Amphibious unmanned vehicle land and water actuator cooperative control method

Abstract

The invention relates to an amphibious unmanned vehicle amphibious actuator cooperative control method, belongs to the technical field of amphibious unmanned vehicles, and solves the problem of poor adaptability of amphibious unmanned vehicles in different scenes in the prior art. The method comprises the following steps: acquiring suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment; at the current moment, predicting the overwater model mismatch probability based on the trained recurrent neural network according to the suspension state data and the attitude angle data at the current moment and the historical moment; determining whether the current scene key point is a scene key point based on the predicted overwater model mismatch probability; the scene key points comprise a landing point, a water leaving point, a water entering point and a full floating point; and calculating an actuating mechanism action instruction of the amphibious unmanned vehicle according to whether the current key point is the scene key point or not and the predicted overwater model mismatch probability. And stable control of the amphibious unmanned vehicle under the land and water working conditions is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of amphibious unmanned vehicles, and in particular to a method for coordinated control of amphibious actuators of an amphibious unmanned vehicle. Background Art

[0002] Amphibious unmanned vehicles are vehicles that can travel across land and water, and have important research value. Because amphibious unmanned vehicles carry two sets of relatively independent and coupled actuators on land and water at the same time, and need to cope with a variety of complex and significantly different scenarios such as on land, on water, and at the junction of land and water, there is a problem of coordinating the two sets of actuators on land and water for different scenarios. Under land and water conditions, it is necessary to control the land actuator and the water actuator at the same time. Most existing technologies do not consider the coordinated control of the two sets of actuators on land and water and the stable switching of working conditions, and have poor adaptability to different scenarios. Summary of the invention

[0003] In view of the above analysis, an embodiment of the present invention aims to provide a method for coordinated control of amphibious actuators of an amphibious unmanned vehicle, so as to solve the problem of poor adaptability of existing amphibious unmanned vehicles to different scenarios.

[0004] On the one hand, an embodiment of the present invention provides a method for cooperatively controlling water and land actuators of an amphibious unmanned vehicle, comprising the following steps:

[0005] Obtain the suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment;

[0006] At the current moment, the probability of water model mismatch is predicted based on the trained recurrent neural network according to the suspension state data and attitude angle data at the current moment and historical moments;

[0007] Determine whether the current scene is a key point based on the predicted water model mismatch probability; the key points of the scene include: landing point, water departure point, water entry point and full floating point;

[0008] The actuator action instructions of the amphibious unmanned vehicle are solved according to whether the current scene is a key point and the predicted water model mismatch probability.

[0009] Based on the further improvement of the above method, whether the current point is a scene key point is determined based on the predicted water model mismatch probability, including:

[0010] If the mismatch probability of the water model predicted at the current moment is greater than 0, and the mismatch probability of the water model predicted at the previous moment is equal to 0, then the current point is the landing point;

[0011] If the mismatch probability of the water model predicted at the current moment is greater than 90%, and the mismatch probability of the water model predicted at the previous moment is less than or equal to 90%, then the current point is off the water;

[0012] If the mismatch probability of the water model predicted at the current moment is less than 100%, and the mismatch probability of the water model predicted at the previous moment is equal to 100%, then the current moment is the entry point;

[0013] If the mismatch probability of the water model predicted at the current moment is less than 10%, and the mismatch probability of the water model predicted at the previous moment is greater than or equal to 10%, the current one is full floating point.

[0014] Based on the further improvement of the above method, the actuator of the amphibious unmanned vehicle includes a gear, a throttle, a water direction control mechanism and a land direction control mechanism;

[0015] The actuator action instructions of the amphibious unmanned vehicle are solved according to whether the current scene is a key point and the predicted water model mismatch probability, including:

[0016] Determine the current solution mode and gear position instruction according to whether the current scene is a key point, wherein the solution mode includes water mode, land-water mode and land mode;

[0017] Calculate the throttle opening according to the current solution mode and the predicted water model mismatch probability;

[0018] If the current mode is land mode, the control instructions of the land direction control mechanism are determined according to the expected curvature; if the current mode is water mode, the control instructions of the water direction control mechanism are determined according to the expected curvature; if the current mode is land and water mode, the control instructions of the land direction control mechanism and the water direction control mechanism are determined according to the expected curvature.

[0019] Based on the further improvement of the above method, the current solution mode and gear position instruction are determined according to whether the current scene is a key point, including:

[0020] If the current point is the water entry point, the solution mode is the water-land mode;

[0021] If the current mode is full floating point, the solution mode is the water mode, and the gear command is the gear disengagement command;

[0022] If the current point is a landing point, the solution mode is the land-water mode, and the gear command is the gear engagement command;

[0023] If the current point is out of water, the solution mode is on land mode.

[0024] Based on the further improvement of the above method, the throttle opening is calculated according to the current solution mode and the predicted water model mismatch probability, including:

[0025] Determine the conversion coefficient between the engine speed and the speed of the unmanned vehicle based on the predicted water model mismatch probability;

[0026] If the current solution mode is the land mode or the water-land mode, the throttle opening is calculated according to the expected speed and the conversion coefficient;

[0027] If the current solution mode is the water mode, the expected speed is corrected, and the throttle opening is calculated based on the corrected expected speed and the conversion coefficient.

[0028] Based on the further improvement of the above method, the conversion coefficient between the engine speed and the unmanned vehicle speed is determined by the following formula based on the predicted water model mismatch probability:

[0029]

[0030] Among them, λ represents the conversion coefficient between engine speed and unmanned vehicle speed, represents the predicted water model mismatch probability, λ l represents the longitudinal kinematic model coefficient for onshore conditions, λ w is the longitudinal kinematic model coefficient of the water condition.

[0031] Based on the further improvement of the above method, the expected speed is corrected, including:

[0032] Calculate the theoretical flow velocity characteristics and calculated flow velocity characteristics within the characteristic time period corresponding to the current moment;

[0033] Obtaining a correction coefficient based on the trained correction coefficient prediction model according to the theoretical flow velocity characteristics and the calculated flow velocity characteristics;

[0034] The expected speed is corrected according to the correction factor.

[0035] Based on the further improvement of the above method, the recurrent neural network is trained in the following way:

[0036] The speed, engine speed, suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment in the entire history are obtained, and the water model mismatch probability at each moment is calculated according to the speed and engine speed; the features of each moment are calculated based on the suspension state data and attitude angle data at each moment; the features of k+1 moments are used as the input data of a sample, and the average water model mismatch probability at k+1 moments after the k+1 moment is used as the label of the sample to construct a training sample set;

[0037] Constructing a recurrent neural network model; training the constructed recurrent neural network model based on the training sample set to obtain a trained recurrent neural network model.

[0038] Based on the further improvement of the above method, the features at each moment are calculated based on the suspension state data and attitude angle data at each moment, including:

[0039] For each moment, the time period from the sth moment before the moment to the moment is the characteristic time period corresponding to the moment;

[0040] The maximum value of the suspension state data, the average value of the suspension state data, the variance of the suspension state data, the maximum value of the attitude angle data, the average value of the attitude angle data and the variance of the attitude angle data in the characteristic time period corresponding to the moment are calculated as the features corresponding to the moment.

[0041] Based on the further improvement of the above method, the water model mismatch probability at each moment is calculated according to the speed and engine speed in the following way:

[0042]

[0043] Where v represents speed, n represents engine speed, and λ w represents the kinematic model coefficient of the water working condition, λ l Represents the kinematic model coefficient of onshore conditions.

[0044] Compared with the prior art, the present invention obtains the suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment; predicts the water model mismatch probability at each current moment, determines whether the current moment is a scene key point based on the predicted water model mismatch probability, and confirms the solution mode according to whether the current moment is a scene key point and the predicted water model mismatch probability to solve the actuator action instructions of the amphibious unmanned vehicle, thereby realizing the coordinated stable process control of the water actuator and the land actuator.

[0045] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0047] Figure 1 This is a flow chart of a method for cooperatively controlling water and land actuators of an amphibious unmanned vehicle according to an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of key points of an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0050] A specific embodiment of the present invention discloses a method for cooperatively controlling water and land actuators of an amphibious unmanned vehicle, such as Figure 1 As shown, the following steps are included:

[0051] S1. Obtain the suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment;

[0052] S2, at the current moment, predict the probability of water model mismatch based on the trained recurrent neural network according to the suspension state data and attitude angle data at the current moment and historical moments;

[0053] S3, determining whether the current scene is a key point based on the predicted water model mismatch probability; the key points of the scene include: landing point, water departure point, water entry point and full floating point;

[0054] S4. Calculate the actuator action instructions of the amphibious unmanned vehicle according to whether the current scene is a key point and the predicted water model mismatch probability.

[0055] The autonomous planning module of the amphibious unmanned vehicle uses intelligent sensors, such as image sensors, to perform visual detection to obtain environmental information, and plans the driving strategy based on intelligent algorithms such as machine learning, reinforcement learning, and deep learning according to the mission objectives, status information, and environmental information of the unmanned platform, and sends control instructions to the collaborative control end to control the unmanned platform to run towards the planned target. The working conditions of the amphibious unmanned vehicle include water conditions, land conditions, and water and land conditions. The amphibious unmanned vehicle water and land actuator collaborative control method of the present invention is mainly applied to the water and land conditions of amphibious unmanned vehicles. During implementation, the suspension displacement change data is collected in real time by the suspension displacement sensor, and the attitude angle data is collected in real time by the combined navigation system, such as inertial navigation, so as to obtain the suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment.

[0056] The water model mismatch probability is used to characterize the response state of the actuator of the amphibious unmanned vehicle under water and land conditions, and is an important feedback for the vehicle to perceive the characteristics of the water-land interface scene. Figure 2 As shown in FIG. 1 , when the amphibious unmanned vehicle enters the water, the track is affected by the water environment, and the probability of water model mismatch gradually decreases; when the amphibious unmanned vehicle lands, the track gradually touches the ground, and the probability of water model mismatch gradually increases. The water model mismatch probability predicted in step S2 is the predicted water model mismatch probability at the future moment.

[0057] Specifically, the key points of the scene include: landing point, water departure point, water entry point and full floating point.

[0058] The landing point is the key point where the track first touches the shore during the landing process of the amphibious unmanned vehicle. After this point, the amphibious unmanned vehicle should simultaneously control the land actuator and the water actuator to achieve the landing action.

[0059] The departure point is the critical point during the landing process of the amphibious unmanned vehicle when the water actuator completely fails (the water gate completely leaves the water surface). After this point, the amphibious unmanned vehicle should only control the land actuator to complete the landing mission.

[0060] The entry point is the key point when the water actuator starts to work (the water gate starts to contact the water surface) during the launching process of the amphibious unmanned vehicle. After this point, the amphibious unmanned vehicle should simultaneously control the land actuator and the water actuator to realize the entry action.

[0061] The full floating point is the critical point when the tracks are completely submerged in water during the launching process of the amphibious unmanned vehicle. After this point, the amphibious unmanned vehicle should only control the water actuators to complete the water navigation mission.

[0062] Compared with the prior art, the method for collaborative control of water and land actuators of an amphibious unmanned vehicle provided in this embodiment obtains the suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment; predicts the water model mismatch probability at each current moment, determines whether the current moment is a scene key point based on the predicted water model mismatch probability, and confirms the solution mode according to whether the current moment is a scene key point and the predicted water model mismatch probability to solve the actuator action instructions of the amphibious unmanned vehicle, thereby realizing collaborative stable process control of the water actuator and the land actuator.

[0063] Taking into account the response delay of the actuator and the smoothness of the control process, the probability of water model mismatch is predicted to avoid the problem of delayed judgment of key points caused by the current model mismatch probability. For example, during the landing process, it is necessary to shift gears and accelerate some time in advance. If the current water model mismatch probability is used to determine that the landing point is shifted and accelerated, there will be a stuck situation at the touchdown point.

[0064] The suspension state and attitude angle changes of amphibious unmanned vehicles near key points are also important indicators for amphibious unmanned vehicles to perceive the characteristics of water-land handover scenes. For example, during the water entry process, the tracks gradually leave the ground, the suspension force changes, and the suspension displacement sensor will show obvious characteristics; during the landing process, because the tracks are gradually touching the ground, the influence of water flow on the attitude angle will gradually weaken. Therefore, the suspension state data and attitude angle data are extracted as features.

[0065] Specifically, the recurrent neural network is trained in the following way:

[0066] The speed, engine speed, suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment in the entire history are obtained, and the water model mismatch probability at each moment is calculated according to the speed and engine speed; the features of each moment are calculated based on the suspension state data and attitude angle data at each moment; the features of k+1 moments are used as the input data of a sample, and the average water model mismatch probability at k+1 moments after the k+1 moment is used as the label of the sample to construct a training sample set;

[0067] Constructing a recurrent neural network model; training the constructed recurrent neural network model based on the training sample set to obtain a trained recurrent neural network model.

[0068] During implementation, the entire historical process includes the entire process of the unmanned vehicle moving from land to water and from water to road.

[0069] During implementation, the water model mismatch probability at each moment is calculated according to the speed and engine speed in the following way:

[0070]

[0071] Where v represents speed, n represents engine speed, and λ w represents the kinematic model coefficient of the water working condition, λ l Represents the kinematic model coefficient of onshore conditions. l and λ w is the known quantity calibrated in the experiment, corresponding to the conversion coefficients of vehicle speed on land and water and engine speed, and λ l >λ w .

[0072] Specifically, the features at each moment are calculated based on the suspension state data and attitude angle data at each moment, including:

[0073] For each moment, the time period from the sth moment before the moment to the moment is the characteristic time period corresponding to the moment;

[0074] The maximum value of the suspension state data, the average value of the suspension state data, the variance of the suspension state data, the maximum value of the attitude angle data, the average value of the attitude angle data and the variance of the attitude angle data in the characteristic time period corresponding to the moment are calculated as the features corresponding to the moment.

[0075] For example, for time t, the time period between time ts and time t is the feature time period corresponding to the time. The maximum value of the suspension state data, the average value of the suspension state data, the variance of the suspension state data, the maximum value of the attitude angle data, the average value of the attitude angle data and the variance of the attitude angle data in the feature time period are taken as the features corresponding to the time t, and the features corresponding to each time are obtained.

[0076] During implementation, the time step length of the sample is k+1, that is, the features of k+1 moments are used as the input data of one sample, and the trained recurrent neural network is used to predict the future water model adaptation probability. Therefore, the average water model mismatch probability of k+1 moments after the k+1 moment is used as the label to obtain a sample.

[0077] At each current moment of the water-land working condition, the probability of water model mismatch is predicted based on the trained recurrent neural network according to the suspension state data and attitude angle data at the current moment and historical moments.

[0078] Specifically, the historical moment is the k moments before the current moment. For example, if the current moment is t, then the moments tk, t-(k-1), …, t-1 are taken as the historical moments.

[0079] The features corresponding to the current moment and each historical moment are calculated according to the above-mentioned method to form a feature sequence, and the feature sequence is input into the trained recurrent neural network to predict the water model mismatch probability, that is, the average water model mismatch probability in the future k+1 moments.

[0080] After the water model mismatch probability is predicted, determine whether the current point is a scene key point based on the predicted water model mismatch probability:

[0081] If the mismatch probability of the water model predicted at the current moment is greater than 0, and the mismatch probability of the water model predicted at the previous moment is equal to 0, then the current point is the landing point;

[0082] If the mismatch probability of the water model predicted at the current moment is greater than 90%, and the mismatch probability of the water model predicted at the previous moment is less than or equal to 90%, then the current point is off the water;

[0083] If the mismatch probability of the water model predicted at the current moment is less than 100%, and the mismatch probability of the water model predicted at the previous moment is equal to 100%, then the current moment is the entry point;

[0084] If the mismatch probability of the water model predicted at the current moment is less than 10%, and the mismatch probability of the water model predicted at the previous moment is greater than or equal to 10%, the current one is full floating point.

[0085] The actuator includes a gear, a throttle, a water direction control mechanism and a land direction control mechanism. The water direction control mechanism includes a steering wheel, and the land direction control mechanism includes a steering rudder and a water gate.

[0086] The actuator action instructions of the amphibious unmanned vehicle are solved according to whether the current scene is a key point and the predicted water model mismatch probability, including:

[0087] S41, determining a current solution mode and a gear position instruction according to whether the current scene is a key point, wherein the solution mode includes an overwater mode, an overwater mode, and an overland mode;

[0088] S41, calculating the throttle opening according to the current solution mode and the predicted water model mismatch probability;

[0089] S43. If the current mode is land mode, the control instructions of the land direction control mechanism are determined according to the expected curvature; if the current mode is water mode, the control instructions of the water direction control mechanism are determined according to the expected curvature; if the current mode is land-water mode, the control instructions of the land direction control mechanism and the water direction control mechanism are determined according to the expected curvature.

[0090] During implementation, the solution modes include water mode, water-land mode and land mode. During the launching process, the land mode is used before the water entry point, and only the action instructions of the land actuator are controlled; the water-land mode is used between the water entry point and the full floating point, and the action instructions of the land actuator structure and the water actuator need to be calculated at the same time; after the full floating point, the water mode is used, and only the action instructions of the water actuator are controlled.

[0091] Similarly, during the landing process, the mode is on water before the landing point, and only the action instructions of the on-water actuator need to be controlled; the mode is on land between the landing point and the full floating point, and the action instructions of the on-land actuator structure and the on-water actuator need to be calculated at the same time; after leaving the water point, the mode is on land, and only the action instructions of the on-land actuator need to be controlled.

[0092] Specifically, the current solution mode and gear position instruction are determined according to whether the current scene is a key point, including:

[0093] If the current point is the water entry point, the solution mode is the water-land mode;

[0094] If the current mode is full floating point, the solution mode is the water mode, and the gear command is the gear disengagement command;

[0095] If the current point is a landing point, the solution mode is the land-water mode, and the gear command is the gear engagement command;

[0096] If the current point is out of water, the solution mode is on land mode.

[0097] If the current point is a water entry point or a landing point, the solution mode is set to the water-land mode; if the current point is a full floating point, the mode is set to the water mode; if the current point is a water departure point, the mode is set to the land mode.

[0098] Considering that the rotation of the tracks on water will consume part of the power, resulting in insufficient propeller power, the gear should be disengaged at the full floating point; considering that the driving resistance increases after the tracks touch the ground and the propeller is not sufficient to push the vehicle ashore, the gear should be engaged at the landing point.

[0099] Specifically, the throttle opening is calculated according to the current solution mode and the predicted water model mismatch probability, including:

[0100] Determine the conversion coefficient between the engine speed and the speed of the unmanned vehicle based on the predicted water model mismatch probability;

[0101] If the current solution mode is the land mode or the water-land mode, the throttle opening is calculated according to the expected speed and the conversion coefficient;

[0102] If the current solution mode is the water mode, the expected speed is corrected, and the throttle opening is calculated based on the corrected expected speed and the conversion coefficient.

[0103] Specifically, the conversion coefficient between the engine speed and the speed of the unmanned vehicle is determined by the following formula based on the predicted water model mismatch probability:

[0104]

[0105] Among them, λ represents the conversion coefficient between engine speed and unmanned vehicle speed, represents the predicted water model mismatch probability, λ l represents the longitudinal kinematic model coefficient for onshore conditions, λ w is the longitudinal kinematic model coefficient of the water condition.

[0106] If the current mode is the land mode or the water and land mode, the throttle opening is directly calculated according to the expected speed and the conversion coefficient.

[0107] Specifically, the throttle opening is calculated using the following formula:

[0108]

[0109] Δn=n des -n

[0110] Θ(k+1)=Θ(k)+pΔn

[0111] Among them, v des represents the expected speed, n des represents the expected engine speed, n represents the current engine speed, Θ(k) represents the current throttle opening, Θ(k+1) represents the calculated throttle opening, and p represents the coefficient.

[0112] The expected speed is determined by the planning of the amphibious unmanned vehicle. After the throttle opening is calculated, the corresponding control command is generated to control the throttle opening.

[0113] The water speed meter based on Bernoulli principle can accurately measure the vehicle speed in still water. However, under the same vehicle speed in upstream conditions, the relative water flow velocity is higher than that in still water, and the pressure is lower, so the vehicle speed data collected by the water speed meter will be lower than the actual vehicle speed. Similarly, the water speed data collected by the water speed meter in downstream conditions is higher than the actual vehicle speed.

[0114] Therefore, if the current solution mode is the water mode, the expected speed needs to be corrected first, and the throttle opening is calculated based on the corrected expected speed and the conversion coefficient.

[0115] Specifically, the expected speed is corrected, including:

[0116] Calculate the theoretical flow velocity characteristics and calculated flow velocity characteristics within the characteristic time period corresponding to the current moment;

[0117] Obtaining a correction coefficient based on the trained correction coefficient prediction model according to the theoretical flow velocity characteristics and the calculated flow velocity characteristics;

[0118] The expected speed is corrected according to the correction factor.

[0119] Specifically, the trained neural network model is obtained in the following way:

[0120] Collect the actual speed and water speed meter speed of the amphibious unmanned vehicle at each moment in the entire historical process to calculate the theoretical flow rate; calculate the flow rate based on the expected speed and actual speed at each moment;

[0121] For each moment, the time period from the sth moment before the moment to the moment is taken as the characteristic time period corresponding to the moment; the theoretical maximum flow rate, average value and variance of the characteristic time period corresponding to the moment are calculated as the theoretical flow rate characteristics corresponding to the moment, and the maximum flow rate, average value and attitude variance are calculated as the calculated flow rate characteristics corresponding to the moment;

[0122] The theoretical flow velocity characteristics and calculated flow velocity characteristics at each moment are used as the input of a sample, and the ratio of the expected velocity at the moment to the time velocity is used as a label to construct a training sample set;

[0123] A neural network model is constructed, and the neural network model is trained based on the constructed training sample set to obtain a trained correction coefficient prediction model.

[0124] During implementation, the vehicle speed collected by the inertial navigation is taken as the real vehicle speed v IMU , the vehicle speed v collected by the water speed meter WS , then the physical quantity that can quantitatively reflect the water flow velocity characteristics is the theoretical flow velocity v t for:

[0125] v t =v IMU -vWS

[0126] Assuming that the speed control deviation is mainly caused by the water flow velocity, the physical quantity that can reflect the deviation between the expected speed and the actual speed of the vehicle is the calculated flow velocity v c It is expressed as:

[0127] v c =v des -v IMU

[0128] During implementation, a neural network model may be constructed using a multi-layer perceptron model.

[0129] According to the above method, the theoretical flow velocity characteristics and calculated flow velocity characteristics in the characteristic time period corresponding to the current moment are calculated, and the correction coefficient is obtained by inputting the trained correction coefficient prediction model.

[0130] Correct the expected speed v′ according to the correction factor des =γ*v des , γ represents the correction coefficient, v des represents the expected speed, v′ des Indicates the corrected desired speed. Substitute the corrected desired speed into the above formula for calculating the throttle opening to calculate the throttle opening. Generate the corresponding control command to control the throttle opening.

[0131] Control the direction control mechanism according to the current solution mode.

[0132] Specifically, if the current mode is land mode, the control instructions of the land direction control mechanism are determined according to the expected curvature; if the current mode is water mode, the control instructions of the water direction control mechanism are determined according to the expected curvature; if the current mode is land and water mode, the control instructions of the land direction control mechanism and the water direction control mechanism are determined according to the expected curvature.

[0133] Although each steering actuator and its control process are relatively independent, when multiple actuators jointly and independently achieve the same steering target, over-execution may occur. However, considering the different characteristics of the land and water scenes in which amphibious unmanned vehicles are located (track contact area, steering rudder and water gate immersion depth, etc.), the response capabilities of the two sets of land and water steering actuators will also change (it is possible that a single actuator cannot achieve the expected curvature even if it sends the expected curvature, but multiple actuators can achieve the effect together), so the expected curvature of the actuator is not corrected. In addition, the land and water working conditions usually perform straight-line driving tasks for landing or launching, and the steering control accuracy requirements are relatively low, so only a simple description of the solution of the steering control amount is given.

[0134] If the current mode is on land, the steering wheel angle of the on-land direction control mechanism, the steering wheel, = steering wheel steering coefficient * expected curvature, and the steering action is realized by generating corresponding control instructions. The steering coefficient is calibrated through experiments.

[0135] If the current mode is on water, the angle of the steering rudder = steering coefficient of the steering rudder * expected curvature, and the steering action is achieved by generating the corresponding control command. If the expected curvature is greater than the steering threshold of the steering rudder, the steering direction is determined according to the expected curvature, and the corresponding water gate is controlled to close. The steering coefficient is calibrated through experiments.

[0136] When the expected curvature is greater than the steering threshold of the steering rudder, the water gate is used to compensate for the insufficient response ability of the steering rudder. For example, the steering direction is determined according to the positive or negative value of the expected curvature, and the corresponding water gate is closed. For example, if it is determined to be a left turn, the left water gate is closed.

[0137] If the current mode is land and water mode, the steering wheel and steering rudder are solved at the same time to determine the corresponding control instructions, so as to avoid the cross-border working conditions affecting the response state of the actuator.

[0138] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0139] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for coordinated control of amphibious actuators of an amphibious unmanned vehicle, characterized in that: The following steps are involved: Obtain the suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment; At the current moment, the probability of water model mismatch is predicted based on the trained recurrent neural network according to the suspension state data and attitude angle data at the current moment and historical moments; Determine whether the current point is a scene key point based on the predicted water model mismatch probability; The key points of the scene include: landing point, water departure point, water entry point and full floating point; The actuator action instructions of the amphibious unmanned vehicle are solved according to whether the current scene is a key point and the predicted water model mismatch probability.

2. The method for cooperatively controlling the amphibious actuators of an amphibious unmanned vehicle according to claim 1, characterized in that: Determine whether the current scene is a key point based on the predicted water model mismatch probability, including: If the mismatch probability of the water model predicted at the current moment is greater than 0, and the mismatch probability of the water model predicted at the previous moment is equal to 0, then the current point is the landing point; If the mismatch probability of the water model predicted at the current moment is greater than 90%, and the mismatch probability of the water model predicted at the previous moment is less than or equal to 90%, then the current point is off the water; If the mismatch probability of the water model predicted at the current moment is less than 100%, and the mismatch probability of the water model predicted at the previous moment is equal to 100%, then the current moment is the entry point; If the mismatch probability of the water model predicted at the current moment is less than 10%, and the mismatch probability of the water model predicted at the previous moment is greater than or equal to 10%, the current one is full floating point.

3. The method for coordinated control of amphibious unmanned vehicle amphibious actuators according to claim 1, characterized in that: The actuators of the amphibious unmanned vehicle include gears, throttle, water direction control mechanism and land direction control mechanism; The actuator action instructions of the amphibious unmanned vehicle are solved according to whether the current scene is a key point and the predicted water model mismatch probability, including: Determine the current solution mode and gear position instruction according to whether the current scene is a key point, wherein the solution mode includes water mode, land-water mode and land mode; Calculate the throttle opening according to the current solution mode and the predicted water model mismatch probability; If the current mode is land mode, the control instructions of the land direction control mechanism are determined according to the expected curvature; if the current mode is water mode, the control instructions of the water direction control mechanism are determined according to the expected curvature; if the current mode is land and water mode, the control instructions of the land direction control mechanism and the water direction control mechanism are determined according to the expected curvature.

4. The method for coordinated control of amphibious unmanned vehicle amphibious actuators according to claim 3 is characterized in that: The current solution mode and gear position command are determined based on whether the current scene is a key point, including: If the current point is the water entry point, the solution mode is the water-land mode; If the current mode is full floating point, the solution mode is the water mode, and the gear command is the gear disengagement command; If the current point is a landing point, the solution mode is the land-water mode, and the gear command is the gear engagement command; If the current point is out of water, the solution mode is on land mode.

5. The method for coordinated control of amphibious unmanned vehicle amphibious actuators according to claim 3 is characterized in that: Calculate the throttle opening according to the current solution mode and the predicted water model mismatch probability, including: Determine the conversion coefficient between the engine speed and the speed of the unmanned vehicle based on the predicted water model mismatch probability; If the current solution mode is the land mode or the water-land mode, the throttle opening is calculated according to the expected speed and the conversion coefficient; If the current solution mode is the water mode, the expected speed is corrected, and the throttle opening is calculated based on the corrected expected speed and the conversion coefficient.

6. The method for coordinated control of amphibious unmanned vehicle amphibious actuators according to claim 5 is characterized in that: Based on the predicted water model mismatch probability, the conversion coefficient between the engine speed and the unmanned vehicle speed is determined using the following formula: Among them, λ represents the conversion coefficient between engine speed and unmanned vehicle speed, represents the predicted water model mismatch probability, λ l represents the longitudinal kinematic model coefficient for onshore conditions, λ w is the longitudinal kinematic model coefficient of the water condition.

7. The method for coordinated control of amphibious unmanned vehicle amphibious actuators according to claim 5, characterized in that: Corrections to the desired speed include: Calculate the theoretical flow velocity characteristics and calculated flow velocity characteristics within the characteristic time period corresponding to the current moment; Obtaining a correction coefficient based on the trained correction coefficient prediction model according to the theoretical flow velocity characteristics and the calculated flow velocity characteristics; The expected speed is corrected according to the correction factor.

8. The method for coordinated control of amphibious unmanned vehicle amphibious actuators according to claim 1, characterized in that: The recurrent neural network is trained in the following way: The speed, engine speed, suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment in the entire history are obtained, and the water model mismatch probability at each moment is calculated according to the speed and engine speed; the features of each moment are calculated based on the suspension state data and attitude angle data at each moment; the features of k+1 moments are used as the input data of a sample, and the average water model mismatch probability at k+1 moments after the k+1 moment is used as the label of the sample to construct a training sample set; Constructing a recurrent neural network model; training the constructed recurrent neural network model based on the training sample set to obtain a trained recurrent neural network model.

9. The method for coordinated control of amphibious unmanned vehicle amphibious actuators according to claim 8, characterized in that: The features of each moment are calculated based on the suspension state data and attitude angle data at each moment, including: For each moment, the time period from the sth moment before the moment to the moment is the characteristic time period corresponding to the moment; The maximum value of the suspension state data, the average value of the suspension state data, the variance of the suspension state data, the maximum value of the attitude angle data, the average value of the attitude angle data and the variance of the attitude angle data in the characteristic time period corresponding to the moment are calculated as the features corresponding to the moment.

10. The method for coordinated control of amphibious unmanned vehicle amphibious actuators according to claim 8, characterized in that: The probability of water model mismatch at each moment is calculated based on the speed and engine speed in the following way: Where v represents speed, n represents engine speed, and λ w represents the kinematic model coefficient of the water working condition, λ l Represents the kinematic model coefficient of onshore conditions.

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