Ultra wide band (UWB) assisted load detection and positioning method

Abstract

The invention relates to a load detection and positioning method which enables the detection and precise positioning of a load with an ultra-wideband (UWB) module with a single antenna and an unknown location, within a certain area, with the help of an autonomous vehicle equipped with an ultra-wideband (UWB) module.

Description

[0001] DESCRIPTION

[0002] ULTRA WIDE BAND (UWB) ASSISTED LOAD DETECTION AND POSITIONING METHOD

[0003] Technical Field

[0004] The invention relates to a load detection and positioning method which enables the detection and precise positioning of a load with an ultra-wideband (UWB) module with a single antenna and an unknown location, within a certain area, with the help of an autonomous vehicle equipped with an ultra-wideband (UWB) module.

[0005] Prior Art

[0006] In the prior art, detection and positioning of a desired object with the help of electro- optical cameras can be done with image processing techniques. In a system without depth perception, the load can be tracked. With Bluetooth and WiFi modules, distance measurement is made possible with the help of RSSI value (received signal strength index) of radio waves.

[0007] Electro-optical cameras are electronic sensors that are connected to ambient light and collect ambient light. Due to their working principles, the amount of light in the environment where the load will be searched and the colour of the load create critical disadvantages for load detection. Since electro-optical cameras cannot measure depth in single use, they cannot perform accurate distance measurement during approach and locking. The use of dual cameras is a method that is not common and has a high computational load.

[0008] Although Bluetooth and WiFi modules enable distance measurement with the help of RSSI (received signal strength index) of radio waves, these measurements are not precise and vary according to the environment. For these reasons, even if these methods are used for load detection, precise positioning is not possible. Since these signals are easy to detect, they are not suitable for use in applications requiring confidentiality. CN113411744A dicloses a high-precision indoor positioning and monitoring method. In the document, a trilateration method is used for positioning with UWB. For the relevant method, the positioning of a moving UWB device is performed with fixed UWB devices whose position is known.

[0009] US2011025562A1 discloses the UWB / IMU connected location estimation system and method. In the document, it is mentioned that there are more than one UWB receiver in the object search section and more than one UWB transmitter on the object to be detected.

[0010] W02023034500A1 discloses, methods, apparatus and systems for data-based position determination. In the method, it is seen that positioning is provided by processing the data through the central positioning system. The positioning of a large number of different information sources with various machine learning based methods is emphasised.

[0011] When the existing studies in the prior art are examined, there is a need to develop a load detection and positioning method that enables the detection and precise positioning of a load with a single antenna ultra-wideband (UWB) module and an unknown location within a certain area with the help of an autonomous vehicle equipped with an ultra-wideband (UWB) module.

[0012] Objectives of the Invention

[0013] The object of the present invention is to develop a load detection and positioning method that enables the detection and precise positioning of a load with an ultra- wideband (UWB) module with a single antenna and an unknown location within a certain area with the help of an autonomous vehicle equipped with an ultra- wideband (UWB) module.

[0014] Another object of the present invention is to develop a load detection and positioning method that is less likely to be detected by the UWB signals used and is less affected by multipath channel effects and jamming effects. Another object of the present invention to provide a method for detecting and positioning a load, which enables distance measurement with high accuracy and precision.

[0015] Another object of the present invention is to develop a load detection and positioning method that enables autonomous vehicles to detect the loads to be transported in the terrain without an operator, and to position them precisely enough to lock / join these loads.

[0016] Detailed Desciption of the Invention

[0017] A load detection and positioning method which enables the detection and precise positioning of a load with an ultra-wideband (UWB) module with a single antenna and an unknown location, within a certain area, with the help of an autonomous vehicle equipped with an ultra-wideband (UWB) module, it comprises,

[0018] Sending the autonomous vehicle to the defined area,

[0019] - Determining the route of the autonomous vehicle within the defined area with the coverage path planning algorithm,

[0020] - Providing mutual communication and simultaneous distance measurement through ultra-wideband (UWB) modules on the autonomous vehicle and the load moving on the determined route,

[0021] - Determining the default position of the load by an algorithm based on the application of the Gaussian Mixture Model (GMM) method to the Extended Kalman Filter (EKF) of the autonomous vehicle, if the ultra-wideband (UWB) signal is captured,

[0022] Determining the load position by determining the movement of the autonomous vehicle that will optimally reduce the state covariance matrix traces created for the fixed load with a partially observable Markov decision process algorithm.

[0023] In the inventive method, an autonomous vehicle is used to detect and precisely position an unlocated load left in a large area. Both the load and the autonomous vehicle are equipped with single antenna ultra-wideband (UWB) radio transceiver units. When the load and the autonomous vehicle are within the coverage areas of the UWB units on the autonomous vehicle, two-way distance measurement and information exchange starts via UWB messaging. The target location of the packet and other necessary information are learnt through these messages.

[0024] From the moment the method starts to be applied, the autonomous vehicle navigates the route created within the relevant area by using search algorithms in order to capture the UWB signal (intersecting the load and coverage areas) within a large area. During the entire operation process, the autonomous device performs its global positioning with the help of GPS and other sensors. The created route is in the shape of Bustrofedon.

[0025] After the UWB unit on the load and the UWB unit on the autonomous vehicle measure the distance and exchange information with each other, the positioning phase starts for the detected load. The positioning phase starts with the autonomous vehicle assigning a default position to the load. This assignment is followed by controlled movements and repeated measurements. The position estimate is updated each time as a result of repeated measurements. After a variable number of measurements and updates according to the position of the load and the defined motion set of the autonomous vehicle, the positioning algorithm starts to estimate the load position consistently. The positioning algorithm runs on the companion computer on the autonomous vehicle. The companion computer controls the interfaces of the UWB unit, the driving controller and the inertial measurement unit to perform the defined motion and simultaneously collect motion and distance measurements. The collected information is converted into position estimation by means of sensor fusion algorithms / Kalman filter.

[0026] With the "Coverage Path Planning" algorithm mentioned above, after the area with predetermined limits is scanned and mutual communication with the UWB module on the load and simultaneous distance measurement is provided, the precise position estimation of the fixed load with unknown position within a certain area by the autonomous vehicle is obtained through an algorithm based on the application of the Gaussian Mixture Model (GMM) method to the Extended Kalman Filter (EKF). The details of the algorithm that provides precise position estimation are given below.

[0027] The Extended Kalman Filter (EKF) equations in the developed algorithm structure are given below. In this algorithm, it is assumed that the position ( position’ Yposition) of the device used in this algorithm is calculated by using other sensors such as Inertial Measurement Unit (IMU) together with GPS. In the EKF algorithm, unlike the Kalman Filter structure, the observation function specified by formula 4 is used. The observation function is designed to take the observations made with the measurements as corrections to the filter state vector (xk). The observation vector (H) used in the filter is constructed as specified by formula 5 to ensure the relationship between the received measurement and the state vector.

[0028] (Formula 1)

[0029] (xk: The value of the predicted position on the x — axis) (yk: The value of the predicted position on the y — axis)

[0030] Pk\k- 1 = FPk-i\k-iFT+ Qk (Formula 2)

[0031] (h (x)'. Observation / correction function fort he member whose location is estimated)

[0032] (xposition. Own x position that can be measured by device (with encoder and IMU)) (yposition '. Own y position that can be measured by device (with encoder and IMU)) xrelative yrelative

[0033] Hk\ h (xfc, fc- 1 ) ft ( / c| fc- 1 )00] (Formula 5)

[0034] / ? = (I2x2)°r< (standard deviation of measurement noise ~5cm) (Formula 9) (Formula 10) zk: Distance measurement)

[0035] (xfc|fc:Filter state vector according to distance measurements)

[0036] Pk\k= Pk\k-i + KkHPkik-1(Formula 11)

[0037] In order to realise the formulas given above, in other words, in order to generate the position of a stationary device whose position is unknown from distance measurements with the EKF algorithm, it is necessary to perform the position change of the autonomous vehicle that will perform the position estimation of the fixed load after the "Coverage Path Planning" stage. In order to increase the speed of the positioning process before the autonomous vehicle starts moving, eight different EKF filters are created based on the Gaussian Mixture Model (GMM) structure, which accepts the state vector consisting of position values at eight different starting points (up, down, right, left and diagonal points between these four points around the autonomous vehicle). The state vector (xk) of each filter generated for any steady-state load consists of eight equally spaced points on the unit circle centred on the location of the autonomous vehicle. For these eight filters, the positioning result for the fixed load is produced by evaluating the most appropriate one among the normal likelihood values generated using the distance measurement correction value and measurement covariance. In the EKF algorithm in the GMM structure, when the trace of the state covariance matrix of the selected filter ( trace is sufficiently low, the position value found is expected to be sufficiently accurate (less than 1 meter). For a location estimate with this accuracy, the POMDP (Partially observable Markov decision process) algorithm determines the action that will optimally reduce the state covariance matrix traces created by the autonomous vehicle for the fixed load, and the autonomous vehicle performs the search action according to this result.

[0038] In summary, after obtaining the distance measurement over the mutual UWB signal following the "Coverage Path Planning" algorithm, the autonomous vehicle initiates eight EKF filters at eight equally spaced points on the unit circle around itself based on the GMM for constant load. According to the POMDP decision process, which is used for the most optimised cumulative reduction of the uncertainty values (covariance matrix traces) of each estimate, the manoeuvre / position change, which may seem random to an outside observer, is performed. When the uncertainty value of the fixed load (covariance matrix traces) reaches the desired levels (less than 1 meter) thanks to the continuous distance measurement corrections taken from the beginning of the filter, the precise position estimation process of the fixed load is performed by the autonomous vehicle.

Claims

CLAIMS1. A load detection and positioning method which enables the detection and precise positioning of a load with an ultra-wideband (UWB) module with a single antenna and an unknown location, within a certain area, with the help of an autonomous vehicle equipped with an ultra-wideband (UWB) module, characterized by, it comprises,Sending the autonomous vehicle to the defined area,- Determining the route of the autonomous vehicle within the defined area with the coverage path planning algorithm,- Providing mutual communication and simultaneous distance measurement through ultra-wideband (UWB) modules on the autonomous vehicle and the load moving on the determined route,- Determining the default position of the load by an algorithm based on the application of the Gaussian Mixture Model (GMM) method to the Extended Kalman Filter (EKF) of the autonomous vehicle, if the ultra-wideband (UWB) signal is captured,Determining the load position by determining the movement of the autonomous vehicle that will optimally reduce the state covariance matrix traces created for the fixed load with a partially observable Markov decision process algorithm.

2. A load detection and positioning method according to claim 1 , characterized by eight separate EKF filters are constructed based on a Gaussian Mixture Model (GMM) structure, which initializes a state vector consisting of position values at eight different starting points.

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