior control command to finish an operation task;
and the main-end controller generates a motion behavior function sequence by adopting the motion behavior generator according to the task of the robot, and sends the motion behavior function control sequence to the robot motion controller so as to control the robot executing mechanism to complete the operation task of the robot.
Further, the robot motion controller comprises a motion behavior function analyzer for analyzing the motion behavior function control sequence; and the terminal motion control end is used for completing tasks according to the analyzed instructions.
Further, the exercise behavior function control sequence is composed of functions in an exercise behavior function alphabet, and the exercise behavior function alphabet is formulated according to actual exercise characteristics of each exercise behavior function.
Furthermore, the main-end controller generates a control logic based on a behavior tree according to the flapping-wing flying robot task, and generates a motion behavior function set; the behavior tree comprises sequence nodes, backspacing nodes, node conditions and robot behaviors, the behavior tree comprises control nodes and execution nodes, and the control nodes are positioned in the behavior tree and used for logical reasoning and route navigation; the execution node is positioned at the bottom end of the behavior tree and used for judging conditions and executing actions; the control nodes comprise sequence nodes and backspacing nodes, and the execution nodes comprise condition nodes and action nodes; in the execution process of the behavior tree, a root node sends a frame signal to a lower-layer node, the signal is transmitted downwards according to the sequence from left to right and from top to bottom, each node in the tree is executed, and after execution is finished, execution results are fed back to the upper-layer node layer by layer.
A behavior control method of a micro flapping wing flying robot comprises the following steps: and planning a path according to the task of the robot, and generating a motion behavior function sequence to the robot motion controller so as to control the robot actuating mechanism to complete the planning and operation tasks of the robot.
Furthermore, a motion behavior function control sequence is generated according to the flying robot task, and a control logic based on a behavior tree is generated, wherein the control logic is determined by a tree structure of the behavior tree, the behavior tree comprises control nodes and execution nodes, and the control nodes are positioned in the behavior tree and used for logical reasoning and route navigation; the execution node is positioned at the bottom end of the behavior tree and used for judging conditions and executing actions, and the control node comprises a sequence node and a backspacing node; the execution nodes comprise condition nodes and action nodes, the control node sends frame signals to the execution nodes according to a certain frequency in the execution process of the behavior tree, the signals are transmitted downwards according to the depth-first sequence and the execution nodes in the behavior tree are executed, and after the execution is finished, the backspacing node feeds back the execution results to the upper nodes layer by layer.
Furthermore, a behavior tree is generated according to the tasks of the flapping wing flying robot, the control logic of the flapping wing flying robot is combined by using the node conditions and the action behaviors at the bottom layer of the behavior tree, a behavior function set is combined by using the node actions at the bottom layer, the slave-end robot is controlled by using the behavior function and the function set, and the behavior control of the flapping wing flying robot is realized.
Furthermore, in an emergency state, the behaviors of the flapping-wing flying robot comprise a direction adjusting behavior, an obstacle avoiding behavior, a base returning behavior and a communication restarting behavior, and a behavior tree consists of a sequence node, a backspacing node, and a plurality of node conditions and actions under the sequence node and the backspacing node; the node action executes a direction adjustment action, an obstacle avoidance action, a base return action or a communication restart action according to different node conditions of the flapping wing flying robot.
Furthermore, the design method for obtaining each motion behavior function according to the actual motion characteristics of the flying robot behavior set comprises the following steps:
completing the operation task of the flapping wing flying robot according to the actual operation task requirement by utilizing a manual control mode of the flapping wing flying robot, and recording gyroscope data and flight data in the manual control mode;
and carrying out fuzzy clustering analysis on the gyroscope data to obtain data classification of the gyroscope, inducing flight characteristics according to the data classification, and designing a motion behavior function corresponding to the classification.
Further, the task decomposition process based on the behavior tree is as follows: decomposing the general task into a next layer of subtasks according to the actual meaning of the general task, decomposing the next layer of subtasks layer by layer according to the type of the task to form a tree structure, enabling the bottom layer to be a node condition and a node action, utilizing a backspacing node to backspace and feed back a successfully executed task to the upper layer by layer, executing all the subtasks of all sequence nodes of the returned layer in sequence, and feeding back to the upper layer by backspace until the general task is completed; and executing the sequence nodes of the same layer according to the sequence of the set tasks, wherein the execution principle of the backspacing node is as follows: and starting to execute each node from left to right and from top to bottom, and searching for a return success or an in-operation state until the total task is completed.
Compared with the prior art, the invention has the beneficial effects that:
the invention observes the defects and advantages of the device by comparing the manual control mode and the motion behavior control mode of the experiment. In a manual control mode controlled by a handle controller, an operator judges in real time through the visual feedback of the robot, and the motion track has the defects of jitter and unsmooth. The manual control mode requires real-time judgment and control by an operator, and the operator has a large workload and is easy to fatigue. In the motion behavior control mode, an operator only needs to monitor and interact with a small number of robot instructions, so that the workload of the operator is greatly saved, and the control track is smooth. Therefore, the control method of the flapping-wing flying robot based on the behaviors has obvious advantages. As can be seen from table 5, the control channel occupancy rate in the manual control mode is 100%, the control command needs to be transmitted in real time, and the behavior control mode control channel occupancy rate is 0.05%, which greatly reduces the traffic. While the operator working time of the manual control mode includes a monitoring time and a control (commanding) time, the duty ratio is 100%. Whereas athletic performance control saves command time, thus the operator workload is 50%. Therefore, as seen from the simulation results, the control method based on the motion behavior is not only effective but also feasible as compared with the manual control.
Drawings
Fig. 1 is a block diagram of a system according to an embodiment of the present invention;
FIG. 2 is a behavior tree for battlefield investigation and mission execution of an ornithopter flight robot according to an embodiment of the present invention;
FIG. 3 is a graph of gyroscope data processed by the fuzzy C-means clustering method according to the embodiment of the present invention;
fig. 4 is a behavior tree diagram of the flapping-wing flying robot provided by the embodiment of the invention under the emergency obstacle avoidance condition;
FIG. 5 is a space motion diagram of a flying robot based on manual control provided by an embodiment of the invention;
fig. 6 is a space motion diagram of a flying robot based on behavior control according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Referring to fig. 1, a behavior control system of a micro flapping wing flying robot comprises:
the robot motion controller has an intelligent control function. The master end controller sends a robot behavior command, and the robot motion controller at the slave end analyzes and executes the behavior command after receiving the behavior level command, so that the robot behavior control is realized. The main controller monitors the state of the robot in real time through robot vision and the sensor, and adjusts the state of the robot through a behavior control instruction to complete an operation task.
The main-end controller plans a path according to the tasks of the robot, generates a track according to the planned path, generates a motion behavior function sequence by adopting a motion behavior generator, and sends the motion behavior function control sequence to the robot motion controller so as to control a robot executing mechanism to complete the planning and operation tasks of the robot.
The robot locomotor has partial intelligent control functions, such as emergency obstacle avoidance and the like. And the master end controller sends the robot behavior command, and the slave end receives the behavior level command and then analyzes and executes the behavior command to realize the robot behavior control. The master end controller can control the robot motion controllers of a plurality of slave ends, and a distributed robot intelligent control mode is realized. The master controller judges the state of the robot in real time through airborne vision, sensors and other data, and adjusts the state of the slave robot through behavior control instructions.
The hardware system architecture of the athletic performance control framework is shown in fig. 1. The functions in the motion behavior alphabet are in the form of L (v, ψ), allowing each function to execute over an arbitrarily long period of time. In the system configuration shown in fig. 1, the robot controller performs path planning and trajectory generation according to the task. Then, a sequence of athletic performances is generated by the athletic performance function generator. And then, the motion behavior control sequence is sent to a motion behavior analyzer so as to control a robot executing mechanism to complete the planning and operation tasks of the robot.
Robot behavior is formally defined as being made up of a string of symbols made up of motion functions, the string of symbols made up of behaviors being referred to as a motion plan.
And establishing a ground control unit by taking the main-end controller as a ground control station, sending the behavior function parameters to the flapping-wing robot, and realizing the receiving and command resolving of the behavior-level control command to form the behavior movement of the flapping-wing robot.
The kinematic behavior control sequence is composed of functions in a kinematic behavior alphabet defined according to a task-oriented robot behavior tree.
And the main-end controller generates a control logic based on a behavior tree according to the flying robot task and generates a motion behavior control sequence. The behavior tree is composed of sequence nodes, backspacing nodes, node conditions and robot behaviors. The behavior tree comprises control nodes and execution nodes, and the control nodes are located in the behavior tree and used for logical reasoning and route navigation. The execution node is positioned at the bottom end of the behavior tree and used for judging conditions and executing actions, the control node comprises a sequence node and a backspacing node, and the execution node comprises a node condition and an execution action. And in the execution process of the behavior tree, the root node sends frame signals to each node, the signals are transmitted downwards according to the sequence from left to right and from top to bottom, the nodes in the tree are executed, and after the execution is finished, the execution results are fed back to the upper-layer nodes layer by layer.
The behavior tree is a hierarchical and modular tree structure with root nodes, is usually used for expressing a behavior model of an intelligent agent, and is an effective method for describing switching between different tasks in an autonomous intelligent agent or a robot. Table 1 shows node types and descriptions of the behavior tree, and table 2 shows a node description table:
TABLE 1 node types and descriptions of behavior trees
Table 2 node description table
The behavior tree includes two types of nodes: a control node and an execution node. The control node is positioned in the behavior tree and used for logical reasoning and route navigation. And the execution node is positioned at the bottom end of the behavior tree and used for judging conditions and executing actions. The control nodes adopted by the invention comprise a rollback node and a sequence node, and the execution nodes comprise node conditions and execution actions. In the execution process of the behavior tree, a root node (a sequence node at the top layer) sends out a frame signal and executes nodes, the signal is transmitted downwards according to the sequence from left to right and from top to bottom, each node in the tree is executed, and after the execution is finished, the execution result is fed back to the nodes at the upper layer by layer. The behavior tree of the flapping-wing flying robot battlefield investigation attack mission is shown in figure 2. Therefore, the flapping wing flying robot forms a control system taking node condition-robot behavior as control logic.
The control logic is as follows: starting execution by the layer 1 sequence node 0, executing the rollback node 1-1, executing patrol behavior until a target is found, and returning the layer 2 sequence node 1-1 as success.
And continuously executing the 2-layer sequence node 1-2, continuously executing the 3-layer backspace node 2-2, executing the direction regulation behavior until the attack angle is met, and returning to the node 2-2 to be successful.
And executing the backspacing node 2-3, and after the attack confirmation, attacking until the attack confirmation stops, wherein the backspacing node 2-3 is successful. And returning 1-2 node success.
And continuously executing the 1-3 sequence nodes, executing the backspacing nodes 2-4, and executing the evasion behavior when the distance is less than the safe distance until the distance is greater than the safe distance and backspacing the 2-4 nodes successfully.
And then, when the airframe is damaged at the rollback node 2-5, the behavior of exiting the battlefield is executed, and the flying robot task is completed.
Therefore, through the definition and analysis of the behavior tree of the flapping-wing flying robot, the behavior tree of the flying robot is composed of sequence nodes, backspacing nodes, node conditions and robot behaviors, and the control logic is determined by the node conditions and the robot behaviors, and can be dynamically adjusted and optimized according to the task of the flying robot.
The design of the flapping wing flying robot motion behavior function based on the behavior tree comprises the following steps:
according to the task of the flapping wing flying robot, a behavior set of the flapping wing flying robot based on the behavior tree can be obtained. According to fig. 2, the task of the flapping wing flying robot is a battlefield reconnaissance attack task, and the bottom node actions can be defined as the behaviors of the flapping wing flying robot, such as patrol, direction adjustment, attack and battlefield exit.
The table of the robot behavior function with behavior characteristics is defined as table 3:
TABLE 3 flapping wing flying robot behavior function table
| Behavior classification | Function of |
|
| 1 | Patrol behavior | L(v1,ψ1) |
| 2 | Adjusting directional behavior | L(v2,ψ2) |
| 3 | Aggressive behavior | L(v3,ψ3) |
| 4 | Quit battlefield | L(v4,ψ4) |
| 5 | Behavior of evasion | L(v5,ψ5) |
The two-dimensional simplified model of the flying robot state space can be expressed as
Where (x, y) represents the center point coordinates of the flying robot, θ represents the azimuth angle of the flying robot with respect to the x-axis, v represents the linear velocity of the robot, and γ represents the driving angle of the robot. The control variables of the robot are the linear velocity v and the driving angle gamma of the robot. Where x ═ (x, y, θ)TTo system state, u ═ v, γ)TIs a control input, y ═ x, y, θ)TIs the system output.
Examples of the meaning of the L (v, ψ) function are illustrated below:
when the flying robot approaches the expected straight line in the advancing direction, the flying robot azimuth angle theta tends to the angle alpha (-) of the expected straight line, and the corresponding sign in the function is defined as:
v1＝v′
φ1＝K tan-1(k1·(k2·Δα(·)+k3vω(Δα(·))δ(x,y)))
wherein v is0Is a constant number k1、k2And k3Is a control gain constant, K is a control angle gain variable, Δ α (·) is a difference between an expected angle and an actual angle, w (Δ α) is an angle difference gain function, and δ (x, y) is a system state expected angle function. L (v)2,ψ2) And L (v)3,ψ3) Symbol definition and function description of (1) reference L (v)1,ψ1)。
According to the definition of the above function, the motion alphabet of the robot motion is as follows: sigma ═ L1,L2,L3,L2,L1... } optimization of flapping-wing flying robot behavior:
and optimizing a defined behavior function table of the flapping wing flying robot, firstly, analyzing each behavior function, removing the behavior function which is difficult to execute and finish by the flapping wing flying robot, and combining more than two behaviors to construct a composite behavior which is beneficial to task execution.
The design method for obtaining each motion behavior function according to the actual motion characteristics of the behavior set of the flapping wing flying robot comprises the following steps:
and completing the operation task of the flapping wing flying robot by utilizing a manual control mode of the flapping wing flying robot according to the requirement of the actual operation task, and recording the data of the airborne gyroscope and the flight data under the manual control mode.
And carrying out fuzzy clustering analysis on the gyroscope data to obtain classification data of the gyroscope, extracting flight characteristics according to the classification data, and designing a motion behavior function corresponding to the classification.
And a characteristic behavior design method oriented to actual operation tasks by utilizing field manual control experimental data.
Firstly, the operation task of the flapping-wing flying robot is completed by utilizing a manual control mode of the flapping-wing flying robot according to the requirement of the actual operation task, and gyroscope data and flight data under the manual control mode are recorded. The gyroscope data is subjected to fuzzy clustering analysis, classification data of the gyroscope is obtained, and a behavior function and a function set of the flapping wing flying robot facing to the actual operation task are set according to the classification data, so that the behavior function of the flapping wing flying robot has flight characteristics of the operation task, and a new method for designing the behavior function of the flying robot facing to the operation task is realized, as shown in fig. 3.
Wherein, the fuzzy clustering process: the three-dimensional data of the gyroscope is subjected to fuzzy clustering processing, optionally three initial points are three clustering initial centers, the distance from each point to the three clustering centers is calculated, then the distance is subjected to fuzzy function processing to obtain fuzzy values, fuzzy matrixes from all the points to the trimerization center are respectively calculated, the fuzzy matrixes are divided into the nearest class according to the fuzzy values, then a new clustering center is calculated, and the next cycle is carried out.
The construction process of the behavior tree comprises the following steps: and decomposing the total task into next-layer subtasks according to the actual meaning of the total task, and decomposing the next-layer subtasks layer by layer according to the type of the task to form a tree structure, wherein the bottom layer is a node condition and an execution action, so that the task decomposition based on the behavior tree is realized. The execution principle of the fallback node is as follows: and starting to execute each node from left to right and from top to bottom, and searching for a return success or an in-operation state until the total task is completed.
According to the actual meaning of the task, decomposing the initial task by using a single child node success mode of the backspacing node, success modes of all child nodes of the sequence node and the like, and then continuing to decompose the nodes into backspacing nodes, sequence nodes, node conditions, action behaviors and the like according to the actual meaning of the hierarchical subtasks so as to meet the requirement of the task in the actual meaning and meet the actual running process of the whole period of the task by using the node conditions and the action behaviors.
The invention also provides a control method for the autonomous obstacle avoidance and fault tolerance behavior of the robot in an emergency state.
When the flapping wing flying robot is in abnormal states such as poor communication signals, signal interruption, communication delay, signal blockage and the like, a fault operation mode is started, a fault operation behavior tree is taken as a description basis, safe obstacle avoidance and communication connection are taken as subtasks, an autonomous obstacle avoidance operation mode in an emergency state of the flapping wing flying robot is formed, a behavior function table of the flapping wing flying robot is shown in a table 4, and a behavior tree is shown in a figure 4.
TABLE 4 flapping-wing flying robot behavior function table
| Behavior classification | Function of |
|
| 1 | Adjusting directional behavior | L(v1,ψ1) |
| 2 | Obstacle avoidance behavior | L(v2,ψ2) |
| 3 | Base return | L(v3,ψ3) |
| 4 | Communication restart | L(v4,ψ4) |
By the autonomous obstacle avoidance fault-tolerant control mode of the flapping wing flying robot under the emergency condition, the obstacle avoidance control of the flapping wing flying robot under the emergency condition can be realized by the direction adjusting action, the obstacle avoidance action, the base return action and the communication restarting action, and reliable robot behavior control support is provided for the flapping wing flying robot to return to a ground control station, the communication reconnection and the like.
The invention provides a behavior control method of a miniature flapping wing flying robot, which is characterized in that a behavior tree is constructed according to the task of the robot, and a motion behavior sequence is generated according to the behavior tree and is sent to a robot motion controller so as to complete the operation task of the robot.
And generating a control logic based on a behavior tree according to the operation task of the flapping wing flying robot, and generating a motion behavior control sequence. The behavior tree comprises control nodes and execution nodes, wherein the control nodes are positioned in the behavior tree and are used for logical reasoning and routing navigation. The execution node is positioned at the bottom end of the behavior tree and used for judging conditions and executing actions, the control node comprises a sequence node and a backspacing node, and the execution node comprises a node condition and an execution action. And in the execution process of the behavior tree, the root node sends frame signals to each node, the signals are transmitted downwards according to the sequence from left to right and from top to bottom, each node in the tree is executed, and after the execution is finished, each node feeds back the execution results to the upper-layer nodes layer by layer.
The node action is taken as the behavior of the flapping wing flying robot, a behavior set of the flying robot based on a behavior tree is obtained according to the task of the flapping wing flying robot, and a motion behavior function control sequence is formed by functions in a motion behavior function alphabet.
In an emergency state, the behavior functions of the flapping-wing flying robot comprise a direction adjusting behavior, an obstacle avoiding behavior, a base return behavior and a communication restarting behavior. The behavior tree adopts sequence nodes and backspacing nodes and a plurality of node conditions and execution actions under the sequence nodes and the backspacing nodes, and the node actions adjust the behaviors, avoid the obstacle behaviors and return to the base or restart the communication behaviors according to the execution direction of the behavior function of the flapping-wing flying robot.
The design method for obtaining the motion behavior function according to the actual motion characteristics of the flapping wing flying robot comprises the following steps:
and completing the operation task of the flapping wing flying robot by utilizing a manual control mode of the flapping wing flying robot according to the requirement of the actual operation task, and recording gyroscope data and flight data in the manual control mode.
And carrying out fuzzy clustering analysis on the gyroscope data to obtain data classification of the gyroscope, inducing flight characteristics according to the data classification, and designing a motion behavior function corresponding to the classification.
The construction process of the behavior tree comprises the following steps: and decomposing the total task into next-layer subtasks according to the actual meaning of the total task, and decomposing the next-layer subtasks layer by layer according to the type of the task to form a tree structure, wherein the bottom layer is a node condition and an execution action, so that the task decomposition based on the behavior tree is realized. The execution principle of the fallback node is as follows: and starting to execute each node from left to right and from top to bottom, and searching for a return success or an operating state until the total task is completed.
The validity of the scheme of the embodiment of the invention is verified:
the defects and the advantages of the method are observed by comparing the manual control mode and the motion behavior control mode of the experiment. In a manual control mode controlled by a handle controller, an operator judges in real time through robot visual feedback, and the robot track has the defects of jitter and non-smoothness, as shown in fig. 5. The manual control mode requires real-time control and judgment by an operator, and the operator has large workload and is easy to fatigue. In the motion behavior control mode, the operator only needs to perform monitoring and a small amount of robot instruction interaction, so that the workload of the operator is greatly saved, and the control track is smooth, as shown in fig. 6. Therefore, the control method of the flapping-wing flying robot based on the behaviors has obvious advantages.
TABLE 5 comparison of control parameters for manual control mode and athletic performance mode
As can be seen from table 5, the occupation ratio of the communication control channel in the manual control mode is 100%, the control command needs to be transmitted in real time, and the occupation ratio of the behavior control mode control channel is 0.05%, which greatly reduces the communication traffic. While the operator workload for the manual control mode includes a monitoring time and a control (command) time, the workload ratio is 100%. Whereas athletic performance control saves command time, with operator workload approaching 50%. Therefore, as seen from the simulation results, the control method based on the motion behavior is not only effective but also feasible as compared with the manual control.
The above description is intended to be illustrative of the preferred embodiment of the present invention and should not be taken as limiting the invention, but rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (10)
1. A miniature flapping wing flying robot behavior control system is characterized by comprising:
the robot motion controller at the slave end analyzes and executes the behavior command to realize the behavior control of the robot, and the master end controller monitors the state of the robot in real time through robot vision and a sensor and adjusts the state of the robot through the behavior control command to finish an operation task;
and the main-end controller generates a motion behavior function sequence by adopting the motion behavior generator according to the task of the robot, and sends the motion behavior function control sequence to the robot motion controller so as to control the robot executing mechanism to complete the operation task of the robot.
2. The control system of claim 1, wherein the robot motion controller includes a motion behavior function parser that parses a motion behavior function control sequence; and the terminal motion control end is used for completing tasks according to the analyzed instructions.
3. A control system according to claim 2, wherein the motion behavior function control sequence is formed as a function of an alphabet of motion behavior functions, the alphabet being formulated on the basis of actual motion characteristics of the respective motion behavior function.
4. The control system of claim 1, wherein the master controller generates a behavior tree based control logic from flapping-wing flying robot missions, generating a set of motion behavior functions; the behavior tree comprises sequence nodes, backspacing nodes, node conditions and robot behaviors, the behavior tree comprises control nodes and execution nodes, and the control nodes are positioned in the behavior tree and used for logical reasoning and routing navigation; the execution node is positioned at the bottom end of the behavior tree and used for judging conditions and executing actions; the control node comprises a sequence node and a backspacing node, and the execution node comprises a condition node and an action node; in the execution process of the behavior tree, a root node sends a frame signal to a lower-layer node, the signal is transmitted downwards according to the sequence from left to right and from top to bottom, each node in the tree is executed, and after execution is finished, execution results are fed back to the upper-layer node layer by layer.
5. A behavior control method of a micro flapping wing flying robot is characterized by comprising the following steps: and planning a path according to the task of the robot, and generating a motion behavior function sequence to the robot motion controller so as to control the robot actuating mechanism to complete the planning and operation tasks of the robot.
6. The control method according to claim 5, characterized in that a motion behavior function control sequence is generated according to the flying robot task, and a control logic based on a behavior tree is generated, wherein the control logic is determined by a tree structure of the behavior tree, the behavior tree comprises a control node and an execution node, and the control node is positioned in the behavior tree and used for logical reasoning and route navigation; the execution node is positioned at the bottom end of the behavior tree and used for judging conditions and executing actions, and the control node comprises a sequence node and a backspacing node; the execution nodes comprise condition nodes and action nodes, the control node sends frame signals to the execution nodes according to a certain frequency in the execution process of the behavior tree, the signals are transmitted downwards according to the depth-first sequence and the execution nodes in the behavior tree are executed, and after the execution is finished, the backspacing node feeds back the execution results to the upper nodes layer by layer.
7. The control method according to claim 6, wherein the behavior tree is generated according to the flapping wing flying robot task, the control logic of the flapping wing flying robot is combined by using the node conditions and the action behaviors at the bottom layer of the behavior tree, the behavior function set is combined by using the node behaviors at the bottom layer, and the slave-end robot is controlled by using the behavior function and the function set, so that the behavior control of the flapping wing flying robot is realized.
8. The control method according to claim 6, wherein in an emergency state, the flapping-wing flying robot behaviors include a direction adjustment behavior, an obstacle avoidance behavior, a base return and a communication restart behavior, and the behavior tree is composed of a sequence node and a rollback node, and a plurality of node conditions and actions under the sequence node and the rollback node; the node action executes a direction adjustment action, an obstacle avoidance action, a base return action or a communication restart action according to different node conditions of the flapping wing flying robot.
9. The control method according to claim 6, wherein the design method for obtaining each motion behavior function according to the actual motion characteristics of the flying robot behavior set comprises the following steps:
completing the operation task of the flapping wing flying robot according to the actual operation task requirement by utilizing a manual control mode of the flapping wing flying robot, and recording gyroscope data and flight data in the manual control mode;
and carrying out fuzzy clustering analysis on the gyroscope data to obtain data classification of the gyroscope, inducing flight characteristics according to the data classification, and designing a motion behavior function corresponding to the classification.
10. Control method according to claim 6, characterized in that the task decomposition procedure based on the behavior tree is: decomposing the general task into a next layer of subtasks according to the actual meaning of the general task, decomposing the next layer of subtasks layer by layer according to the type of the task to form a tree structure, enabling the bottom layer to be a node condition and a node action, utilizing a backspacing node to backspace and feed back a successfully executed task to the upper layer by layer, executing all the subtasks of all sequence nodes of the returned layer in sequence, and feeding back to the upper layer by backspace until the general task is completed; and executing the sequence nodes of the same layer according to the sequence of the set tasks, wherein the execution principle of the backspacing node is as follows: and starting to execute each node from left to right and from top to bottom, and searching for a return success or an in-operation state until the total task is completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210364323.3A CN114779829A (en) | 2022-04-08 | 2022-04-08 | Behavior control method and system for micro flapping wing flying robot |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116227361A (en) * | 2023-03-06 | 2023-06-06 | 中国人民解放军32370部队 | Intelligent body decision method and device |
| CN117369521A (en) * | 2023-12-04 | 2024-01-09 | 中国科学院自动化研究所 | Method, device and equipment for generating behavior tree model path for unmanned aerial vehicle decision |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116227361A (en) * | 2023-03-06 | 2023-06-06 | 中国人民解放军32370部队 | Intelligent body decision method and device |
| CN116227361B (en) * | 2023-03-06 | 2023-08-15 | 中国人民解放军32370部队 | Intelligent body decision method and device |
| CN117369521A (en) * | 2023-12-04 | 2024-01-09 | 中国科学院自动化研究所 | Method, device and equipment for generating behavior tree model path for unmanned aerial vehicle decision |
| CN117369521B (en) * | 2023-12-04 | 2024-02-13 | 中国科学院自动化研究所 | Method, device and equipment for generating behavior tree model path for unmanned aerial vehicle decision |
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