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WO2023163685A1 - Mécanisme autonome de tension de chenille et procédé de fonctionnement pour véhicules terrestres sans pilote - Google Patents

Mécanisme autonome de tension de chenille et procédé de fonctionnement pour véhicules terrestres sans pilote Download PDF

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Publication number
WO2023163685A1
WO2023163685A1 PCT/TR2023/050172 TR2023050172W WO2023163685A1 WO 2023163685 A1 WO2023163685 A1 WO 2023163685A1 TR 2023050172 W TR2023050172 W TR 2023050172W WO 2023163685 A1 WO2023163685 A1 WO 2023163685A1
Authority
WO
WIPO (PCT)
Prior art keywords
vehicle
tensioning mechanism
track
track tensioning
artificial intelligence
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.)
Ceased
Application number
PCT/TR2023/050172
Other languages
English (en)
Inventor
Ahmet Semih PARLAK
Halil AKBULUT
Necati Mert CELEN
Muhittin SOLMAZ
Mehmet Onur OZCELİK
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Havelsan Hava Elektronik Sanayi ve Ticaret AS
Original Assignee
Havelsan Hava Elektronik Sanayi ve Ticaret AS
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Priority claimed from TR2022/002511 external-priority patent/TR2022002511A2/xx
Application filed by Havelsan Hava Elektronik Sanayi ve Ticaret AS filed Critical Havelsan Hava Elektronik Sanayi ve Ticaret AS
Publication of WO2023163685A1 publication Critical patent/WO2023163685A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D55/00—Endless track vehicles
    • B62D55/08—Endless track units; Parts thereof
    • B62D55/30—Track-tensioning means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0272—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means comprising means for registering the travel distance, e.g. revolutions of wheels

Definitions

  • the invention relates to an autonomous track tensioning mechanism that enables the unmanned ground vehicle to have optimum driving performance in different terrains (stony, sandy, muddy, grassy, wet ground, etc.) and environmental conditions in the missions performed by unmanned ground vehicles, and to an operation method for the autonomous track tensioning mechanism.
  • Track tensioning mechanism is a subsystem that frequently used in unmanned ground vehicles.
  • the tension of the rubber track of the vehicle is adjusted autonomously by using camera, sensors, artificial intelligence algorithms, machine learning-based classification algorithms and deep learning-based object detection algorithms according to the road conditions during its deployment to the mission area, thereby ensuring that the unmanned ground vehicle has optimum driving performance on different terrains and environmental conditions.
  • the track tensioning mechanism in tracked vehicles is installed in two different ways. These methods are hydraulic and mechanical track tensioning mechanisms. Hydraulic track tensioning mechanism is used in vehicles that require larger tension force (e.g., Heavy Tracked Vehicles, Main Battle Tanks, etc.). Mechanical track tensioning mechanisms are used in vehicles where the tension force is smaller (Small and Medium Class Unmanned Ground Vehicles, Explosive Ordinance Disposal Robots, etc.). Hydraulic and mechanical track tensioning mechanisms have variable strokes and for different terrain conditions, the tracks must have different tension forces. The track tension value changes the distribution of the force transmitted between the power pack and the track force of the vehicle and the transmitted force from the tracked vehicle to the ground.
  • the track tensioning operation can be performed by the operator of the vehicle before the operation, or it can be adjusted during the operation. In mechanical track tensioning mechanisms, in general, stroke can only be adjusted by the operator before the operation.
  • Hydraulic track tensioning mechanisms tend to have leakage problem due to working with high-pressure fluids contained in hydraulic pistons, thereby making the track tensioning mechanism susceptible to dust and dirt, accumulated dust and dirt directly or indirectly affect the mobility of the mechanism.
  • the mobility of the mechanism is directly or indirectly affected due to dust and dirt accumulated in the screw threads.
  • the hydraulic track tensioning mechanism is manually operated by the operator before the operation by means of hydraulic pumps in the vehicle and the track tensioning process is performed manually.
  • the track tensioning process in the hydraulic tensioning mechanism can also be performed during the operation.
  • the mechanical track tensioning mechanism can be manually adjusted by the operator only before the operation.
  • the track tensioning mechanism is an open-loop operation and its effect on vehicle maneuverability and driving ability is evaluated by user experience.
  • tensioning the track system at the wrong value will negatively affect vehicle performance.
  • the track tensioning process should be kept within a closed control loop in order not to cause errors in vehicle maneuverability and off-road performance.
  • machine learning algorithms that are not optimized for the purpose can be applied to any data but will not give reliable results. It may produce results such as failure to detect track wear and suspension failures, incorrect detection, and misgrading of track wear zones. Thus, machine learning algorithms must be customized. Similarly, the algorithm trained for track damage detection and fault detection will not perform detection of terrain type. So, the learning dataset should be trained with images of different terrains and the data should be filtered. So that it can perform more reliable manner under different outdoor conditions.
  • a system for monitoring track tensioning for a track assembly of a heavy duty vehicle may comprise a track tensioning assembly has a fluid-driven actuator.
  • the invention relates to the technical field of traffic equipment, in particular to a multi-functional smart railway platform consists a tracked powertrain system.
  • the environmental measurement system may comprise an inertial navigation system and camera.
  • the objective of this invention is to realize an autonomous track tensioning mechanism that enables the unmanned ground vehicle to have optimum driving performance in different terrains (stony, sandy, muddy, grassy, wet ground, etc.) and environmental conditions in the missions performed by unmanned ground vehicles as well as an autonomous operation method of track tensioning mechanism.
  • Another objective of the present invention is to realize an track tensioning mechanism as well as an autonomous operation method of track tensioning mechanism enable the use of vehicle performance parameters in the planning of operations in different geographies and terrain types in subsequent operations by obtaining important information for the logistics and sustainment of an operation planned after the operation since vehicle performance (vehicle speed, motor speed, power consumption, etc.) on different terrains is recorded.
  • Figure 1 An exploded view of the autonomous track tensioning mechanism subject to the invention on the unmanned ground vehicle.
  • Figure 2 A perspective view of the autonomous track tensioning mechanism subject to the invention on the unmanned ground vehicle.
  • Figure 3 A top view of the unmanned ground vehicle when the autonomous track tensioning mechanism subject to the invention is on the unmanned ground vehicle.
  • Figure 4 B-B cross-sectional view of the unmanned ground vehicle when the autonomous track tensioning mechanism subject to the invention is on the unmanned ground vehicle.
  • the autonomous track tensioning mechanism developed for unmanned ground vehicles subject to the invention comprises the following parts:
  • a camera (2) which is located in front of the vehicle body (1) and performs the function of acquiring an image of the ground
  • a linear actuator (4) which is located inside the track tensioning mechanism (3) of the vehicle and used for tensioning the track of the unmanned ground vehicle,
  • An inertial navigation system (5) which is located on the vehicle body (1) and measures the linear (x, y, z) acceleration and angular (roll, pitch, yaw) acceleration data of the vehicle in three directions by means of sensors located thereon,
  • An unmanned ground vehicle motor (6) which is located inside the vehicle body (1) and provides instant power transmission of the vehicle,
  • An motor driver (7) which is located inside the vehicle body (1) and performs the task of feeding the motors (6) of the vehicle with the required force values in consideration of the commands given by the remote-control unit,
  • An encoder (8) which is located on the motor (6) and measures the instantaneous track speed of the vehicle
  • An artificial intelligence computer (9) which is located inside the vehicle body (1), processes sensor data from the inertial navigation system (5), the unmanned ground vehicle motor (6) and the encoder (8) and applies the required tension force to the track of the unmanned ground vehicle by means of a linear actuator (4).
  • the autonomous track tensioning mechanism subject to the invention is integrated into the unmanned ground vehicle.
  • the camera (2) delivers the image of the terrain on which the unmanned ground vehicle is located the artificial intelligence computer (9) of the vehicle, and also sensor data from the inertial navigation system (5), the motor driver (7), the unmanned ground vehicle motor (6) and the encoder (8) are delivered to the artificial intelligence computer (9).
  • the artificial intelligence computer (9) By means of a deep learning-based object detection algorithm, machine learning algorithm, artificial intelligence algorithm and sensor fusion technique in the artificial intelligence computer (9), the ground on which the unmanned ground vehicle is located is identified and the terrain type-tension force table prepared in advance is checked.
  • the required tension force is calculated by the artificial intelligence computer (9).
  • the determined force is delivered to the linear actuators (4) by the artificial intelligence computer (9).
  • the sensor data from the motor (6), the encoder (8), the inertial navigation system (5) and the motor driver (7) of the vehicle are also delivered to the artificial intelligence computer (9) together with the vehicle orientation and motor current data.
  • the artificial intelligence computer (9) adjusts the tension force by means of the linear actuator (4) if necessary corrections are needed in the tension force.
  • the method of operation of the autonomous track tensioning mechanism subject to the invention comprises the following steps:
  • an artificial intelligence computer which includes machine learning, deep learning and artificial intelligence methods, - Detecting the terrain type information on which the unmanned ground vehicle is located by a deep learning-based object detection algorithm by using a convolutional neural network,
  • the dynamics and kinematics of the unmanned ground vehicle are evaluated by means of the sensors located on the vehicle, the environment in which it is located is detected, a meaningful conclusion is drawn by fusing all the inferred data, and by this conclusion, a solution to the performance problem in different terrains is provided.
  • the terrain type detection can be successfully determined by training a deep learning-based object detection algorithm by using the camera (2) images collected from different terrains in order to perform the terrain type information detection process.
  • These processes are, respectively, collecting images from the vehicle camera (2), marking the related terrain from the collected images, preparing the convolutional neural network for object detection after the marking stage, and finally training the algorithm.
  • the satellite positioning system module located in the inertial navigation system (5) can measure the position of the vehicle on the earth and the linear acceleration of the UGV can be measured with the accelerometer. By integrating the linear accelerations, the linear speed can be confirmed, and by integrating this linear speed, the position of the vehicle can be confirmed.
  • the angular speed of the UGV can be detected with the gyroscope located in the inertial navigation system (5).
  • the angular speed of the vehicle and the orientation information of the vehicle obtained by means of the satellite positioning system module and magnetometer can be monitored instantaneously.
  • the instantaneous vehicle position and orientation obtained from the sensors on the vehicle are compared with the path given by the remote-control and the amount of deviation of the UGV from the path is controlled.
  • the encoder (8) connected to the shaft of the vehicle's electric motor (6) measures the speed of the traction gear that drives the track.
  • the spur gear and the track move but the UGV cannot move on the ground and the track turns idle. Since the position information of the UGV is obtained from the sensors on the vehicle, it can be checked whether there is any slippage when this information is compared with the information of the road the vehicle should have proceeded, which is calculated with the motor (6) revolution information obtained from the encoder (8). Similarly, by comparing the orientation information received from the vehicle with the orientation information of the vehicle calculated from the spur gear revolution information from the encoder (8), it is possible to obtain information about whether there is slippage on the right or left track.
  • the required tension force is determined by comparing the terrain type information generated during the operation with the terrain information data in the table by means of a machine learning algorithm.
  • a machine learning algorithm In order to use machine learning algorithms more efficiently, the following processes are first performed on the collected data: filtering missing or empty data, calculating the direct proportions and inverse proportions of the data to each other by performing exploratory data analysis, updating the information related to this calculation, scaling the data, filtering outlier data and normalizing the data. After these processes are applied, the machine learning algorithm will give better results.
  • the power consumed by the UGV on different terrain types and conditions (slope, surface profile, etc.) and the currents consumed by the electric motors are determined by performed tests. These data are recorded under the terrain type label.
  • the UGV detects the terrain on which it is traveling, while the slope information of the terrain is obtained from the inertial navigation system (5) as well as the current and power consumed from the motor drivers (7). The obtained information is compared with previously performed tests. If the UGV consumes a higher current than usual for similar terrain type and structure, there is a possibility that the amount of track tensioning is excessive. For this reason, the motors (6) are overloaded in terms of torque while the vehicle is traveling.
  • the determined amount of track tensioning is realized by the linear electric actuator on the UGV. If there is no deviation from the path after the track tension correction has been made, the track tension amount realized for the instantaneous terrain type and structure is accepted as the correct parameter.
  • the algorithm and data that determine the amount of track tensioning according to the terrain type and structure are not static and constant; they dynamically adjust the amount of track tensioning according to changing conditions.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Combustion & Propulsion (AREA)
  • Chemical & Material Sciences (AREA)
  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Business, Economics & Management (AREA)
  • Medical Informatics (AREA)
  • Game Theory and Decision Science (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Electromagnetism (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

L'invention concerne un mécanisme autonome de tension de chenille qui permet à un véhicule terrestre sans pilote (UGV) d'avoir une performance de conduite optimale sur différents terrains (rocailleux, sablonneux, boueux, herbeux, mouillé, etc.) et dans différentes conditions environnementales lors des missions réalisées par les UGV, et un procédé de fonctionnement pour le mécanisme autonome de tension de chenille. Le mécanisme de tension de chenille est un sous-système fréquemment utilisé dans les UGV. Afin de réaliser avec succès différentes missions telles que des déploiements logistiques, une recherche et un sauvetage, une patrouille autonome, une reconnaissance et une surveillance ; la tension de la chenille en caoutchouc du véhicule est ajustée de manière autonome à l'aide d'une caméra (2), de capteurs, d'algorithmes d'intelligence artificielle, d'algorithmes de classification basés sur de l'apprentissage automatique et d'algorithmes de détection d'objet basés sur un apprentissage profond en fonction des conditions de terrain pendant son déploiement vers la zone de mission, garantissant ainsi que l'UGV a des performances de conduite optimales sur différents terrains et dans différentes conditions environnementales.
PCT/TR2023/050172 2022-02-23 2023-02-22 Mécanisme autonome de tension de chenille et procédé de fonctionnement pour véhicules terrestres sans pilote Ceased WO2023163685A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
TR2022002511 2022-02-23
TR2022/002511 TR2022002511A2 (tr) 2022-02-23 İnsansiz kara araçlari i̇çi̇n otonom palet gergi̇ mekani̇zmasi ve çalişma yöntemi̇

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WO2023163685A1 true WO2023163685A1 (fr) 2023-08-31

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PCT/TR2023/050172 Ceased WO2023163685A1 (fr) 2022-02-23 2023-02-22 Mécanisme autonome de tension de chenille et procédé de fonctionnement pour véhicules terrestres sans pilote

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119117132A (zh) * 2024-11-13 2024-12-13 河北石安特智能科技有限公司 监测用全地形冲锋履带式机器人
CN120135312A (zh) * 2025-05-15 2025-06-13 福建华南重工机械制造有限公司 一种橡胶履带在室内工程车辆的控制方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6431008B1 (en) * 2000-10-31 2002-08-13 Caterpillar Inc. Method and apparatus for determining a slack-side tension of a track on an earthworking machine
US20090072617A1 (en) * 2007-09-14 2009-03-19 Arto Alfthan Automatic Track Tensioning System
US20210362791A1 (en) * 2020-05-22 2021-11-25 Deere & Company Track tension management system and method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6431008B1 (en) * 2000-10-31 2002-08-13 Caterpillar Inc. Method and apparatus for determining a slack-side tension of a track on an earthworking machine
US20090072617A1 (en) * 2007-09-14 2009-03-19 Arto Alfthan Automatic Track Tensioning System
US20210362791A1 (en) * 2020-05-22 2021-11-25 Deere & Company Track tension management system and method

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119117132A (zh) * 2024-11-13 2024-12-13 河北石安特智能科技有限公司 监测用全地形冲锋履带式机器人
CN119117132B (zh) * 2024-11-13 2025-03-11 河北石安特智能科技有限公司 监测用全地形冲锋履带式机器人
CN120135312A (zh) * 2025-05-15 2025-06-13 福建华南重工机械制造有限公司 一种橡胶履带在室内工程车辆的控制方法
CN120135312B (zh) * 2025-05-15 2025-08-08 福建华南重工机械制造有限公司 一种橡胶履带在室内工程车辆的控制方法

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