CN112904873A - Bionic robot fish control method and device based on deep reinforcement learning and storage medium - Google Patents
Bionic robot fish control method and device based on deep reinforcement learning and storage medium Download PDFInfo
- Publication number
- CN112904873A CN112904873A CN202110110948.2A CN202110110948A CN112904873A CN 112904873 A CN112904873 A CN 112904873A CN 202110110948 A CN202110110948 A CN 202110110948A CN 112904873 A CN112904873 A CN 112904873A
- Authority
- CN
- China
- Prior art keywords
- network
- reinforcement learning
- deep reinforcement
- deep
- bionic robot
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/80—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in fisheries management
Abstract
The invention provides a bionic robot fish control method and device based on deep reinforcement learning and a storage medium thereof, belonging to the technical field of bionic robot control. The method solves the problems that a bionic robot fish joint motion control method based on a deep reinforcement learning CPG network aiming at joint swimming control of the bionic robot fish is lacked in the prior art. The invention comprises S1: constructing an outer-layer bionic robot fish information network through deep learning, and giving a preliminary instruction through interaction with the environment; s2: and constructing an inner CPG network aiming at the preliminary instruction, and giving a specific joint movement instruction by constructing a movement model based on a central pattern generator. The invention has the advantages of regulating the bionic fish in a complex underwater environment and the like.
Description
Technical Field
The invention belongs to the technical field of bionic robot control, and particularly relates to a bionic robot fish control method and device based on deep reinforcement learning and a storage medium thereof.
Background
Deep Reinforcement Learning mainly comprises Deep Learning (Deep Learning) and Reinforcement Learning (Reinforcement Learning). The concept of deep learning was first derived from Artificial Neural Networks (ANN). The model is usually formed by combining multiple layers of nonlinear operation units, and the output of a lower layer is used as the input of a higher layer, so that abstract feature representation is learned from a large amount of training data, and distributed features of the data are discovered. The deep learning theory can effectively mine the deep characteristics of data, and an important branch diagram neural network can effectively break through the limitation of the traditional neural network on the requirement of processing images by virtue of the characteristics of the important branch diagram neural network, so that the important branch diagram neural network becomes one of the most important research directions at present. Whereas CPG (central pattern generator) is a neural network that is capable of generating coordinated patterns of rhythmic activity without any rhythm input from sensory feedback or a superior control center. Because of its good performance, CPG-based controls have been widely used to generate various swimming modes, such as forward swimming, backward swimming, and turn. Although more CPG model methods are proposed, the method is simple and difficult to deal with the complex underwater environment, the intelligent degree of the control method is low, the current movement control research of the bionic robot fish integrated with the CPG of the deep reinforcement learning is still in a starting stage, a bionic robot fish joint movement control method based on the CPG network of the deep reinforcement learning aiming at the joint swimming control of the bionic robot fish is lacked, and many researches lack specific implementation schemes.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a bionic robot fish control method and device based on deep reinforcement learning and a storage medium thereof.
The first object of the present invention can be achieved by the following technical solutions: a bionic robot fish control method based on deep reinforcement learning is characterized by comprising the following steps:
s1: constructing an outer-layer bionic robot fish information network through deep learning, and giving a preliminary instruction through interaction with the environment;
s2: constructing an inner CPG network aiming at the preliminary instruction, and giving a specific joint movement instruction by constructing a movement model based on a central pattern generator;
the working principle of the invention is as follows: the method has good applicability to the joint motion control of the bionic robot fish with multiple joints or multiple degrees of freedom in a complex underwater environment, is combined with a deep reinforcement learning network, and provides a joint motion common control method of the bionic robot fish, which integrates an outer bionic robot fish information network and an inner CPG network model, and can realize the intelligent autonomous high-efficiency swimming control of the bionic robot fish.
In the above method for controlling a biomimetic robotic fish based on deep reinforcement learning, the outer-layer biomimetic robotic fish information network includes input information processed by a cooperative conversion method, and the input information is used for generating a deep reinforcement learning network of a preliminary instruction and an inner-layer CPG network transmission interface.
In the depth reinforcement learning-based bionic robot fish control method, the cooperative conversion method includes associating and labeling continuous 4-frame images acquired by an external sensor of the bionic robot fish with depth, distance and more than two kinds of data, and the cooperative conversion method packs the multi-element data into structured data which can be directly processed by a depth network and serves as subsequent depth reinforcement learning network input.
In the control method for the bionic robot fish based on the deep reinforcement learning, the deep reinforcement learning network adopts the deep reinforcement Q learning network to construct the deep network, the initial instruction of the movement of the bionic robot fish is generated through a good processing mechanism of the deep reinforcement Q learning network and the good interaction capacity with the external environment, and the deep network generates the initial instruction and then inputs an inner CPG network interface.
In the above method for controlling a biomimetic robotic fish based on deep reinforcement learning, the deep reinforcement learning network uses DQN algorithm to construct a deep reinforcement learning framework, and inputs the multivariate data into the deep reinforcement learning framework, and the deep reinforcement learning framework generates a corresponding Q value of the input multivariate data by setting a target reward value, and the Q value generation formula is as shown in formula (1):
wherein P isa(s, s ') represents the probability of transition from the current state s to the next state s ', and R (s, s ') represents the reward after the action performed in the current state, being a gamma decay coefficient, maxQ*(s ', a') represents the operation of selecting the current maximum Q value, generating an estimated value of Q through the deep network, and completing the parameter updating of the deep network through the difference between the Q value and the estimated value of Q, as shown in formula (2):
where L (θ) represents a loss function and E represents the desired operation.
In the above method for controlling a biomimetic robotic fish based on deep reinforcement learning, the inner-layer CPG network interface may convert the preliminary instruction and transmit the preliminary instruction into the inner-layer CPG network to implement a specific joint motion of the biomimetic robotic fish, and a generation formula of a specific joint motion model of the biomimetic robotic fish is as follows:
wherein
t is a direction control parameter, theta is a phase difference between neurons,
representing different phases, epsilon, of the ith neuroniω denotes the amplitude and frequency of the neuron, and Pu，PvIs a perturbation term, where Pv＝c2ui+ 1sinθ+c1ui+1cosθ，Pu＝c1ui-1cosθ-c2vi-1sinθ，c1，c2After the CPG model is constructed for the neuron coupling coefficient, the phase output is converted and then input to each joint of the bionic robot fish, as shown in formula (4):
Γi＝ζivi+Θi (4)
wherein gamma isiIndicates the input of the ith joint, ζiThe corresponding transformation coefficient of the joint is determined by the corresponding motor, thetaiAnd finally, finishing interaction and intelligent high-efficiency swimming control of the bionic robot fish in a complex environment through the cooperative cooperation of an outer deep reinforcement learning network and an inner CPG network for the initial instruction coefficient generated by the upper network.
In the above method for controlling a biomimetic robotic fish based on deep reinforcement learning, the inner layer CPG network interface includes a mechanism for decomposing, calibrating and transmitting a preliminary instruction.
In the above method for controlling a biomimetic robotic fish based on deep reinforcement learning, the inner CPG network includes a motion model based on a central pattern generator and a specific joint motion instruction conversion transmission mechanism.
The second object of the present invention can be achieved by the following technical solutions: a bionic robot fish joint motion control device based on a deep reinforcement learning CPG network comprises:
a computer terminal;
a controller;
one or more processors;
a memory;
and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing a method for deep reinforcement learning CPG network based biomimetic robotic fish joint motion control as described above.
The third object of the present invention can be achieved by the following technical solutions: a storage medium storing a computer program for use with a computer and a display, wherein the computer program is executable by a processor to perform the method for controlling joint motion of a biomimetic robotic fish based on a deep reinforcement learning CPG network as described above.
Compared with the prior art, the invention has the advantages of capability of adjusting the bionic fish in a complex underwater environment, sensitive adjustment and the like.
Drawings
FIG. 1 is a schematic diagram of the bionic robot fish joint motion control based on deep reinforcement learning CPG.
FIG. 2 is a schematic diagram of the angle input of a bionic robot fish with an inner layer based on a CPG network.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
As shown in fig. 1-2, the control method of the biomimetic robotic fish based on deep reinforcement learning is characterized by comprising the following steps:
s1: constructing an outer-layer bionic robot fish information network through deep learning, and giving a preliminary instruction through interaction with the environment;
s2: constructing an inner CPG network aiming at the preliminary instruction, and giving a specific joint movement instruction by constructing a movement model based on a central pattern generator;
the outer-layer bionic robot fish information network generates a bionic robot fish joint motion preliminary instruction through constructing a bionic robot fish information network based on deep reinforcement learning and environment interaction, and the inner-layer CPG network converts the preliminary instruction into a motion angle of a specific joint of the bionic robot fish through constructing a rhythm motion network based on CPG, so that joint motion control of the bionic robot fish is realized.
In further detail, the outer-layer bionic robot fish information network comprises input information processed by a cooperative conversion method, the input information is used as a deep reinforcement learning network for generating a preliminary instruction and an inner-layer CPG network transmission interface, the input information is processed by the cooperative conversion method and then serves as input, the preliminary movement instruction of the bionic robot fish is realized by adopting a DQN algorithm framework, the preliminary movement instruction is input to the lower-layer interface, and the movement angle of the specific joint of the bionic robot fish is realized by constructing a CPG-based rhythm movement network.
In further detail, the cooperative conversion method comprises the steps of correlating and labeling continuous 4-frame images acquired by an external sensor of the bionic robot fish with depth, distance and more than two kinds of data, packaging the multi-element data into structured data which can be directly processed by a depth network and used as subsequent deep reinforcement learning network input, correlating image information with the acquired data of the depth, the distance and the like, packaging the data into structured data which can be processed by the depth network and used as the input information of the deep reinforcement learning network for processing.
In further detail, the deep reinforcement learning network adopts a deep reinforcement Q learning network to construct a deep network, a good processing mechanism of the deep reinforcement Q learning network and good interaction capacity with an external environment are used for generating a preliminary instruction of the movement of the bionic robot fish, and the preliminary instruction is generated by the deep network and then is input into an inner CPG network interface.
In further detail, the deep reinforcement learning network utilizes a DQN algorithm to construct a deep reinforcement learning framework, and inputs the multivariate data into the deep reinforcement learning framework, and the deep reinforcement learning framework generates a corresponding Q value of the inputted multivariate data by setting a target reward value, and the Q value generation formula is as shown in formula (1):
wherein P isa(s, s ') represents the probability of transition from the current state s to the next state s ', R (s, s ') represents the reward after performing the action in the current state, is a gamma attenuation coefficient, max Q (s ', a ') represents the operation of selecting the current maximum Q value, generates an estimated value of Q through the depth network, and completes the parameter update of the depth network through the difference between the Q value and the estimated value of Q, as shown in formula (2):
wherein L (theta) represents a loss function, E represents a desired operation, and the network architecture also adopts mechanisms such as memory playback, target network and the like.
In further detail, the inner layer CPG network interface can convert the preliminary instruction and transmit the preliminary instruction into the inner layer CPG network to realize the specific joint motion of the biomimetic robotic fish, and the generation formula of the specific joint motion model of the biomimetic robotic fish is as follows:
wherein
t is a direction control parameter, theta is a phase difference between neurons,
representing different phases, epsilon, of the ith neuroniω denotes the amplitude and frequency of the neuron, and Pu，PvIn order to be a disturbance term,
Pv＝c2ui+1sinθ+c1ui+1cosθ Pu＝c1ui-1cosθ-c2vi-1sinθ
wherein, c1，c2After the CPG model is constructed for the neuron coupling coefficient, the phase output is converted and then input to each joint of the bionic robot fish, as shown in formula (4):
Γi＝ζivi+Θi (4)
wherein gamma isiIndicates the input of the ith joint, ζiThe corresponding transformation coefficient of the joint is determined by the corresponding motor, thetaiThe preliminary instruction coefficient generated for the upper layer network is finally completed by the cooperation of the outer deep reinforcement learning network and the inner CPG networkThe interaction and intelligent high-efficiency swimming control of the adult bionic robot fish in a complex environment.
In further detail, the interface of the inner layer CPG network includes a mechanism for decomposing, calibrating and transmitting the preliminary command, for example, decomposing "fast right rotation" into "right rotation", and transmitting "fast right rotation" into the inner layer CPG network.
In further detail, the inner CPG network comprises a movement model based on a central pattern generator and a specific joint movement instruction conversion transmission mechanism.
The second object of the present invention can be achieved by the following technical solutions: a bionic robot fish joint motion control device based on a deep reinforcement learning CPG network comprises:
a computer terminal;
a controller;
one or more processors;
a memory;
and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing a method of deep reinforcement learning CPG network based biomimetic robotic fish joint motion control as described above.
The third object of the present invention can be achieved by the following technical solutions: a storage medium stores a computer program used in combination with a computer terminal and a display, and the computer program can be executed by a processor to implement the method for controlling the joint motion of the biomimetic robotic fish based on the deep reinforcement learning CPG network.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although a large number of terms are used here more, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.
Claims (10)
1. A bionic robot fish control method based on deep reinforcement learning is characterized by comprising the following steps:
s1: constructing an outer-layer bionic robot fish information network through deep learning, and giving a preliminary instruction through interaction with the environment;
s2: and constructing an inner CPG network aiming at the preliminary instruction, and giving a specific joint movement instruction by constructing a movement model based on a central pattern generator.
2. The bionic robotic fish control method based on deep reinforcement learning of claim 1, characterized in that: the outer-layer bionic robot fish information network comprises input information processed by adopting a cooperative conversion method, and the input information is used for generating a deep reinforcement learning network of a preliminary instruction and an inner-layer CPG network transmission interface.
3. The bionic robotic fish control method based on deep reinforcement learning of claim 1, characterized in that: the cooperative conversion method comprises the steps of correlating and labeling continuous 4-frame images acquired by an external sensor of the bionic robot fish with depth, distance and more than two kinds of data, and packaging the multi-element data into structured data which can be directly processed by a depth network and used as subsequent deep reinforcement learning network input.
4. The bionic robotic fish control method based on deep reinforcement learning of claim 1, characterized in that: the deep reinforcement learning network adopts a deep reinforcement Q learning network to construct a deep network, a good processing mechanism of the deep reinforcement Q learning network and good interaction capacity with an external environment are used for generating a preliminary instruction of the movement of the bionic robot fish, and the deep network generates the preliminary instruction and then inputs an inner CPG network interface.
5. The bionic robotic fish control method based on deep reinforcement learning of claim 1, characterized in that: the deep reinforcement learning network utilizes a DQN algorithm to construct a deep reinforcement learning framework, the multivariate data is input into the deep reinforcement learning framework, the deep reinforcement learning framework generates a corresponding Q value of the input multivariate data by setting a target reward value, and a Q value generation formula is shown as a formula (1):
Q*(s，a)＝∑pa(s，s′)(Ra(s，s′)+γmaxa′Q*(s′，a′)) (1)
wherein P isa(s, s ') represents the probability of transition from the current state s to the next state s ', and R (s, s ') represents the reward after the action performed in the current state, being a gamma decay coefficient, maxQ*(s ', a') represents the operation of selecting the current maximum Q value, generating an estimated value of Q through the deep network, and completing the parameter updating of the deep network through the difference between the Q value and the estimated value of Q, as shown in formula (2):
L(θ)＝E((R+γmaxa′(s′，a′，θ)-Q(s，a，θ))2) (2)
where L (θ) represents a loss function and E represents the desired operation.
6. The bionic robotic fish control method based on deep reinforcement learning of claim 1, characterized in that: the inner CPG network interface can convert the preliminary instruction and transmit the preliminary instruction into the inner CPG network to realize the specific joint movement of the bionic robot fish, and the generation formula of the specific joint movement model of the bionic robot fish is as shown in formula (3):
wherein
t is a direction control parameter, theta is a phase difference between neurons,
representing different phases, epsilon, of the ith neuroniω denotes the amplitude and frequency of the neuron, and Pu，PvIs a perturbation term, where Pv＝c2ui+1sinθ+c1ui+1cosθ，Pu＝c1ui-1cosθ-c2vi-1sinθ，c1，c2After the CPG model is constructed for the neuron coupling coefficient, the phase output is converted and then input to each joint of the bionic robot fish, as shown in formula (4):
Γi＝ζivi+Θi (4)
wherein riIndicates the input of the ith joint, ζiThe corresponding transformation coefficient of the joint is determined by the corresponding motor, thetaiAnd finally, finishing interaction and intelligent high-efficiency swimming control of the bionic robot fish in a complex environment through the cooperative cooperation of an outer deep reinforcement learning network and an inner CPG network for the initial instruction coefficient generated by the upper network.
7. The bionic robotic fish control method based on deep reinforcement learning of claim 1, characterized in that: the inner CPG network interface comprises a decomposition, calibration and transmission mechanism of a preliminary instruction.
8. The bionic robotic fish control method based on deep reinforcement learning of claim 1, characterized in that: the inner CPG network comprises a movement model based on a central pattern generator and a specific joint movement instruction conversion transmission mechanism.
9. The bionic robot fish control device based on deep reinforcement learning as described in any one of the above items 1 to 8, characterized in that: the method comprises the following steps:
a computer terminal;
a controller;
one or more processors;
a memory;
and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing a method for deep reinforcement learning CPG network based biomimetic robotic fish joint motion control as described above.
10. A storage medium as in any one of claims 1-8, wherein: the computer program is used in combination with a computer and a display, and can be executed by a processor to implement the method for controlling the joint motion of the biomimetic robotic fish based on the deep reinforcement learning CPG network.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210507310.7A CN115390442A (en) | 2021-01-26 | 2021-01-26 | Bionic robot fish control method and device for deep reinforcement learning and storage medium |
| CN202110110948.2A CN112904873B (en) | 2021-01-26 | 2021-01-26 | Bionic robot fish control method and device based on deep reinforcement learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110110948.2A CN112904873B (en) | 2021-01-26 | 2021-01-26 | Bionic robot fish control method and device based on deep reinforcement learning |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210507310.7A Division CN115390442A (en) | 2021-01-26 | 2021-01-26 | Bionic robot fish control method and device for deep reinforcement learning and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112904873A true CN112904873A (en) | 2021-06-04 |
| CN112904873B CN112904873B (en) | 2022-08-26 |
Family
ID=76118857
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210507310.7A Pending CN115390442A (en) | 2021-01-26 | 2021-01-26 | Bionic robot fish control method and device for deep reinforcement learning and storage medium |
| CN202110110948.2A Active CN112904873B (en) | 2021-01-26 | 2021-01-26 | Bionic robot fish control method and device based on deep reinforcement learning |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210507310.7A Pending CN115390442A (en) | 2021-01-26 | 2021-01-26 | Bionic robot fish control method and device for deep reinforcement learning and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN115390442A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114609925A (en) * | 2022-01-14 | 2022-06-10 | 中国科学院自动化研究所 | Training method of underwater exploration strategy model and underwater exploration method of bionic machine fish |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101916071A (en) * | 2010-08-04 | 2010-12-15 | 中国科学院自动化研究所 | CPG feedback control method of biomimetic robot fish movement |
| CN110286592A (en) * | 2019-06-28 | 2019-09-27 | 山东建筑大学 | A kind of multi-modal movement technique of machine fish based on BP neural network and system |
| CN110488611A (en) * | 2019-09-02 | 2019-11-22 | 山东建筑大学 | A kind of biomimetic robot fish movement control method, controller and bionic machine fish |
| CN110909859A (en) * | 2019-11-29 | 2020-03-24 | 中国科学院自动化研究所 | Bionic robot fish motion control method and system based on antagonistic structured control |
| CN110989399A (en) * | 2019-12-16 | 2020-04-10 | 山东建筑大学 | Robot fish bionic control method and system fusing Spiking neural network and CPG |
| CN111158385A (en) * | 2020-01-10 | 2020-05-15 | 南京工程学院 | Motion control method, device and equipment of bionic robot fish and readable storage medium |
| CN111176116A (en) * | 2020-01-02 | 2020-05-19 | 西安交通大学 | Closed-loop feedback control method for robot fish based on CPG model |
-
2021
- 2021-01-26 CN CN202210507310.7A patent/CN115390442A/en active Pending
- 2021-01-26 CN CN202110110948.2A patent/CN112904873B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101916071A (en) * | 2010-08-04 | 2010-12-15 | 中国科学院自动化研究所 | CPG feedback control method of biomimetic robot fish movement |
| CN110286592A (en) * | 2019-06-28 | 2019-09-27 | 山东建筑大学 | A kind of multi-modal movement technique of machine fish based on BP neural network and system |
| CN110488611A (en) * | 2019-09-02 | 2019-11-22 | 山东建筑大学 | A kind of biomimetic robot fish movement control method, controller and bionic machine fish |
| CN110909859A (en) * | 2019-11-29 | 2020-03-24 | 中国科学院自动化研究所 | Bionic robot fish motion control method and system based on antagonistic structured control |
| CN110989399A (en) * | 2019-12-16 | 2020-04-10 | 山东建筑大学 | Robot fish bionic control method and system fusing Spiking neural network and CPG |
| CN111176116A (en) * | 2020-01-02 | 2020-05-19 | 西安交通大学 | Closed-loop feedback control method for robot fish based on CPG model |
| CN111158385A (en) * | 2020-01-10 | 2020-05-15 | 南京工程学院 | Motion control method, device and equipment of bionic robot fish and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| CAFER BALA ETC: ""CPG-based autonomous swimming control for multi-tasks of a biomimetic robotic fish"", 《OCEAN ENGINEERING》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114609925A (en) * | 2022-01-14 | 2022-06-10 | 中国科学院自动化研究所 | Training method of underwater exploration strategy model and underwater exploration method of bionic machine fish |
| CN114609925B (en) * | 2022-01-14 | 2022-12-06 | 中国科学院自动化研究所 | Training method of underwater exploration strategy model and underwater exploration method of bionic machine fish |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112904873B (en) | 2022-08-26 |
| CN115390442A (en) | 2022-11-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220212342A1 (en) | Predictive robotic controller apparatus and methods | |
| Metta et al. | A developmental approach to visually-guided reaching in artificial systems | |
| Qiang et al. | Reinforcement learning model, algorithms and its application | |
| US20190184556A1 (en) | Apparatus and methods for online training of robots | |
| US20180260685A1 (en) | Hierarchical robotic controller apparatus and methods | |
| US10962976B1 (en) | Motion control method and system for biomimetic robotic fish based on adversarial structured control | |
| Liu et al. | A hybrid control architecture for autonomous robotic fish | |
| CN112904873B (en) | Bionic robot fish control method and device based on deep reinforcement learning | |
| Ghadirzadeh et al. | Bayesian meta-learning for few-shot policy adaptation across robotic platforms | |
| Lan et al. | Learning locomotion skills in evolvable robots | |
| Chao et al. | Learning robotic hand-eye coordination through a developmental constraint driven approach | |
| Kim et al. | Learning and generalization of dynamic movement primitives by hierarchical deep reinforcement learning from demonstration | |
| Hauser et al. | Leveraging morphological computation for controlling soft robots: Learning from nature to control soft robots | |
| Arie et al. | Creating novel goal-directed actions at criticality: A neuro-robotic experiment | |
| Mohan et al. | How past experience, imitation and practice can be combined to swiftly learn to use novel “tools”: Insights from skill learning experiments with baby humanoids | |
| Zhang et al. | Robot path planning method based on deep reinforcement learning | |
| CN105467841B (en) | A kind of class nerve control method of humanoid robot upper extremity exercise | |
| CN113967909A (en) | Mechanical arm intelligent control method based on direction reward | |
| JP2669626B2 (en) | Robot control system | |
| Crawford et al. | Learning controllers for complex behavioral systems | |
| Luo et al. | Diffusion-based learning theory for organizing visuo-motor coordination | |
| Qiu et al. | Reinforcement Learning of Serpentine Locomotion for a Snake Robot | |
| Chen et al. | A Cerebellum-Inspired Control Scheme for Kinematic Control of Redundant Manipulators | |
| JP3236361B2 (en) | Motion control device | |
| Yu et al. | CPG-Based Swimming Control |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |