Soft Real-Time Systems for Low-Cost Unmanned Ground Vehicle

Ziębiński, Adam; Cupek, Rafał; Drewniak, Marek; Wolny, Bartlomiej

doi:10.1007/978-3-030-28374-2_17

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Additionally, information about dangers has to be propagate and therefore vehicles must communicate with the environment and each other [25,26]. To ensure the most recent data, the system should be a real-time system in order to perform actions and respond to events within predictable and specific time constraints [27]. In positioning systems, defined time constraints allow to determine the possible displacement of the object from the position in which the sensors' data were obtained to the place where the information about the position is available.…”

Section: Introductionmentioning

confidence: 99%

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

Paszek,

Grzechca

2023

Sensors

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Automation of transportation will play a crucial role in the future when people driving vehicles will be replaced by autonomous systems. Currently, the positioning systems are not used alone but are combined in order to create cooperative positioning systems. The ultra-wideband (UWB) system is an excellent alternative to the global positioning system (GPS) in a limited area but has some drawbacks. Despite many advantages of various object positioning systems, none is free from the problem of object displacement during measurement (data acquisition), which affects positioning accuracy. In addition, temporarily missing data from the absolute positioning system can lead to dangerous situations. Moreover, data pre-processing is unavoidable and takes some time, affecting additionally the object’s displacement in relation to its previous position and its starting point of the new positioning process. So, the prediction of the position of an object is necessary to minimize the time when the position is unknown or out of date, especially when the object is moving at high speed and the position update rate is low. This article proposes using the long short-term memory (LSTM) artificial neural network to predict objects’ positions based on historical data from the UWB system and inertial navigation. The proposed solution creates a reliable positioning system that predicts 10 positions of low and high-speed moving objects with an error below 10 cm. Position prediction allows detection of possible collisions—the intersection of the trajectories of moving objects.

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Section: Introductionmentioning

confidence: 99%

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

Paszek,

Grzechca

2023

Sensors

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“…The UWB technology enriches a range of network types that can be used in the IoT for smart city applications [ 12 ]. The UWB system, apart from the option of data transmission, allows for determining the position with high accuracy, which together with the growing interest in autonomous vehicles will be important in the smart cities or smart industries [ 13 , 14 , 15 ]. Positioning systems such as Lidar, radar, ultrasound, interaction sensors, or cameras provide information about the vehicle’s surroundings [ 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Design of the UWB Positioning System Simulator for LOS/NLOS Environments

Paszek

Grzechca

Becker

2021

Sensors

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UWB is a rapidly developing technology characterised by high positioning accuracy, additional data transferability, and communication security. Low costs and energy demand makes it a system that meets the requirements of smart cities (e.g., smart mobility). The analysis of the positioning accuracy of moving objects requires a ground truth. For the UWB system, it should have an accuracy of the order of millimetres. The generated data can be used to minimize the cost and time needed to perform field tests. However, there is no UWB simulators which can consider the variable characteristics of operation along with distance to reflect the operation of real systems. This article presents a 2D UWB simulator for outdoor open-air areas with obstacles and a method of analysing data from the real UWB system under line-of-sight (LOS) and non-line-of-sight conditions. Data are recorded at predefined outdoor reference distances, and by fitting normal distributions to this data and modelling the impact of position changes the real UWB system can be simulated and it makes it possible to create virtual measurements for other locations. Furthermore, the presented method of describing the path using time-dependent equations and obstacles using a set of inequalities allows for reconstructing the real test scenario with moving tags with high accuracy.

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