A robot motion mode control method and system with a dual-purpose tail fin for water and air

Abstract

The present invention provides a method and system for controlling the motion modes of a robot with a dual-purpose water and air tail fin, belonging to the technical field of water and air amphibious cross-media robots. The invention collects data through a sensor system and a feedback system, and uses a mode selector based on reinforcement learning to perform mode selection and control, thereby realizing smooth switching of underwater mode, water-to-air mode, air mode and air-to-water mode, and improving the problem of insufficient compatibility of multi-modal motion. At the same time, by designing a dual-purpose water and air tail fin system, the V-shaped adjustment of the rudder surface above the tail fin and the movement of the rudder surface below the tail fin are adopted to realize efficient and highly maneuverable posture adjustment, thereby improving the problem of overall performance imbalance of the robot system and improving the operating efficiency and adaptability of the robot system in complex environments.

Description

Technical Field

[0001] The present invention relates to the technical field of water-air amphibious cross-medium robots, and in particular to a robot motion mode control method and system with a water-air dual-purpose tail fin. Background Art

[0002] The water-air cross-medium robot is a new type of integrated robot system that has the ability to navigate underwater, fly in the air, and freely transition between media. It breaks the inherent pattern of traditional robot systems and greatly improves the operating efficiency and adaptability of unmanned systems in complex water environments. It has broad application prospects in maritime reconnaissance, penetration, marine ecological environment monitoring, and marine resource exploration.

[0003] The water-air cross-media robot system in the existing technology mainly includes propeller-propelled rotor cross-media UAVs, fixed-wing cross-media UAVs, bionic cross-media robots, flying fish-like cross-media robots and squid-like cross-media robots. The propeller-driven rotor cross-medium UAV uses multiple propeller differential drives to achieve propulsion and maneuvering movements. It has no structural compatibility design, and its underwater propulsion speed and efficiency are seriously insufficient. The fixed-wing cross-medium UAV uses propellers as underwater and aerial propulsion devices. Some wings adopt a variable-sweep foldable design, which can be fully unfolded in the air to enhance lift. It has the advantages of high flight efficiency and fast speed, but its folding ratio is small underwater and cannot be completely folded into the body, and it usually does not have a dedicated underwater maneuvering mechanism. Bionic cross-medium robots usually have a specific movement advantage, and it is difficult to take into account multiple movement modes such as underwater navigation and aerial flight, and the overall performance is insufficient. The flying fish-like cross-medium robot adopts underwater fish-like tail swing propulsion and foldable pectoral fin design. It has good underwater navigation capabilities but insufficient aerial flight capabilities. The squid-like cross-medium robot adopts jet propulsion to achieve rapid medium transition, etc., but lacks underwater and aerial navigation capabilities. In summary, the existing water-air cross-media robot systems have certain technical advantages in their respective fields, but they generally have problems such as lack of intelligence in multimodal motion control, insufficient system compatibility, and unbalanced overall performance. Summary of the Invention

[0004] To solve the problems in the above-mentioned prior art, the present invention provides a method and system for controlling the motion modes of a robot equipped with a dual-purpose tail fin for water and air. The invention collects data through a sensor system and a feedback system, and uses a mode selector based on reinforcement learning to select and control the mode, thereby achieving smooth switching between underwater mode, water-to-air mode, air mode, and air-to-water mode, improving the problems of lack of intelligence in multi-modal motion control and insufficient system compatibility. At the same time, by designing a dual-purpose tail fin system for water and air, the V-shaped adjustment of the rudder surface on the tail fin and the movement of the rudder surface under the tail fin are adopted to achieve efficient and highly maneuverable posture adjustment, improve the problem of overall performance imbalance of the robot system, and improve the operating efficiency and adaptability of the robot system in complex environments. To achieve the above-mentioned purpose, the technical solution is as follows:

[0005] In one aspect, the present invention provides a method for controlling motion modes of a robot having a dual-purpose tail fin for water and air, the method comprising:

[0006] S1. Collect data through the sensor system to obtain the current state data set;

[0007] S2. Based on the current state data set, a modality selector based on reinforcement learning is used to select a modality to obtain the current environment modality;

[0008] S3. According to the current environmental modality, the robot is controlled by the controller system to obtain the current form of the robot;

[0009] S4. Obtaining the actual state of the robot through the robot's feedback system according to the current state of the robot;

[0010] S5. According to the actual state of the robot, the controller system is adjusted to obtain a stable form of the robot.

[0011] Optionally, the sensor system comprises:

[0012] Vision sensors, used to collect images and environmental features;

[0013] Inertial measurement sensor, used to collect acceleration, angular velocity and attitude data of the robot;

[0014] The depth sensor is used to collect the vertical position information of the robot, and the vertical position information includes the depth in water and the flying height in the air.

[0015] Optionally, in S1, data is collected through a sensor system to obtain a data set of the current state, including:

[0016] S11. Extract features using a convolutional neural network based on the visual sensor to obtain visual features of the environment;

[0017] S12. Obtaining acceleration, angular velocity, and posture data of the robot by collecting data using the inertial measurement sensor;

[0018] S13. Obtaining vertical position information of the robot through acquisition using the depth sensor;

[0019] S14. Perform matrix processing based on the visual features of the environment, the acceleration of the robot, the angular velocity of the robot, the posture data of the robot, and the vertical position information of the robot to obtain a data set of the current state.

[0020] Optionally, the training method of the reinforcement learning-based modality selector includes:

[0021] S21, by setting the initial reinforcement learning environment, obtaining the initialized Q network;

[0022] S22, inputting the training data set into the Q network, adopting a greedy strategy to obtain the robot mode under the training data set state;

[0023] S23. Control the robot through the controller system according to the robot mode in the training data set state to obtain the current training form and movement of the robot;

[0024] S24. Obtaining the actual training state of the robot through the robot's feedback system according to the current training state of the robot;

[0025] S25. According to the actual training state of the robot and the current training form of the robot, the controller system is adjusted and compared and analyzed, and the correctness reward function of the modality and the adjusted robot training state are obtained through formula (1).

[0026] r t =R correct -R incorrect -R switch_cost +R efficiency (1)

[0027] Where r t is the correctness reward function of the modality, R correct is the reward for choosing the correct mode, R incorrect is the penalty for choosing the wrong mode, R switch_cost is the penalty for frequent mode switching, R efficiency incentives for energy efficiency;

[0028] S26. Repeat steps S22 to S25 to store the current training form of the robot, the robot's actions, the correctness reward function of the modality, and the adjusted robot training state into the experience replay pool to obtain a training experience replay pool.

[0029] S27, input the training experience replay pool into the Q network, and update the parameters of the Q network through formula (2) to obtain the final parameters of the Q network.

[0030]

[0031] Where, Q(s t ,a t ) is the action quality function of the current training form, s t is the current training form of the robot, a t is the action of the robot, a′ is the action of any robot, r t is the correctness reward function of the modality, s t+1 is the adjusted robot training state, γ is the discount factor, and α is the learning rate;

[0032] S28. Input the parameters of the final Q network into the Q network to obtain a mode selector based on reinforcement learning.

[0033] Optionally, the greedy strategy includes:

[0034] Strategy 1: Select a random robot action instruction with probability ∈;

[0035] Strategy 2: Select the robot’s action instruction with the maximum action quality function of the current training form with probability 1-∈.

[0036] Optionally, in S3, controlling the robot through the controller system according to the current environmental modality to obtain the current form of the robot includes:

[0037] S31. Controlling the fixed-wing propulsion system through a controller system according to the current environmental modality to obtain a configuration of the fixed-wing propulsion system;

[0038] S32. Controlling the variable structure membrane wing system through the controller system according to the current environmental mode to obtain a shape of the variable structure membrane wing system;

[0039] S33, controlling the pectoral fin system through the controller system according to the current environmental modality to obtain a shape of the pectoral fin system;

[0040] S34. Control the body cabin system through the controller system according to the current environmental modality to obtain a configuration of the body cabin system.

[0041] S35. Control the multi-joint tail cabin system through the controller system according to the current environmental modality to obtain a configuration of the multi-joint tail cabin system;

[0042] S36. Controlling the dual-purpose water-air tail fin system through the controller system according to the current environmental mode to obtain a configuration of the dual-purpose water-air tail fin system;

[0043] S37. Obtain the current form of the robot based on the form of the fixed-wing propulsion system, the form of the variable structure membrane wing system, the form of the pectoral fin system, the form of the body cabin system, the form of the multi-joint tail cabin system and the form of the water-air dual-purpose tail fin system.

[0044] Optionally, the dual-purpose water-air tail fin system comprises:

[0045] The rudder under the tail fin is used to provide a control surface below to adjust the direction of the flow in water or air;

[0046] The rudder surface on the tail fin is used to provide an upper control surface to adjust the direction of the flow in water or air;

[0047] The rudder surface under the tail fin drives the servo, which is used to adjust the angle change of the rudder surface under the tail fin;

[0048] Rudder arm, used to transmit driving force;

[0049] A bracket, used for fixing the lower rudder surface of the tail fin and the upper rudder surface of the tail fin;

[0050] A planetary gear is used to adjust the angle of the rudder surface on the tail fin;

[0051] A planetary gear fixing frame, used for fixing the planetary gear and connecting the multi-joint tail cabin system;

[0052] The lower tail fin rudder surface drives the servo to be fixedly connected to the rudder arm, and the rudder arm is fixedly connected to the lower tail fin rudder surface. The lower tail fin rudder surface drives the servo to rotate, thereby adjusting the angle between the lower tail fin rudder surface and the upper tail fin rudder surface;

[0053] The planetary gear is fixedly connected to the upper control surface of the tail fin, and the rotation of the planetary gear realizes the V-shaped adjustment movement of the upper control surface of the tail fin.

[0054] Optionally, the planetary gear includes a driving gear, a transmission gear 1, a transmission gear 2 and a transmission gear 3. The driving gear rotates under the drive of the transmission shaft, and the transmission gear 1, the transmission gear 2 and the transmission gear 3 are respectively engaged with the driving gear.

[0055] Optionally, the stable forms of the robot include underwater mode, water-into-air mode, air mode and air-into-water mode.

[0056] In another aspect, the present invention provides a motion modal control system for a robot having a dual-purpose tail fin for water and air, the system being applied to a motion modal control method for a robot having a dual-purpose tail fin for water and air, the system comprising:

[0057] The data acquisition module is used to collect data through the sensor system to obtain the current state data set;

[0058] A mode selection module is used to select a mode based on the current state data set using a reinforcement learning-based mode selector to obtain the current environment mode;

[0059] A first acquisition module is used to control the robot through a controller system according to the current environmental modality to obtain the current form of the robot;

[0060] The second acquisition module is used to obtain the actual state of the robot through the robot's feedback system according to the current state of the robot;

[0061] The third acquisition module is used to obtain a stable form of the robot through adjustment of the controller system according to the actual state of the robot.

[0062] Compared with the prior art, the technical solution of the present invention has at least the following beneficial effects:

[0063] On the one hand, the above scheme collects data through the sensor system and feedback system, and uses the mode selector based on reinforcement learning to select and control the mode, thereby realizing the smooth and intelligent switching of underwater mode, water-to-air mode, air mode and air-to-water mode, and improving the problems of lack of intelligence in multi-modal motion control and insufficient system compatibility. On the other hand, by designing a dual-purpose water-air tail fin system, the V-shaped adjustment of the upper rudder surface of the tail fin and the movement of the lower rudder surface of the tail fin are adopted to realize efficient and highly maneuverable attitude adjustment, improve the problem of overall performance imbalance of the robot system, and improve the operation efficiency and adaptability of the robot system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0065] Figure 1 is a flow chart of an embodiment of a method for controlling motion modes of a robot with a dual-purpose tail fin for water and air according to the present invention;

[0066] Figure 2 is a flow chart of obtaining a data set of a current state in an embodiment of a method for controlling a motion mode of a robot with a dual-purpose tail fin for water and air according to the present invention;

[0067] Figure 3Flowchart of a training method of a mode selector based on reinforcement learning in an embodiment of a method for controlling a motion mode of a robot with a dual-purpose tail fin for water and air according to the present invention;

[0068] Figure 4 This is a flow chart of obtaining the current shape of the robot in an embodiment of the method for controlling the motion mode of the robot with a dual-purpose tail fin for water and air of the present invention;

[0069] Figure 5 1 is a schematic structural diagram of a robot with a dual-purpose underwater and airborne tail fin in an embodiment of a method for controlling a motion mode of a robot with a dual-purpose underwater and airborne tail fin according to the present invention;

[0070] Figure 6 1 is a schematic structural diagram of a dual-purpose water-air tail fin system in an embodiment of a method for controlling motion modes of a robot equipped with a dual-purpose water-air tail fin according to the present invention;

[0071] Figure 7 1 is a schematic structural diagram of a planetary gear system of a robot equipped with a dual-purpose aeronautical tail fin in an embodiment of a method for controlling a motion mode of the robot according to the present invention;

[0072] Figure 8 It is a system block diagram of an embodiment of a robot motion modal control system with a water-air dual-purpose tail fin according to the present invention.

[0073] Explanation of the numbers in the figure: fixed-wing propulsion system 1, variable structure membrane wing system 2, pectoral fin system 3, body cabin system 4, multi-jointed tail cabin system 5, water-air dual-purpose tail fin system 6, tail fin lower control surface 61, tail fin upper control surface 62, tail fin lower control surface driving servo 63, rudder arm 64, bracket 65, planetary gear 66, planetary gear fixing bracket 67, drive gear 661, transmission gear 1 662, transmission gear 2 663, transmission gear 3 664. DETAILED DESCRIPTION

[0074] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0075] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0076] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0077] like Figure 1The flowchart of an embodiment of a method for controlling the motion mode of a robot with a dual-purpose water and air tail fin of the present invention is shown. The present invention provides a method for controlling the motion mode of a robot with a dual-purpose water and air tail fin. The method is implemented by a motion mode control system of the robot with a dual-purpose water and air tail fin. The method includes:

[0078] S1. Collect data through the sensor system to obtain the current state data set;

[0079] Specifically, the sensor system includes:

[0080] Vision sensors, used to collect images and environmental features;

[0081] Inertial measurement sensor, used to collect acceleration, angular velocity and attitude data of the robot;

[0082] The depth sensor is used to collect the vertical position information of the robot, and the vertical position information includes the depth in water and the flying height in the air.

[0083] like Figure 2 The flowchart of obtaining a data set of the current state in an embodiment of the method for controlling the motion mode of a robot with a dual-purpose tail fin of the present invention is shown. In S1, data is collected by a sensor system to obtain a data set of the current state, including:

[0084] S11. Extract features using a convolutional neural network based on the visual sensor to obtain visual features of the environment;

[0085] S12. Obtaining acceleration, angular velocity, and posture data of the robot by collecting data using the inertial measurement sensor;

[0086] S13. Obtaining vertical position information of the robot through acquisition using the depth sensor;

[0087] S14. Perform matrix processing based on the visual features of the environment, the acceleration of the robot, the angular velocity of the robot, the posture data of the robot, and the vertical position information of the robot to obtain a data set of the current state.

[0088] S2. Based on the current state data set, a modality selector based on reinforcement learning is used to select a modality to obtain the current environment modality;

[0089] Specifically, if Figure 3 The flowchart of the training method of the mode selector based on reinforcement learning in the embodiment of the motion mode control method of the robot with a dual-purpose tail fin of the present invention is shown. The training method of the mode selector based on reinforcement learning includes:

[0090] S21, by setting the initial reinforcement learning environment, obtaining the initialized Q network;

[0091] S22, inputting the training data set into the Q network, adopting a greedy strategy to obtain the robot mode under the training data set state;

[0092] S23. Control the robot through the controller system according to the robot mode in the training data set state to obtain the current training form and movement of the robot;

[0093] S24. Obtaining the actual training state of the robot through the robot's feedback system according to the current training state of the robot;

[0094] S25. According to the actual training state of the robot and the current training form of the robot, the controller system is adjusted and compared and analyzed, and the correctness reward function of the modality and the adjusted robot training state are obtained through formula (1).

[0095] r t =R correct -R incorrect -R switch_cost +R efficiency (1)

[0096] Where r t is the correctness reward function of the modality, R correct is the reward for choosing the correct mode, R incorrect is the penalty for choosing the wrong mode, R switch_cost is the penalty for frequent mode switching, R efficiency incentives for energy efficiency;

[0097] S26. Repeat steps S22 to S25 to store the current training form of the robot, the robot's actions, the correctness reward function of the modality, and the adjusted robot training state into the experience replay pool to obtain a training experience replay pool.

[0098] S27, input the training experience replay pool into the Q network, and update the parameters of the Q network through formula (2) to obtain the final parameters of the Q network.

[0099]

[0100] Where, Q(s t ,a t ) is the action quality function of the current training form, s t is the current training form of the robot, a t is the action of the robot, a′ is the action of any robot, r t is the correctness reward function of the modality, st+1 is the adjusted robot training state, γ is the discount factor, and α is the learning rate;

[0101] S28. Input the parameters of the final Q network into the Q network to obtain a mode selector based on reinforcement learning.

[0102] The greedy strategy includes:

[0103] Strategy 1: Select a random robot action instruction with probability ∈;

[0104] Strategy 2: Select the robot’s action instruction with the maximum action quality function of the current training form with probability 1-ε.

[0105] S3. According to the current environmental modality, the robot is controlled by the controller system to obtain the current form of the robot;

[0106] Specifically, if Figure 4 The flowchart of obtaining the current state of the robot in the embodiment of the motion mode control method of the robot with a dual-purpose tail fin of the present invention is shown as follows: Figure 5 The schematic diagram of the structure of a robot with a dual-purpose aquatic tail fin in an embodiment of the method for controlling the motion mode of a robot with a dual-purpose aquatic tail fin of the present invention is shown. In step S3, the robot is controlled by the controller system according to the current environmental mode to obtain the current state of the robot, including:

[0107] S31. Control the fixed-wing propulsion system 1 through the controller system according to the current environmental modality to obtain a configuration of the fixed-wing propulsion system;

[0108] S32, controlling the variable structure membrane wing system 2 through the controller system according to the current environmental mode to obtain the shape of the variable structure membrane wing system;

[0109] S33, controlling the pectoral fin system 3 by the controller system according to the current environmental modality to obtain a shape of the pectoral fin system;

[0110] S34. According to the current environmental modality, the controller system controls the body cabin system 4 to obtain the configuration of the body cabin system.

[0111] S35, controlling the multi-joint tail cabin system 5 through the controller system according to the current environmental modality to obtain a configuration of the multi-joint tail cabin system;

[0112] S36, controlling the water-air dual-purpose tail fin system 6 through the controller system according to the current environmental mode to obtain a shape of the water-air dual-purpose tail fin system;

[0113] S37. Obtain the current form of the robot based on the form of the fixed-wing propulsion system, the form of the variable structure membrane wing system, the form of the pectoral fin system, the form of the body cabin system, the form of the multi-joint tail cabin system and the form of the water-air dual-purpose tail fin system.

[0114] Furthermore, the fixed-wing propulsion system 1 is used as a power source during aerial flight, the variable structure membrane wing system 2 is used for flight stability and generation of flight lift during aerial flight, the pectoral fin system 3 is used for pitch, yaw and other controls during underwater swimming, the body cabin system is used for control, perception, communication and contraction of the variable structure membrane wing system 2 in the underwater swimming state, the multi-joint tail cabin 5 is used for a multi-joint fish-like tail swinging propulsion method, and the water-air dual-purpose tail fin system 6 is used as the main mechanism for generating propulsion force of the fish-like tail fin in the underwater mode and as the V-shaped tail fin structure of the fixed-wing robot in the air mode to achieve adjustment of pitch, yaw and other postures.

[0115] like Figure 6 The structure diagram of the dual-purpose tail fin system of the robot with dual-purpose tail fin in the embodiment of the motion mode control method of the present invention is shown as follows. The dual-purpose tail fin system 6 includes:

[0116] The lower rudder surface 61 of the tail fin is used to provide a control surface below to adjust the direction of the fluid in water or air;

[0117] The rudder surface 62 on the tail fin is used to provide an upper control surface to adjust the direction of the flow in water or air;

[0118] The tail fin lower control surface drives the steering gear 63, which is used to adjust the angle change of the tail fin lower control surface 61;

[0119] A rudder arm 64 for transmitting driving force;

[0120] A bracket 65 is used to fix the lower rudder surface 61 and the upper rudder surface 62 of the tail fin;

[0121] The planetary gear 66 is used to adjust the angle of the control surface 62 on the tail fin;

[0122] A planetary gear fixing frame 67 is used to fix the planetary gear 66 and connect the multi-joint tail cabin system 5;

[0123] The tail fin lower rudder driving servo 63 is fixedly connected to the rudder arm 64, and the rudder arm 64 is fixedly connected to the tail fin lower rudder 61. The tail fin lower rudder driving servo 63 rotates to adjust the angle between the tail fin lower rudder 61 and the tail fin upper rudder 62.

[0124] When the output shaft of the lower tail fin control surface driving servo 63 rotates, the angle between the lower tail fin control surface 61 and the upper tail fin control surface 62 can be adjusted. When the lower tail fin control surface 61 is parallel, the output shaft of the lower tail fin control surface driving servo 63 remains unchanged, indicating the underwater mode. In this mode, the lower tail fin control surface 61 and the upper tail fin control surface 62 remain stationary, serving only as underwater propulsion sources, and underwater propulsion is achieved through the swinging of the multi-jointed tail compartment system 5. In the airborne mode, the upper tail fin control surface 62 forms a V-shaped tail angle, meaning that the planetary gear 66 causes the two upper tail fin control surfaces 62 to rotate in opposite directions.

[0125] The lower tail fin rudder surface 61 drives the rudder arm 64 to rotate through the lower tail fin rudder surface, thereby pulling the bracket 65 to achieve deflection, and realize pitch and yaw control when flying in the air.

[0126] The planetary gear 66 is fixedly connected to the upper control surface 62 of the tail fin. The rotation of the planetary gear 66 enables the V-shaped adjustment movement of the upper control surface 62 of the tail fin.

[0127] Furthermore, if Figure 7 The figure shows a schematic diagram of the structure of the planetary gear of the dual-purpose tail fin system in the embodiment of the motion mode control method of the robot with dual-purpose tail fin of the present invention. The planetary gear 66 includes a driving gear 661, a transmission gear 1 662, a transmission gear 2 663 and a transmission gear 3 664. The driving gear 661 rotates under the drive of the transmission shaft, and the transmission gear 1 662, the transmission gear 2 663 and the transmission gear 3 664 are respectively engaged with the driving gear 661.

[0128] S4. Obtaining the actual state of the robot through the robot's feedback system according to the current state of the robot;

[0129] S5. According to the actual state of the robot, the controller system is adjusted to obtain a stable form of the robot.

[0130] Specifically, the stable forms of the robot include underwater mode, water-to-air mode, air mode and air-to-water mode.

[0131] Furthermore, in the aerial mode, the V-shaped tail angle of the robot is usually fixed, and the aerial attitude adjustment is achieved only by adjusting the angle between the lower tail fin rudder 61 and the upper tail fin rudder 62, that is, adjusting the lower tail fin rudder 61 to change the received aerodynamic force; in the underwater mode, the V-shaped tail angle of the robot is usually 180 degrees, and the angle between the lower tail fin rudder 61 and the upper tail fin rudder 62 is also 180 degrees, that is, the water-air dual-purpose tail fin system 6 constitutes an integral crescent-shaped tail fin, similar to the tail fin of a tuna.

[0132] like Figure 8The system block diagram of an embodiment of a robot motion modal control system with a water-air dual-purpose tail fin of the present invention is shown. The present invention provides a robot motion modal control system with a water-air dual-purpose tail fin. The system is applied to a robot motion modal control method with a water-air dual-purpose tail fin. The system includes a data acquisition module, a mode selection module, a first acquisition module, a second acquisition module, and a third acquisition module. Specifically,

[0133] The data acquisition module is used to collect data through the sensor system to obtain the current state data set;

[0134] A mode selection module is used to select a mode based on the current state data set using a reinforcement learning-based mode selector to obtain the current environment mode;

[0135] A first acquisition module is used to control the robot through a controller system according to the current environmental modality to obtain the current form of the robot;

[0136] The second acquisition module is used to obtain the actual state of the robot through the robot's feedback system according to the current state of the robot;

[0137] The third acquisition module is used to obtain a stable form of the robot through adjustment of the controller system according to the actual state of the robot.

[0138] The present invention provides a method and system for controlling the motion modes of a robot with a dual-purpose water and air tail fin. The invention collects data through a sensor system and a feedback system, and uses a mode selector based on reinforcement learning to perform mode selection and control, thereby achieving smooth switching between underwater mode, water-to-air mode, air mode and air-to-water mode, improving the problems of lack of intelligence in multi-modal motion control and insufficient system compatibility. At the same time, by designing a dual-purpose water and air tail fin system, the V-shaped adjustment of the rudder surface above the tail fin and the movement of the rudder surface below the tail fin are adopted to achieve efficient and highly maneuverable posture adjustment, thereby improving the problem of overall performance imbalance of the robot system and improving the operating efficiency and adaptability of the robot system in complex environments.

[0139] It will be appreciated that the present invention is described by way of the above embodiments and should not be construed as limiting the embodiments of the present invention and the scope of the present invention. It will be appreciated by those skilled in the art that various changes or equivalent replacements may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application fall within the scope protected by the present invention.

Claims

1. A method for controlling the motion mode of a robot with a dual-purpose tail fin for water and air, characterized in that: The method comprises: S1. Collect data through the sensor system to obtain the current state data set; S2. Selecting a modality based on the current state data set using a reinforcement learning-based modality selector to obtain a current environment modality; S3. Controlling the robot through the controller system according to the current environmental modality to obtain the current form of the robot; S4. Obtaining the actual state of the robot through the robot's feedback system according to the current state of the robot; S5. According to the actual state of the robot, the controller system is adjusted to obtain a stable state of the robot; The training method of the mode selector based on reinforcement learning includes: S21, by setting the initial reinforcement learning environment, obtaining the initialized Q network; S22, inputting the training data set into the Q network, adopting a greedy strategy to obtain the robot mode under the training data set state; S23. Control the robot through the controller system according to the robot mode in the state of the training data set to obtain the current training form and movement of the robot; S24. Obtaining the actual training state of the robot through the robot's feedback system according to the current training state of the robot; S25. Adjust and compare the actual training state of the robot and the current training state of the robot through the controller system, and obtain the modal correctness reward function and the adjusted robot training state through formula (1). r t =R correct -R incorrect -R switch_cost +R efficiency (1) Where r t is the correctness reward function of the modality, R correct is the reward for choosing the correct mode, R incorrect is the penalty for choosing the wrong mode, R switch_cost is the penalty for frequent mode switching, R efficiency incentives for energy efficiency; S26. Repeat steps S22 to S25 to store the current training form of the robot, the robot's actions, the modality correctness reward function, and the adjusted robot training state into an experience replay pool to obtain a training experience replay pool. S27, input the training experience replay pool into the Q network, and update the parameters of the Q network by formula (2) to obtain the final parameters of the Q network. Where, Q(s t ,a t ) is the action quality function of the current training form, s t is the current training form of the robot, a t is the action of the robot, a′ is the action of any robot, r t is the correctness reward function of the modality, s t+1 is the adjusted robot training state, γ is the discount factor, and α is the learning rate; S28. Inputting the parameters of the final Q network into the Q network to obtain a mode selector based on reinforcement learning; The greedy strategy includes: Strategy 1: Select a random robot action instruction with probability ∈; Strategy 2: Select the robot’s action instruction with the maximum action quality function of the current training form with probability 1-∈.

2. The motion mode control method of a robot with a dual-purpose tail fin for water and air according to claim 1, characterized in that: The sensor system comprises: Vision sensors, used to collect images and environmental features; An inertial measurement sensor for collecting acceleration, angular velocity and posture data of the robot; The depth sensor is used to collect the vertical position information of the robot, and the vertical position information includes the depth in water and the flying height in the air.

3. The motion mode control method of a robot with a dual-purpose tail fin for water and air according to claim 2, characterized in that: In S1, data is collected through the sensor system to obtain a data set of the current state, including: S11. Extract features using a convolutional neural network based on the visual sensor to obtain visual features of the environment; S12. Obtaining acceleration, angular velocity, and posture data of the robot by collecting data according to the inertial measurement sensor; S13. Obtaining vertical position information of the robot by collecting data using the depth sensor; S14. Perform matrix processing based on the visual features of the environment, the acceleration of the robot, the angular velocity of the robot, the posture data of the robot, and the position information of the robot in the vertical direction to obtain a data set of the current state.

4. The motion mode control method of a robot with a dual-purpose tail fin for water and air according to claim 1, characterized in that: In S3, the robot is controlled by the controller system according to the current environmental modality to obtain the current form of the robot, including: S31. Controlling the fixed-wing propulsion system through a controller system according to the current environmental modality to obtain a configuration of the fixed-wing propulsion system; S32. Controlling the variable structure membrane wing system through the controller system according to the current environmental mode to obtain a shape of the variable structure membrane wing system; S33, controlling the pectoral fin system through the controller system according to the current environmental modality to obtain a shape of the pectoral fin system; S34. Controlling the body cabin system through the controller system according to the current environmental modality to obtain a configuration of the body cabin system; S35. Controlling the multi-joint tail cabin system through the controller system according to the current environmental modality to obtain a configuration of the multi-joint tail cabin system; S36, controlling the dual-purpose water-air tail fin system through the controller system according to the current environmental mode to obtain a shape of the dual-purpose water-air tail fin system; S37. Obtain the current form of the robot based on the form of the fixed-wing propulsion system, the form of the variable structure membrane wing system, the form of the pectoral fin system, the form of the body cabin system, the form of the multi-joint tail cabin system and the form of the water-air dual-purpose tail fin system.

5. The motion mode control method of a robot with a dual-purpose tail fin for water and air according to claim 4, characterized in that: The dual-purpose water-air tail fin system includes: The rudder under the tail fin is used to provide a control surface below to adjust the direction of the flow in water or air; The rudder surface on the tail fin is used to provide an upper control surface to adjust the direction of the flow in water or air; The rudder under the tail fin drives the servo, which is used to adjust the angle of the rudder under the tail fin; Rudder arm, used to transmit driving force; A bracket, used for fixing the lower rudder surface of the tail fin and the upper rudder surface of the tail fin; A planetary gear, used to adjust the angle of the control surface on the tail fin; A planetary gear fixing frame, used for fixing the planetary gear and connecting the multi-joint tail cabin system; The lower tail fin rudder surface driving servo is fixedly connected to the rudder arm, and the rudder arm is fixedly connected to the lower tail fin rudder surface. The lower tail fin rudder surface driving servo performs rotational motion to adjust the angle between the lower tail fin rudder surface and the upper tail fin rudder surface; The planetary gear is fixedly connected to the upper control surface of the tail fin, and the rotation of the planetary gear realizes the V-shaped adjustment movement of the upper control surface of the tail fin.

6. The motion mode control method of a robot with a dual-purpose tail fin for water and air according to claim 5, characterized in that: The planetary gear includes a driving gear, a transmission gear 1, a transmission gear 2 and a transmission gear 3. The driving gear rotates under the drive of the transmission shaft, and the transmission gear 1, the transmission gear 2 and the transmission gear 3 are respectively engaged with the driving gear.

7. The motion mode control method of a robot with a dual-purpose tail fin for water and air according to claim 1, characterized in that: The stable forms of the robot include underwater mode, water-into-air mode, air mode and air-into-water mode.

8. A robot motion mode control system with a dual-purpose tail fin for water and air, used to implement the robot motion mode control method with a dual-purpose tail fin for water and air as claimed in any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to collect data through the sensor system to obtain the current state data set; A modality selection module, configured to select a modality based on the current state data set using a reinforcement learning-based modality selector to obtain a current environment modality; a first acquisition module, configured to control the robot through a controller system according to the current environmental modality to obtain the current form of the robot; A second acquisition module is configured to obtain the actual state of the robot through a feedback system of the robot according to the current state of the robot; The third acquisition module is used to obtain a stable form of the robot through adjustment by the controller system according to the actual state of the robot.

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