Advanced Materials Research Vol. 905 (2014) pp 443-447 © (2014) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMR.905.443
Unmanned Ground Vehicle SysML Navigation Model Conducted by Energy Efficiency Eero Väljaots1, a, Raivo Sell1, b 1
Tallinn University of Technology, Ehitajate tee 5, Tallinn, Estonia a
b
[email protected],
[email protected]
Keywords: unmanned ground vehicle, navigation algorithms, energy efficiency, SysML
Abstract. In this paper a SysML navigation models and early design methodology is briefly introduced. The methodology is offering tool and pre-defined models for mobile robot design in early design stage. The main target is reaching the optimal and efficient conceptual solution for detail design stage by using the pre-defined and validated SysML models according to the robot purpose and missions. As an example a snow plowing mission is demonstrated. Real mobile robot platform called “UKU” is developed and used for model validation purpose. Introduction Development of consumer robotic products is rapidly increased in recent years. Robotic systems like mobile robots or Unmanned Ground Vehicles (UGV) are getting available for wider users groups as robot base components like microcontrollers and sensors have increased their computing power while becoming cheaper on the same time and modern actuators and electrical brushless motors are more efficient. This fact has lead many new robotic system developments targeted to consumer market instead of purely military use only. Nevertheless, designing the mobile robot system is not a trivial task and the designer must be aware of many design limitation, constraints, user requirements etc. To manage all these aspects efficiently, reach the optimum solution in terms of initial requirements and achieve energy efficient product a methodology is essential to apply in early design stage. The described solution in this paper is a part of Mobile Robot Toolkit explained in the thesis and journal paper [1, 2]. Unmanned ground vehicle can be constructed in different moving abilities, which depend on tasks needed to perform. Mobility needs limited independent power sources, therefore energy management is important to get required tasks completed efficiently. Sufficient motion dynamics with minimum energy consumption is one of the most important constraints in early design of UGV’s. Determining the optimal key parameters on very beginning of product design stage reduces significantly the product design cost and helps to develop optimal conceptual solution for mobile robot locomotion system. The result of this research is a validation part of general mobile robot development framework incorporating methodologies, tools and experimental data focusing on the early stage product design support. SysML technical language [3] based on UML concept is used in the proposed methodology. The SysML is a graphical modelling language for specifying, analysing, designing and verifying complex socio-technical systems. The systems can be in all-scale, including hardware, software, information, people, processes, and facilities. The language provides graphical representations with a semantic foundation for modelling system requirements, behaviour, structure, and parametrics [4]. For the current research, SysML therefore provides useful tools for planning the test layout and modelling key-parameter relations and dependencies. The research is targeted to the available mid-size class UGV’s and in particularly to the solutions developed in Tallinn University of Technology, Department of Mechatronics called “UKU” [5] (fig. 1), which is the platform for measuring and validating motion parameters of the current research.
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Fig. 1. Unmanned Ground Vehicle “UKU”. Requirement modelling Almost every design process starts with some need specified by requirements. It is essential in complex systems that serious attention is guaranteed to the requirement engineering process. Task specific requirement model involves track and vehicle energy efficiency important parameters [6]. Processing time is dependent on power and navigation. Range is dependent on energy consumption and capacity. Also traction and wheel friction are very important factors in mobility performance tests [7]. The UGV is required to have enough capacity to complete the test during given time. One important factor in requirement engineering is to define measures, how every single requirement is validated. For more complex requirements is it advised in our methodology to assign a test procedure defined by activity or state machine diagram. In the following SysML diagram (Fig. 2) one mission specific requirement is shown with basic connections to other design components.
Fig. 2. Requirements diagram for navigation model.
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Energy efficiency parameters All commercial vehicles are tested for fuel efficiency. Traditional measure of fossil fuel efficiency in U.S. is miles per gallon (MPG), which is extensively used in comparison of different vehicles. To compare all vehicles, no matter their energy resource, U.S. Environmental Protection Agency (EPA) compares energy consumption of alternative fuel vehicles, plug-in electric vehicles and other advanced technology vehicles with the fuel economy of conventional internal combustion vehicles expressed as miles per US gallon. Miles per gallon gasoline equivalent (MPGe) is a measure of the average distance travelled per unit of energy consumed. As there is no standard for comparison and evaluation particularly UGV energy efficiency, it is reasonable to adapt the equivalent metrics – metres per Watt-hour. UGV converts battery energy to useful mechanical work like driving and task performing. Therefore its efficiency ratio is covered area or travelled distance and task accomplished related to consumed energy. Summary efficiency (Fig. 3) of travelled distance can be adjusted with traction efficiency ratio which is distance covered without slipping related to whole distance covered. Also it is adjusted with navigation efficiency ratio, which is ideal route related to covered route. Ideal route can be calculated on map using intelligent algorithms to find the optimal route. Energy consumption of an electric vehicle can be calculated from measures of battery voltage and consumed current in given time. The total current consumption can be divided into parts, which enables to analyse all different important UGV construction factors from efficiency point of view. Energy efficiency is dependent on many parameters of a vehicle, including its motor parameters, transmission friction, aerodynamic drag, weight, track gradient and rolling resistance [8]. Ideally, a vehicle traveling at a constant velocity on level ground in a vacuum with frictionless wheels could travel at any speed without consuming any energy beyond what is needed to get it up to speed. In real life, any vehicle must expend energy on overcoming road load forces, which consist of aerodynamic drag, tire rolling resistance, and inertial energy that is lost when the vehicle is decelerated by friction brakes. Most parameters in measurement system model are calculated during the navigation test, but other efficiency contributors are measured separately or calculated before test. The UGV is weighted before test. Rolling resistance is measured on separate indoor test, where UGV is decelerated on smooth concrete surface.
Fig. 3. Parametric diagram and constraints hierarchy on block definition diagram.
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Mission model for navigation algorithms testing Navigation test model purpose to arrange easily repeatable test layouts, which enables real-time measurements of key-parameters [9]. The key parameters, like tire grip versus rolling resistance, are used for design optimization and simulation model validation. The test platform UKU, shown in Fig. 1, is used for model validation and mission test drives. An example SysML navigation model for simple snow plowing test on closed area is shown in Fig. 4. The navigation algorithm being tested must solve several tasks: • calculate the optimal (shortest) course length to travel, • calculate the plowing track width based on given plow and snow parameters, • track its position, work completion and avoid obstacles, • prevent the UKU for being stuck with too big pile of snow and enable to solve these situations effectively, • reverse and recalculate track on traction loss.
Fig. 4. Snow plowing efficiency test layout on activity diagram. The performance of snow plowing is covered area related to consumed energy. The working area for navigation algorithms is predefined using GPS-coordinates. UKU uses several electronic and mechanical sensors to discover proximity with obstacles on the side of the testing area. In this task, due to ground specifics (usually snowy and icy), wheel slipping ratio is very important measure. UKU is equipped with gyroscope and acceleration sensors - Xsens motion tracker. The Xsens combined with GPS and MEMS inertial navigation system (INS) enables to record the distance covered without slipping. All covered distance is measured with wheel encoder. In snow plowing task, the UKU is not just traveling through the navigation courses trying to overcome usual movement resistive forces, but needs to deal with additional resistive forces. The greatest resistive force is added by snow being compressed by the plow. This force is linear dependent on plow width and non-linear dependent on snow thickness. Also it varies greatly with air temperature and snow aging.
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The drawbar pull is the amount of horizontal force available to a vehicle for accelerating or pulling a load. Drawbar pull is a function of velocity, and in general decreases as the speed of the vehicle increases. Drawbar pull resistive force is determined with dynamometer, which is attached into UKU plow placement joint and it measures force generated from snow stack resistance. This prevents the test from failing when UKU gets stuck with a great pile of snow and becomes unable to solve it. In this paper the detection of snow and obstacles is not covered, but there are several solutions like stereo camera vision [10], laser systems, etc. which can be applied in combination to effectively detect different types of obstacles, including the snow. Conclusions Proposed snow plowing test is easy and enough repeatable for testing UGV navigation models and construction energy efficiency key-parameters. Although energy consumption compared with useful work displays the overall vehicle efficiency, important contributors can further separated and measured. SysML provides useful tools for requirement, parametrics and structure planning of test layout and result processing. The acquired real-time measurement results according to modelled testing methodology enable to validate the simulation models and provide a useful tool for optimizing dynamics and efficiency on early design of different autonomous platforms. Acknowledgement It is a project supported by the Estonian Research Council grant ETF8652. References [1] R. Sell, A. Petritsenko: Early Design and Simulation Toolkit for Mobile Robot Platforms, International Journal of Product Development, 18(2), p. 168 – 192 (2013). [2] R. Sell: Model Based Mechatronic Systems Modeling Methodology in Conceptual Design Stage, Ph.D thesis, Tallinn University of Technology (2007). [3] SysML specification, http://sysml.org [4] L. Delligatti: SysML distilled, Addison-Wesley (2013). [5] R. Sell, P. Leomar: Universal Navigation Algorithm Planning Platform for Unmanned Systems, Solid State Phenomena, 164, p. 405-410 (2010). [6] E. Väljaots, R. Sell: Dynamic Motion Energy Efficiency Measurement of Ground Vehicles, 8th International DAAAM Baltic Conference "Industrial Engineering", Tallinn (2012). [7] T. Thueer, R. Siegwart: Mobility Evaluation of Wheeled All-terrain Robots, Robotics and Autonomous Systems, 58, p. 508-519 (2010). [8] E. Väljaots, R. Sell, M. Kaeeli: Motion and Energy Efficiency Parameters of Unmanned Ground Vehicle, 9th International Conference: Mechatronic Systems and Materials, Vilnius (2013). [9] J. van Diggelen, R. Looije, T. Mioch, M. A. Neerincx and N. J. J. M. Smets: Usage-Centered Evaluation Methodology for Unmanned Ground Vehicles, in 5th International Conference on Advances in Computer-Human Interactions, Valencia, Spain (2012). [10] F. Jesus, R. Ventura, Simultaneous localization and mapping for tracked wheel robots combining monocular and stereo vision, Journal of Automation, Mobile Robotics & Intelligent Systems, 6(1):21–27, (2013).
