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1

Improving navigation autonomy in mobile robots with Monte-Carlo particle filters

Algorri Maria-Elena

Zeszyty Naukowe Instytutu Pojazdów / Politechnika Warszawska

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2019

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z. 4/122

13--22

EN

Autonomous Navigation is possible when a vehicle combines internal information about its own state with external information about its environment. A vehicle receives the information as a permanent feed of measurements from its internal sensors (odometry) and from external sensors (ultrasound, cameras, IMUs, radar etc.). The information provided by the sensors is always uncertain and therefore a vehicle cannot estimate its own motion based only on internal sensing (odometry) and cannot plan its trajectory based only on external sensing. For autonomous navigation, all the sensors in a vehicle must be statistically characterized, their accuracy and error margin in a given dynamic range must be available to the vehicle. Using the statistics of all the sensors, a vehicle can take a combined decision that reduces the overall error when estimating its own position and planning a trajectory. Vehicles can use different algorithms to take a decision based on uncertain sensor measurements. One of the most popular algorithms is the Monte Carlo Particle Filter (MCPF). The MCPF uses Bayesian Statistics to reduce the overall uncertainty of a group of uncertain measurements, therefore improving the estimation of the vehicle internal and external states. In this article we give a practical explanation of the meaning of the MCPF. We also present the results of using the MCPF in autonomous navigation of mobile robots.

DE

Autonome Navigation ist möglich, wenn ein Fahrzeug interne Informationen über seinen eigenen Zustand mit externen Informationen über seine Umgebung kombiniert. Ein Fahrzeug erhält die Informationen als permanente Messdatenübertragung von seinen internen Sensoren (Odometrie) und von externen Sensoren (Ultraschall, Kameras, IMUs, Radar usw.). Die von den Sensoren gelieferten Informationen sind immer unsicher und daher kann ein Fahrzeug seine eigene Bewegung nicht nur auf Basis der internen Sensoren (Odometrie) abschätzen und seine Flugbahn nicht nur auf Basis der externen Erfassung planen. Für eine autonome Navigation müssen alle Sensoren in einem Fahrzeug statistisch charakterisiert werden, und ihre Genauigkeit und Fehlertoleranz in einem bestimmten Dynamikbereich muss dem Fahrzeug zur Verfügung stehen. Unter Verwendung der Statistik aller Sensoren kann ein Fahrzeug eine kombinierte Entscheidung treffen, die den Gesamtfehler bei der Schätzung seiner eigenen Position und der Planung einer Trajektorie verringert. Fahrzeuge können unterschiedliche Algorithmen verwenden, um eine Entscheidung basierend auf unsicheren Sensormessungen zu treffen. Einer der beliebtesten Algorithmen ist der Monte-Carlo-Partikelfilter (MCPF). Die MCPF verwendet die Bayes'sche Statistik, um die Gesamtunsicherheit einer Gruppe unsicherer Messungen zu verringern und so die Schätzung der internen und externen Zustände des Fahrzeugs zu verbessern. In diesem Artikel geben wir eine praktische Erklärung der Bedeutung der MCPF. Wir präsentieren auch die Ergebnisse des Einsatzes der MCPF in der autonomen Navigation von mobilen Robotern.

2

Vehicle Navigation Systems Involving Inertial Sensors and Odometry Data from On-Board Diagnostics in Non-GPS Applications

Prusaczyk Piotr, Baranowski Leszek, Panasiuk Jarosław

Technical Sciences / University of Warmia and Mazury in Olsztyn

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2019

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nr 22(3)

263--270

EN

This paper explores the applicability of on-board diagnostics data for minimizing inertial navigation errors in vehicles. The results of driving tests were presented and discussed. Knowledge of a vehicle’s exact initial position and orientation was crucial in the navigation process. Orientation errors at the beginning of navigation contributed to positioning errors. GPS data were not processed by the algorithm during navigation.

3

Correction of navigational information supplied to biomimetic Autonomous Underwater Vehicle

Praczyk T.

Polish Maritime Research

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2018

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nr 1

13--23

EN

In order to autonomously transfer from one point of the environment to the other, Autonomous Underwater Vehicles (AUV) need a navigational system. While navigating underwater the vehicles usually use a dead reckoning method which calculates vehicle movement on the basis of the information about velocity (sometimes also acceleration) and course (heading) provided by on-board devicesl ike Doppler Velocity Logs and Fibre Optical Gyroscopes. Due to inaccuracies of the devices and the influence of environmental forces, the position generated by the dead reckoning navigational system (DRNS) is not free from errors, moreover the errors grow exponentially in time. The problem becomes even more serious when we deal with small AUVs which do not have any speedometer on board and whose course measurement device is inaccurate. To improve indications of the DRNS the vehicle can emerge onto the surface from time to time, record its GPS position, and measure position error which can be further used to estimate environmental influence and inaccuracies caused by mechanisms of the vehicle. This paper reports simulation tests which were performed to determine the most effective method for correction of DRNS designed for a real Biomimetic AUV.

4

Application Of Kalman Filter In Navigation Process Of Automated Guided Vehicles

Śmieszek M., Dobrzańska M.

Metrology and Measurement Systems

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2015

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Vol. 22, nr 3

443--454

EN

In the paper an example of application of the Kalman filtering in the navigation process of automatically guided vehicles was presented. The basis for determining the position of automatically guided vehicles is odometry – the navigation calculation. This method of determining the position of a vehicle is affected by many errors. In order to eliminate these errors, in modern vehicles additional systems to increase accuracy in determining the position of a vehicle are used. In the latest navigation systems during route and position adjustments the probabilistic methods are used. The most frequently applied are Kalman filters.

5

Localization of the Wheeled Mobile Robot Based on Multi-Sensor Data Fusion

Jaroszek P., Trojnacki M.

Journal of Automation Mobile Robotics and Intelligent Systems

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2015

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Vol. 9, No. 3

73--84

EN

The paper presents a method of localization of a mobile robot which relies on aggregation of data from several sensors. A review of the state of the art regarding methods of localization of ground mobile robots is presented. An overview of design of the four-wheeled mobile robot used for the research is given. The way of representation of robot environment in the form of maps is described. The localization algorithm which uses the Monte Carlo localization method is described. The simulation environment and results of simulation investigations are discussed. The measurement and control equipment of the robot is described and the obtained results of experimental investigations are presented. The obtained results of simulation and experimental investigations confirm the validity of the developed robot localization method. They are the foundation of further research, where additional sensors supporting the localization process could be used.

6

A modular mobile robot for multi-robot applications

Rostkowska M., Topolski M., Skrzypczyński P.

Pomiary Automatyka Robotyka

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2013

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R. 17, nr 2

288-293

EN

This paper presents the process of designing and constructing a desktop-size mobile robot aimed at multi-robot applications. We present the mechanical structure, the electronics and the software, focusing on the modularity concept, which determines the educational values of the entire design. The decision process underlying the design is presented in details: choosing motors, sensors and crucial electronics components, implementing communication buses, and construction of the additional modules. Possible multi-robot applications of this design are outlined at the end.

PL

Niniejsza praca przedstawia poszczególne etapy projektowania i konstrukcji miniaturowego, dydaktycznego robota mobilnego własnego pomysłu. Zawarto w niej opis każdego z etapów, ze szczególnym naciskiem na koncepcję modułowości, która stanowi o wartości dydaktycznej całej konstrukcji. Opisano m.in. proces projektowania mechaniki, doboru czujników i najważniejszych elementów, projektowania elektroniki, opis zastosowanych magistral komunikacyjnych oraz opis wytworzonych modułów i programów zawartych w mikrokontrolerach oraz na komputerze PC.

7

Wireless communication for control of manipulation systems

Belda K., Rychnovský V., Píša P.

Archives of Control Sciences

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2012

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Vol. 22, no. 1

29-41

EN

The paper deals with a novel application of the wireless data communication for a continual control of the distributed manipulation systems. The solution is intended for industrial robotic plants. A considered way of the wireless communication is based on ZigBee protocol. The protocol is tested for a real time bidirectional data communication within a manipulation system. The system consists of several moving manipulation units, several stationary auxiliary units and one control computer. The computer provides the cooperation of all units in the system in relation to the user requirements. The system is controlled by a simple feedback multi-level control realized in MATLAB - Simulink environment. The paper is focused on the realization of the boards of power electronics, transmitters, optical positional sensors, optical gates and their networking in accordance with ZigBee protocol definition. The behavior of the ZigBee is illustrated by several records from real experiments.