Navigation-based domesticated animal automatic driving system and method

Abstract

The invention discloses a navigation-based domesticated animal automatic driving system and method, and belongs to the technical field of intelligent animal control. The method comprises the following steps: acquiring an animal position in real time through a navigation positioning module; presetting a target path and an expected speed through the control processor; comparing the current position information of the animal with a preset position through an instruction generation module, and generating a motion adjustment instruction; the tactile stimulation execution module applies adjustable stimulation to a specific part of the body surface of the animal according to the instruction so as to drive the animal to move; meanwhile, animal response is monitored through the behavior recognition module, stimulation parameters are dynamically adjusted through the mode adjusting module, and closed-loop control of perception-decision-execution-feedback is formed. According to the invention, unmanned, automatic and accurate control of the movement direction and speed of domesticated animals such as horses, cattle and sheep is realized, and the system is suitable for the fields of livestock management, special operation, animal training and the like, and has the advantages of high control precision, small animal stress, strong adaptability and the like.

Description

Technical Field

[0001] This invention relates to the field of intelligent equipment and animal behavior control technology, and in particular to an automated motion control method and system for domesticated animals that integrates precise positioning, intelligent planning and bio-friendly stimulus feedback. Specifically, it relates to a navigation-based automated driving and riding system and method for domesticated animals. Background Technology

[0002] Animal movement control technology has significant application value in livestock management, scientific research, ecological monitoring, and special operations. For example, in animal husbandry, it is necessary to guide livestock to move along planned routes to achieve efficient grazing; in scientific research, it is necessary to accurately record and control the movement trajectories of experimental animals; and in ecological protection, it is also necessary to control animal movement to achieve population monitoring or endangered species protection. Currently, the movement control of domesticated animals mainly relies on manual driving, such as riders driving horses and herders driving cattle and sheep, which suffers from low efficiency, poor consistency, and high labor costs. Existing automated animal guidance methods are mostly based on sound, electric shock, or physical fences, which have low control precision, easily cause stress reactions in animals, and are difficult to achieve precise control of complex paths. With the development of precision animal husbandry and intelligent farming, there is an urgent need for an unmanned, low-stress, and high-precision animal movement control method.

[0003] Existing technologies mostly employ open-loop control, lacking real-time positioning and feedback adjustment mechanisms. For example, GPS-based animal tracking systems can only record location and cannot actively intervene in movement direction; manual herding or sound training is affected by environmental noise, animal condition, and other factors, making precise speed and path control difficult. Furthermore, traditional stimulation methods (such as single electrical stimulation or mechanical restraint) lack standardized control systems. On the one hand, fixed-parameter stimulation patterns cannot adapt to the differences in physiological responses among different animals; on the other hand, the lack of feedback mechanisms to adjust stimulation parameters based on the animal's real-time movement status leads to low control efficiency and may cause excessive stress to the animal. Existing technologies are typically designed for single-type animals (such as terrestrial mammals) and are difficult to apply simultaneously to animals in multiple scenarios, including land, water, and air. For example, underwater animal control equipment needs to balance waterproofing and signal transmission stability, while aerial animal control equipment has stringent requirements on device weight and energy consumption; existing solutions often cannot meet the needs of multiple environments. Traditional methods fail to form a systematic closed loop between positioning information, movement planning, and stimulation regulation. For example, the inability to automatically adjust stimulus parameters based on real-time positional deviations leads to the accumulation of deviations between the animal's movement trajectory and the preset target, making it difficult to achieve long-term stable control. Summary of the Invention

[0004] To address the problems of existing animal driving methods being reliant on manual labor, having rough control, and easily causing stress to animals, the purpose of this invention is to provide a navigation-based automatic driving and control system and method for domesticated animals.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a navigation-based automatic driving and taming system for domesticated animals, comprising: The information perception layer is used to acquire the animal's geographical location, heading, and speed information in real time, while also collecting the animal's movement posture and physiological response signals. The intelligent decision-making layer is used to preset the target path and desired speed, and compare the animal's current position and speed with the preset target path and desired speed, and calculate movement adjustment instructions through path tracking algorithm and speed planner; The friendly execution layer is used to apply physical stimulation to animals based on movement adjustment commands; An adaptive adjustment layer is used to continuously monitor the animal's actual response to stimuli and, based on the deviation between the actual response and the expected response, uses an adaptive control algorithm to adjust the output parameters of the stimulus online, forming a negative feedback closed loop that causes the animal's movement trajectory to converge to the target path.

[0006] Furthermore, the information perception layer includes a navigation and positioning module and a behavior recognition module. The navigation and positioning module is used to acquire the animal's geographical location, heading, and speed information in real time, and the behavior recognition module is used to collect the animal's movement posture and physiological response signals.

[0007] Furthermore, the navigation and positioning module employs one or more combinations of Global Navigation Satellite System, UWB ultra-wideband positioning, and inertial measurement unit.

[0008] Furthermore, the intelligent decision-making layer includes a control processor and an instruction generation module. The control processor is used to run control algorithms and coordinate the work of various modules, and to preset target paths and desired speeds. The instruction generation module is used to compare the animal's current position and speed with the preset target paths and desired speeds, and to calculate motion adjustment instructions, including direction adjustments and speed adjustments, through a built-in path tracking algorithm and speed planner.

[0009] Furthermore, the friendly execution layer includes a tactile stimulation execution module, which receives motion adjustment instructions and maps them into physical stimuli to specific parts of the animal's body surface. The stimulus is a non-noxious tactile signal, and its intensity, frequency, and timing can be encoded according to the motion adjustment instructions and the individual animal model.

[0010] Furthermore, the tactile stimulation execution module includes multiple independently controlled stimulation units arranged in an array. The stimulation units are micro-vibration motors, micro-airbags, or electromagnetic pulse generators, and are placed on the animal's neck, shoulders, back, flanks, and legs.

[0011] Furthermore, the adaptive adjustment layer includes a behavior recognition module and a pattern adjustment module. The behavior recognition module is used to continuously monitor the animal's actual response to stimuli, including whether it turns as expected and the rate of change of speed. The pattern adjustment module is used to adjust the output parameters of the stimulus online based on the deviation between the actual response and the expectation, using an adaptive control algorithm to form a negative feedback closed loop, so that the animal's motion tracking trajectory converges to the target path.

[0012] Furthermore, the behavior recognition module identifies the animal's turning, acceleration, deceleration, resistance, or stress state through inertial sensors, electromyography sensors, or visual sensors mounted on the animal's body.

[0013] Furthermore, the mode adjustment module uses fuzzy control or PID control algorithms to adjust the intensity, frequency, duration, and spatial excitation sequence of stimuli in real time according to the animal's movement deviation.

[0014] This invention also provides a navigation-based method for automatically driving and controlling domesticated animals, comprising the following steps: (1) The current geographical location, heading and speed information of the animal are obtained in real time through the information perception layer; (2) The target motion path and desired speed are set through the intelligent decision layer, and motion control commands containing motion direction and speed adjustment information are generated through path and speed planning algorithms based on the deviation between the current position and the target path. (3) The friendly execution layer receives the motion control command and applies non-harmful tactile signals with adjustable intensity, frequency or pattern to multiple parts of the animal's body surface to drive the animal to move according to the command; (4) Monitor the animal’s real-time movement and posture through the information perception layer and feed the recognition results back to the adaptive adjustment layer; (5) The adaptive adjustment layer dynamically adjusts the output parameters of the friendly execution layer according to the feedback information to form a closed-loop feedback control, so that the animal's movement trajectory converges to the target path.

[0015] Furthermore, the method also includes a training and calibration phase: in the initial phase, by applying a fixed pattern of stimulation and recording the animal's response, a "stimulus-response" baseline model is established for specific individuals of different species of animals to optimize subsequent control parameters.

[0016] The beneficial effects of this invention are as follows: (1) Fully automatic and high precision: It realizes unmanned operation from path planning to motion execution. Combined with high-precision navigation and closed-loop control, it can achieve path tracking accuracy from centimeters to meters.

[0017] (2) Animal-friendly and low-stress: The use of gentle tactile stimulation instead of traditional electric shocks or forced mechanical drives greatly reduces animal fear and stress response, which meets the requirements of animal welfare ethics.

[0018] (3) Adaptability and intelligence: The system can dynamically adjust its strategy based on the animal's real-time feedback, and has the ability to learn and adapt to individual differences, making the control more flexible and intelligent.

[0019] (4) Wide range of applications: It can be adapted to a variety of terrestrial domesticated animals (horses, cattle, sheep, dogs, etc.) and applied to various scenarios such as intelligent grazing, automated training, scientific research experiments, and scenic area guidance. It has high practical value and market potential. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the system of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of this application.

[0023] like Figure 1 As shown, this embodiment of the invention provides a navigation-based automatic driving and taming system for domesticated animals, comprising: The information perception layer is used to acquire the animal's geographical location, heading, and speed information in real time, while also collecting the animal's movement posture and physiological response signals.

[0024] The intelligent decision-making layer is used to preset the target path and desired speed, and compare the animal's current position and speed with the preset target path and desired speed. It then calculates movement adjustment instructions through path tracking algorithms and speed planners.

[0025] The friendly execution layer is used to apply physical stimulation to animals based on movement adjustment instructions.

[0026] An adaptive adjustment layer is used to continuously monitor the animal's actual response to stimuli and, based on the deviation between the actual response and the expected response, uses an adaptive control algorithm to adjust the output parameters of the stimulus online, forming a negative feedback closed loop that causes the animal's movement trajectory to converge to the target path.

[0027] In a preferred embodiment, the information perception layer includes a navigation and positioning module and a behavior recognition module. The navigation and positioning module is used to acquire the animal's geographical location, heading, and speed information in real time, and the behavior recognition module is used to collect the animal's movement posture and physiological response signals.

[0028] Preferably, the navigation and positioning module employs one or more combinations of Global Navigation Satellite System, UWB ultra-wideband positioning, and inertial measurement unit.

[0029] In a preferred embodiment, the intelligent decision-making layer includes a control processor and an instruction generation module. The control processor is used to run control algorithms and coordinate the work of various modules, and to preset target paths and desired speeds. The instruction generation module is used to compare the animal's current position and speed with the preset target paths and desired speeds, and to calculate motion adjustment instructions, including direction adjustments and speed adjustments, through a built-in path tracking algorithm and speed planner.

[0030] Preferably, the control processor integrates a wireless communication module.

[0031] In a preferred embodiment, the user-friendly execution layer includes a tactile stimulation execution module, which receives motion adjustment instructions and maps them into physical stimuli for specific parts of the animal's body surface. The stimulus is a non-noxious tactile signal, and its intensity, frequency, and timing can be encoded according to the motion adjustment instructions and the individual animal model.

[0032] Preferably, the tactile stimulation execution module includes multiple independently controlled stimulation units arranged in an array. The stimulation units are micro-vibration motors, micro-airbags, or electromagnetic pulse generators, and are placed on the animal's neck, shoulders, back, flanks, and legs.

[0033] In a preferred embodiment, the adaptive adjustment layer includes a behavior recognition module and a pattern adjustment module. The behavior recognition module is used to continuously monitor the animal's actual response to stimuli, including whether it turns as expected and the rate of change of speed. The pattern adjustment module is used to adjust the output parameters of the stimulus online based on the deviation between the actual response and the expectation, using an adaptive control algorithm to form a negative feedback closed loop, so that the animal's motion tracking trajectory converges to the target path.

[0034] Preferably, the behavior recognition module identifies the animal's turning, acceleration, deceleration, resistance, or stress state through inertial sensors, electromyography sensors, or visual sensors mounted on the animal's body. The pattern adjustment module adjusts the intensity, frequency, duration, and spatial excitation sequence of stimuli in real time based on the animal's movement deviations using fuzzy control or PID control algorithms.

[0035] This invention also provides a navigation-based method for automatically driving and controlling domesticated animals, comprising the following steps: S1. The information perception layer obtains the animal's current geographical location, heading, and speed information in real time.

[0036] S2. The target motion path and desired speed are set through the intelligent decision layer, and motion control commands containing motion direction and speed adjustment information are generated through path and speed planning algorithms based on the deviation between the current position and the target path.

[0037] S3. The friendly execution layer receives the motion control command and applies non-harmful tactile signals with adjustable intensity, frequency, or pattern to multiple corresponding parts of the animal's body surface to drive the animal to move according to the command.

[0038] S4. Monitor the animal's real-time movement and posture through the information perception layer, and feed the recognition results back to the adaptive adjustment layer.

[0039] S5. The adaptive adjustment layer dynamically adjusts the output parameters of the user-friendly execution layer based on the feedback information to form a closed-loop feedback control, so that the animal's movement trajectory converges to the target path.

[0040] Preferably, the method further includes a training and calibration phase: in the initial phase, by applying a fixed pattern of stimulation and recording the animal's response, a "stimulus-response" benchmark model is established for specific individuals of different species of animals to optimize subsequent control parameters.

[0041] As a preferred embodiment, this embodiment takes controlling a horse to walk along a predetermined grassland path as an example, but it should not be construed as a limitation of the present invention.

[0042] In terms of system hardware deployment, the horses wear specially designed saddle pads, which integrate: a positioning unit (high-precision GPS module and 9-axis IMU); a stimulation unit (16 miniature vibration motors embedded in the saddle pad in a 4x4 matrix, covering key response areas on the horse's back and sides); a recognition unit (surface electromyography (sEMG) sensors additionally installed in the saddle pad to monitor the activation status of major muscle groups); a control host (a ruggedized embedded computer (such as one based on ARM architecture) fixed to the saddle, integrating a wireless communication module (4G / 5G or LoRa) for remote monitoring and command issuance); and a power supply (using a high-energy-density lithium battery pack to power the entire system).

[0043] In terms of software and control algorithms, this includes: (1) Path planning: Pre-set a digital path (a series of latitude and longitude coordinates and suggested speed) on the control host or remote server.

[0044] (2) Tracking algorithm: The "pure tracking" algorithm is adopted. The controller calculates the lateral deviation and heading deviation of the horse's current position from the target path in real time, thereby obtaining the desired turning curvature.

[0045] (3) Stimulus mapping strategy: Establish a "control command-stimulus pattern" mapping library. For example: The "Slight Left Turn" command activates a vibration motor in the left front shoulder area, which vibrates at a frequency of 3Hz and a medium intensity for 2 seconds.

[0046] The "accelerate" command activates a series of motors along the spine, causing short, wave-like vibrations from back to front.

[0047] (4) Adaptive adjustment: Fuzzy control rules are adopted. For example, if the sEMG sensor detects that the animal does not respond to the stimulus (the muscle is not activated as expected), the fuzzy controller will adjust the "stimulus intensity" variable from "medium" to "high".

[0048] The specific workflow is as follows: After system startup, GPS / IMU begins to output fused precise positioning information; the control algorithm calculates the deviation between the current position and the target path, generating a "turn 15 degrees to the left" command; according to the mapping library, this command is converted into "activate left shoulder stimulation point A, mode M1"; stimulation point A operates according to mode M1. Simultaneously, the sEMG sensor monitors whether the left shoulder deltoid muscle contracts; if the expected muscle response is detected within a set time (e.g., 1.5 seconds), and IMU data confirms that the horse's head has begun to turn left, the command is considered valid, and the current parameters are maintained; if no response is detected, the fuzzy controller increases the stimulation intensity or switches to a backup stimulation point (e.g., the left side of the neck) until a valid turning action is produced; during the turning process, the stimulation is continuously fine-tuned based on position feedback until the heading deviation returns to zero.

[0049] Based on safety and ethical considerations, the present invention has made the following provisions: Intensity Limitation: The physical output of all stimulation units is rigorously calibrated to ensure it remains below a threshold that could cause pain or discomfort. The adjustable vibration intensity range ensures it is only at the tactile alert level.

[0050] Emergency Stop Mechanism: The system has a built-in "Animal Stress Monitoring" subroutine that analyzes IMU data (abnormally vigorous movement) and heart rate monitoring (if equipped) to determine if the animal is panicked. Once stress is detected, all stimulation is immediately stopped and an alarm is issued.

[0051] Individualized adaptation: Emphasizing the early calibration stage, avoiding "one-size-fits-all" stimulation parameters, and respecting individual differences among animals.

[0052] Human-machine collaboration: The system supports human intervention at any time, and remote monitors can override automatic commands and switch to manual guidance mode at any time.

[0053] This invention achieves safe, precise, and adaptive automated motion control of domesticated animals through the aforementioned closed-loop system of hardware and software collaboration, providing an innovative technical solution for related fields.

[0054] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only.

[0055] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A navigation-based domesticated animal automatic driving system, characterized by, Comprise: An information perception layer for real-time acquisition of the geographical position, heading and speed information of the animal, as well as collection of the motion posture and physiological response signals of the animal; An intelligent decision-making layer for presetting a target path and an expected speed, and comparing the current position and speed of the animal with the preset target path and expected speed, and calculating a motion adjustment instruction through a path tracking algorithm and a speed planner; A friendly execution layer for applying physical stimulation to the animal according to the motion adjustment instruction; An adaptive adjustment layer for continuously monitoring the actual response of the animal to the stimulation, and adjusting the output parameters of the stimulation online according to the deviation between the actual response and the expectation, forming a negative feedback loop, so that the motion trajectory of the animal converges to the target path.

2. The system of claim 1, wherein, The information perception layer comprises a navigation positioning module and a behavior recognition module, the navigation positioning module is used for real-time acquisition of the geographical position, heading and speed information of the animal, and the behavior recognition module is used for collection of the motion posture and physiological response signals of the animal.

3. The system of claim 2, wherein, The navigation positioning module adopts one or more combinations of global satellite navigation system, UWB ultra-wideband positioning and inertial measurement unit.

4. The system of claim 1, wherein, The intelligent decision-making layer comprises a control processor and an instruction generation module, the control processor is used for running a control algorithm and coordinating the work of each module, and presetting a target path and an expected speed; the instruction generation module is used for comparing the current position and speed of the animal with the preset target path and expected speed, and calculating a motion adjustment instruction through a built-in path tracking algorithm and a speed planner, including direction adjustment and speed adjustment.

5. The system of claim 1, wherein, The friendly execution layer comprises a tactile stimulation execution module, which receives the motion adjustment instruction and maps it to physical stimulation to specific parts of the body surface of the animal, wherein the stimulation form is a non-injurious tactile signal, and the intensity, frequency and timing can be encoded according to the motion adjustment instruction and the individual model of the animal.

6. The system of claim 5, wherein, The tactile stimulation execution module comprises a plurality of independently controlled stimulation units arranged in an array, the stimulation units are micro-vibration motors, micro airbags or electromagnetic pulse generators, arranged on the neck, shoulder, back, side abdomen and leg of the animal.

7. The system of claim 1, wherein, The adaptive adjustment layer comprises a behavior recognition module and a mode adjustment module, the behavior recognition module is used for continuously monitoring the actual response of the animal to the stimulation, including whether to turn as expected and the speed change rate; the mode adjustment module is used for adjusting the output parameters of the stimulation online according to the deviation between the actual response and the expectation through an adaptive control algorithm, forming a negative feedback loop, so that the motion tracking trajectory of the animal converges to the target path.

8. The system of claim 7, wherein, The behavior recognition module identifies the turning, acceleration, deceleration, resistance or stress state of the animal through the inertial sensor, electromyographic sensor or visual sensor mounted on the body of the animal; the mode adjustment module adjusts the intensity, frequency, duration and spatial excitation sequence of the stimulation in real time through a fuzzy control or PID control algorithm according to the motion deviation of the animal.

9. A navigation-based method for automatically driving and guiding a domesticated animal, based on the system of any one of claims 1-8, characterized in that, The method comprises the following steps: (1) Real-time acquisition of the current geographical position, heading and speed information of the animal through the information perception layer; (2) The intelligent decision layer sets the target motion path and expected speed, and generates motion control instructions containing motion direction and speed adjustment information through path and speed planning algorithm according to the deviation of current position and target path; (3) The friendly execution layer receives the motion control instructions and applies non-injurious tactile signals with adjustable intensity, frequency or mode on multiple parts of the animal's body surface to drive the animal to move according to the instructions; (4) The information perception layer monitors the real-time motion state and posture of the animal and feeds back the recognition results to the adaptive adjustment layer; (5) The adaptive adjustment layer dynamically adjusts the output parameters of the friendly execution layer according to the feedback information to form a closed-loop feedback control, so that the animal motion trajectory converges to the target path.

10. The method of claim 9, wherein, The method further comprises a training and calibration phase: in the initial stage, by applying a fixed mode of stimulation and recording the animal's response, a "stimulus-response" benchmark model specific to different kinds of animals and individuals is established for optimizing subsequent control parameters.

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