Dynamic investigation on a new robotized vehicle for urban freight transport

Simulation Modelling Practice and Theory 96 (2019) 101938

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Dynamic investigation on a new robotized vehicle for urban freight transport

T

Paolo Silvestri , Matteo Zoppi, Rezia Molfino ⁎

Department of Mechanical Engineering, Energetics, Management and Transportation (DIME) Polytechnic School - University of Genoa, Via Opera Pia, 15A, Genoa 16145, Italy

ARTICLE INFO

ABSTRACT

Keywords: Freight transport Urban areas Electric vehicle Robotics Vehicle stability simulation

The paper presents and discusses modelling and simulation of a robotic vehicle called FURBOT, designed for last mile delivery of freights. The focus is on the design process through simulation to meet requirements typical of a robot as well as automotive performance as for a commercial van. It is introduced the original concept of vehicle self-adapting its configuration and speed to ensure stability and integrity of the load and to protect the chassis itself from overload and mechanical fatigue, making possible a leaner sizing of the chassis structures and so higher payload to overall mass. FURBOT is small, dexterous, thought to work in fleet, suitable for ecommerce market and endowed with a robotized system for loading/unloading the freight. A virtual prototype of the vehicle is implemented to predict with sufficient accuracy, yet from the first steps of the design process, the reaction and performance against typical driving conditions typical of vehicle development; control logics are consequently derived.

1. Introduction 1.1. Background Worldwide, population and trading on place are highly and increasingly concentrated in urban areas. Together with changes in consumer and producer behaviors and new developments in logistics, this has led to a change in the requirements and priorities of freights delivery. On the one hand, with many consumer goods produced in small batches and an increasing share of purchases home-delivered, the need and number of shipments is growing. On the other hand, ecological and quality-of-life concerns impose severe limitations against traditional deliveries with large or fuel-driven delivery trucks and mostly manual loading operations. Thus, a key modern focus in the distribution of goods is on highly-automated and speedy shipments of relatively low individual volumes within an urban environment with limits on vehicle access and strict constraints on noise and chemical pollution. FURBOT is a European project conceived to face more comprehensively the challenge of the management and execution of urban freight delivery as stated above. It introduces a new kind of freight vehicle, Fig. 1, and an infrastructure to organize the delivery network. It proposes a new system for urban freight transport, using a fleet of these small fully electric vans able to perform both transport and handling; the network is managed by a centralized real time system able to optimize the planning of the delivery based on user requirements and freight availability at deconsolidation and consolidation facilities [1,2].

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Corresponding author. E-mail address: [email protected] (P. Silvestri).

https://doi.org/10.1016/j.simpat.2019.101938 Received 19 February 2019; Received in revised form 17 May 2019; Accepted 1 June 2019 Available online 03 June 2019 1569-190X/ © 2019 Published by Elsevier B.V.

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Fig. 1. FURBOT prototype and multibody model.

1.2. Problem of interest addressed in the paper The FURBOT vehicle has been fully developed, designed and constructed within the FURBOT project. It is a robot and at the same time must be an efficient full electric vehicle so the design has been addressed to a mixed set of specifications. As a mobile robot, it meets requirements on sensorization and actuation for all the operational functions that are related to navigation and the management of the payload transported; as electric vehicle, it has to be light with respect to the payload capacity, compact, and must have a place for an operator meeting requirements on ergonomics and safety. Its category is heavy quadricyle, L7e, in the European vehicle classification: this places limits on mass and speed in change of lower requirements on performance and homologation tests for the prototype realized. The compromise between size wrt payload size, overall mass wrt payload mass, and operator place requirements, geometry of the loading deck is found applying a dedicated design method derived from automotive and adapted: this is addressed in the paper. A summary of the characteristics of FURBOT is provided in Table 1. 1.3. Relevant state of the art The distribution of freights in urban areas has been addressed in several projects reported in the literature; this is relevant to explain the position of FURBOT. FIDEUS [3,4], CityLog [5], and CITY MOVE [6] are projects funded by the European Commission proposing vehicles and systems for freight distribution, each proposing creative solutions and valuable ideas; on one hand the solutions studied are impossible to integrate in an existing urban environment as is without major modifications, what is not possible in most old European cities and especially in their city centers; on the other hand, the vehicles for delivery proposed do not include any automated loading/unloading system and this central function is left to traditional workforce. The design of the vehicles is not an issue addressed and it is supposed to undergo on the approach of non-optimal, non-method guided modification of existing commercial chassis or graphical concepts of dedicated platforms. A DHL trend-report on logistics [7] published in 2016 structures the matter of short future logistics of freights using systems involving robotics; the study covers all steps of the handling and distribution and one is transportation seen as carried on using a combination of modified commercial platforms (trucks and vans for the grouped carry) while the so called last mile is left to home delivery robots, mobile robots carrying in the city multiport and multibox containers for customer close-home picking off working hours, and micro delivery of few units addressed to customers registered to a priority supply service. Conclusions after this report are that priority customer service of a limited volume of goods per day to business customers is an emerging business for companies in logistics and it may parallel or replace major weekly deliveries with traditional small trucks and vans; the Amazon approach with Table 1 FURBOT summary of design and performance features. Unladen mass excluded batteries (vehicle intended to carry goods)

600 kg

Body

Stainless steel square hollow tubes

Maximum design payload

1000 kg

Powertrain

Overall length

3890 mm

Suspension, steering, and brakes

Wheelbase Overall width

2690 mm 1510 mm

Charging Instrumentation

Track (front and rear)

1466 mm

Nominal power electric motor

Lithium-ion battery Three phase, four pole AC induction motor Drive inverter with variable frequency drive and regenerative braking system Height adjustable suspensions with McPherson strut and telescopic damper integrated, rack-pinion steering box on front wheels, hydraulic brakes on automotive standard Direct with BMS Touchscreen with media, communication, cabin, and vehicle controls. Two Lidars, cameras on all sides and on forks 14 kW

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small ground or flying robots is relevant to private delivery (at home of people) and focuses on this, while cannot apply to the supplying of a shop or restaurant in a city center. Now, looking at the hardware available, left aside any modified commercial chassis, what is in the literature and proposed are modified ground robots; the focus of researchers is, rightly, on the missing performance in robust navigation and decision making, while the vehicle designs still belong all to the category of ‘cart with some wheels attached’. There is hence room for better (design of) vehicles in general and larger vehicles for B2B thought for being efficient automated delivery systems. The difference between a robot delivery van-size platform (800–1000 kg payload) and a commercial van are in speed (max 50 km/h), run on small streets in presence of pedestrians (overall shape and size ratios), embedding of sensing, design around the critical phase of automated transferring of the freights (loading-unloading): the geometry of the chassis to meet these requirements and not priority to driving performance at high speed on large roads. Staying now on the topic of the design method, concurrent engineering applies with specifications for vehicle design. Software tools are largely available, stable and merging kinematics, dynamics and structural simulation, some specific for vehicle design; among this, one used in automotive is Siemens LMS Virtual.Lab, a multibody solver built on the CATIA v5 architecture [8]. In the recent literature this approach applied to vehicle design is reported in particular in [9] combined to a method of selection of the geometry of the suspension close to the one used for FURBOT; [10] proposes a semi-active suspension compliant with the behavior of the FURBOT suspensions during the phase of transport and we adopt a similar modelling for simulation; [11] has given a reference and hints for the operational case of FURBOT changing ground clearance to prepare a phase of loading/unloading of freights; an example of rear tire dynamics is in [12,13] develops a concept of ride comfort that in FURBOT is interpreted as appropriate dynamics at the interface between freights and vehicle and developed then as rules for load safety and stability during travelling. The aspect of validation of the model in particular for chassis design is discussed in the review [14]. The basics of the modelling used are in classical literature, in particular [15,16]. The control built over the multibody model follows the classical methods in [17,18] with the transient discussed as for instance in [19] where focus is on controllability in travel conditions that we adapt to the case of load onboard and structural integrity of the loading deck of the FURBOT. 1.4. Scope and contribution of the paper As shown in the review above, the methods proposed and discussed in the literature are either for the development of vehicles meant in a traditional sense to be operated by humans and designed using appropriate methodologies involving vehicle dynamics, or for the development of mobile robots and robotized vehicles where the focus is on aspects typical of robots, like sensing for ambient awareness, planning, navigation, intelligence; in these cases still minor or null is the attention paid to an appropriate mechanical design. When it deals with transporting passengers or replicating the function of any other type of vehicle currently in use with human driving, a convenient approach that is adopted is to modify and equip a commercial chassis; when this is not possible as there is no ready chassis to take, the dedicated designs done are of primitive chassis with poor performance, for low speed, modest vehicle dynamics overall, generally oversized. Now the perspective is of applications where human driving will be a backup, or even not foreseen, for services thought from the beginning to be provided by automatic vehicles: here the way of the modified commercial chassis is leading to very inappropriate solutions, bad or low performing, on the other hand, the specific design done cannot be poor, especially when dealing with full electric system where the energy efficiency matters a make a difference. Assemble four wheels on a board with a battery somewhere is not anymore a possible way. The case of Furbot falls in this class: cannot be a forklift, cannot be a modified van as the loading deck must be very close to ground, and therefore had to be designed for the application. The methods used have been the same as for a market vehicle and presenting this approach, not familiar to developers of robotic vehicles, is one scope of this paper. FURBOT can be equipped to operate autonomously but the current regulation for use in public areas still requires a person is present onboard and has responsibility on the operation of the vehicle. The skills required to the operator and the intensity of attention are limited, compared to traditional freight vans, by introducing a SW filter that uses data from lidars present on the vehicle to adjust the trajectories, prevent and avoid collisions, and adjust the speed of the vehicle for the integrity of the vehicle chassis and of the freight transported and for the safety of the people close to it and the environment. A second scope of the paper is to discuss the modelling done to decide, check and tune the strategies used by this filter; for that the same model used for the design of the chassis has been used, running tests typical of the automotive design but overlaying a control using such filters (for example to moderate the speed and so prevent load falls and chassis structural dynamic overloading). So the contribution of the paper is about the architecture of the parametric model, the way it is used for the optimization of the subsystems of the new vehicle, for the definition and the setup of suitable control (filtering) strategies and to evaluate the reliability and effectiveness of operation. In these terms the vehicle model, or virtual prototype, has been used as a tool for supporting the whole design phase. 1.5. Organization of the paper The paper is organized in four following sections. Section 2 describes the virtual prototype and it is further split in three subsections on frame, suspensions, and steering and virtual pilot. Section 3 is on model validation and presents extensively the method 3

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proposed and the way it has been applied. Section 4 is on the dynamic analysis of the virtual vehicle and it is organized in four subsections that treat the benchmarking cases of double lane-change, minimum steering radius, circular trajectory at increasing speed, braking stability testing. Section 5 presents the driving assistant logics considered and proposed. Section 6 is on conclusions. 2. Virtual prototype The operation of FURBOT is analyzed using a multibody model in a 3D environment. The overall model comes from the integration and merge of the models of the main subsystems in which the vehicle is split: frame, suspensions, steering and virtual pilot. The starting point is an initial design to further optimize and develop through the process. The following addresses the main subsystems and explains the adopted model hypotheses. 2.1. Frame The vehicle frame influences significantly the vehicle behavior both for the direct consequence on the travel dynamics and due to physical conditioning on the other sub-systems that are assembled onto it. Furthermore, the FURBOT frame houses the loading/ unloading system consisting of two freight-handling robotized devices. The frame reference solution is composed of a minimalist network of welded stainless steel tubes with square and rectangular sections. In particular, the loading deck uses four 100 mm x 40 mm x 2 mm thickness pipes with transversal connection beams. The structural design of the frame has been developed through two sets of analyses: static for the first definition of the geometry and sections; dynamic with time variant external loads at the interfaces of the suspensions to minimize the mass of the structure. The accurate modelling of this sub-system influences significantly the reliability of the virtual prototype and its characteristics. Because the frame structural elasticity could influence the vehicle dynamics, the multibody model takes into account the flexibility of the frame components. The material is supposed to undergo elastic deformations and the assumption is confirmed by numerical analysis of the simulation results. Then the approach based on mode superposition has been considered acceptable to represent the structural behavior while performing different operations. Then, to accommodate the inclusion of flexibility, multibody simulation incorporates modal data obtained from a finite element analysis to describe the bodies that deform. The technique is referred to as component mode synthesis or modal superposition. For this study, the Craig-Bampton method has been used, describing the motion of the components as a combination of Normal Modes and of Static-Constraint Modes [20]. Normal modes are used to represent the natural vibration of the body and static-constraint modes are used to account for localized loading and deformation caused by coupling the body to other vehicle components. This coupling generally comes from the flexible body being connected to other components through joints and force elements, such as springs and actuators. Based on the data so obtained, the vehicle flexible frame has been introduced into the model. This approach provides structural information in terms of stress and strain useful, beyond the frame right dimensioning, to identify the critical points more sensible in terms of deformation to the vehicle under operative loads that better can be used as feedback for protection of the vehicle integrity. Beam elements have been used to simulate the frame structure; lumped masses have been introduced to model vehicle components as electric motors, hydraulic systems and the payload. MPC (Multi Point Constraint) elements are used to model the connections of mesh elements to lumped masses and to other subsystems interfaced to the frame (front and rear suspension, steering subsystems). Fig. 2 shows the reference vehicle frame and frame model used in the multibody environment. The loading scenario is of two standard euro pallet sized loads, each of mass 500 kg, laid over the prongs of the fork units. The center of mass of the load units is considered both at the center as for uniform mass

Fig. 2. Chassis frame (a); FEM model (b). Markers and arrows highlight the interface points between subsystems and to lumped masses, in particular modelling the load units of 500 kg each.

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Fig. 3. Opposite test for front suspension (a) and parallel test for rear suspension (b). Visualization of the two extreme conditions.

distribution and at 0.3 m toward the open side of the vehicle to represent an unbalanced load unit stressing the situation of loss of stability of the load. 2.2. Suspensions The suspensions of the vehicle are made up of a McPherson strut with a telescopic damper integrated with a lifting hydraulic cylinder that allows moving vertically the entire chassis. So the height of the vehicle loading deck from ground can be changed and the loading deck can be positioned at the same level of, for instance, a sidewalk, during loading-unloading of a freight pallet [1]. A single contact point tire non-linear model has been used that can handle transient driving situations into the multibody dynamics software. The friction coefficients are calculated as a function of the value of slip, adopting the algorithm reported in [21]. The main characteristic angles and parameters of the front and rear suspensions in different operating scenarios have been analyzed. “Parallel travel” and “opposite test” (Fig. 3) were investigated (identical and opposite pumping of the suspensions of an axle respectively) and the obtained camber and toe angle variation have resulted acceptable for the front and rear axles (1 deg max change). Moreover chassis down shift during the phase of vehicle loading/unloading was also considered and also in this case the suspension behavior showed acceptable. The travel-track value variation for the front and rear axles for the previous considered suspension strokes is acceptable in all conditions; transversal drift of the wheels, when the suspensions range between their extremes, does not involve considerable sliding of the tires on the ground and so this should not influence tire wear. Measures on the physical prototype have confirmed the values obtained through simulation. Static simulations have been used to evaluate the effect of the traction torque on the toe angle of the rear wheels. They are mainly used to analyze the influence of the rubber bushings and other elastic connections on the behavior of the suspension. The loads adopted are the same as those generated by the vehicle's propulsion system in normal operating conditions. The input to the simulation is the longitudinal force (wheel thrust): the variation of toe angle is calculated considering the torque at wheels required to generate that longitudinal thrust force. The relationship between toe angle and longitudinal force (between ground and tire), generated by the propulsion torque, is shown in Fig. 4. This value provides information on the suspension stiffness and on the transmissibility characteristics of excitation generated by road irregularities on the vehicle main frame. In this case, the small variation of this angle with the transmitted torque underlines the good vehicle rear suspension characteristics (also ensuring uniform tire tread wear). This result shows that, when changing the amount of wheel thrust, the toe angle does not change significantly and so the vehicle does not vary characteristics and behavior in a way that might compromise its drivability. This is relevant to assisted or autonomous driving as simplifies an aspect of the vehicle control requiring lower promptness.

Fig. 4. Vectors representing contact forces between tire and ground in the test "traction load case" (a); toe angle change for the rear suspension in the case of traction load case (b). 5

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Fig. 5. The extreme positions of a Benevelli axle model in an opposite test performed in the multibody simulation environment.

Alternative suspension architectures have been considered too. A commercial stiff rear axle has been modelled, assembled to the vehicle and its behavior in terms of camber and toe angles in parallel and opposite tests (Fig. 5) evaluated through simulation proved to be acceptable. Another solution evaluated for the front suspension derived from a commercial vehicle of the same size of FURBOT, shown in Fig. 6 demonstrated an acceptable behavior. The results achieved in this analysis highlight the possibility to adopt different suspension architectures keeping appropriate response of the vehicle in all operational phases included that of freight loading/unloading. 2.3. Steering and virtual pilot The steering system in the prototypes (physical and virtual) consists of a translating rack operated by a torque input (Fig. 7a). Endstrokes define the maximum vehicle steering limits. In the model, for the pursuit of the desired trajectories, a path follower control has been used whose input is the desired trajectory and the output is the sum of a proportional and a derivative term of the error between the desired trajectory and the real one evaluated on a reference placed on the vehicle. A look ahead parameter is introduced corresponding to the distance of a point, ahead of the vehicle, used to calculate deviation from the prescribed path. The output, appropriately amplified, is applied as a torque to the steering system [22]. The control algorithm recalculates the output every time-step, this means it can respond to changes in the trajectory. To clarify the parameters used, consider Fig. 7b: the desired trajectory is the continuous red line; the vehicle is supposed to currently move along a wrong trajectory (dashed red line); the position error used by the control is the blue line shortest distance between the current position of the vehicle and the desired trajectory. The control parameters have been identified through a trial and error process in order to obtain an adequate reproduction of the desired trajectory in all significant operations of the vehicle. Fig. 8 shows the sensitivity of the vehicle's response to vehicle variation of the speed and of the position and velocity gains. It was possible to identify values appropriate to the vehicle's driving dynamics and to the maneuvers required. A longitudinal control has been developed that allows the vehicle's traction system to be reproduced. A gain curve has been specified which allows to apply a torque to the rear wheel to correct its speed relative to the desired vehicle speed specified. Similarly, a comparable control allows the vehicle's braking system to be simulated. In order to respect the mechanics of the physical prototype, the torque both in traction and in braking drives only the rear wheels. 3. Model validation The validation of the numerical model was carried out through different types of correlation with the reference physical prototype. A first static correlation was conducted with which it was possible to verify that the physical and virtual prototype had the same mass value and the same position of the center of gravity in the absence of a payload. The longitudinal and transverse positions of the center of gravity have been obtained by measuring the loads on the individual

Fig. 6. Front suspension using off the shelf components of a lorry on the market. 6

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Fig. 7. (a) Steering subsystem and (b) path follower control for the pursuit of the desired trajectories.

Fig. 8.. Sensitivity of vehicle response to various parameters of the path follower control evaluated on a double lane change maneuver.

wheels keeping the vehicle horizontal. The loads on the wheels of the vehicle have been measured in the physical prototype and the total mass of the vehicle calculated. For the localization of the center of mass vertical position on the physical prototype an experimental method was adopted giving also the height of the center of mass through weighting of the vehicle with an axle elevated above the ground of a known length. By modifying the positions of some concentrated masses corresponding to the contributions of the electric power components, transmission elements and the system for unloading and loading the freight, it was possible to obtain a good correlation with the model. Subsequently it seemed appropriate to proceed with a second correlation of dynamic type comparing the vibrating modes of the numerical model with those obtained experimentally on the physical prototype. The classical technique of experimental modal analysis has been used to get the modal model (frequency, damping and mode shapes) of the physical prototype [23]. The method called impact technique was adopted, with an instrumented hammer to apply and measure the excitation of the structure. Measurements were performed through a “fixed hammer” procedure, i.e. the position of the excitation point was kept fixed and the response point was varied. The excitation was applied on two points of the frame (see green marker in Fig. 9) in 3 directions and the first vibration modes

Fig. 9. Geometric model for impact testing on the chassis (a); hammer impact excitation on frame (lower excitation point) for FRF measurement (b). 7

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Fig. 10. Sum function (red trace), stabilization diagram (frequency domain algorithm) and mode indicator function (dotted green trace). Blue frequency band -> rigid body motion on suspension; yellow frequency band -> frame flexible modes (a); experimental mode shapes of one vibration modes of the frame extracted in the frequency band of the analysis (14.01 Hz) (b).(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

extracted. The mass of the hammer, 5 kg, was sufficient to provide enough energy to the structure during the impact measurement. The response was acquired through ICP three axial micro accelerometers having a 10 mV/g nominal sensitivity. Acquisition and processing of the signals were done using a Siemens LMS SCADAS acquisition front end. In the present study, 227 experimental response points were considered and their positions are shown in Fig. 9 by the markers. For each point, five measurements were taken and the frequency response function (FRF) average was saved for the subsequent extraction of vibration modes. Based on the acquired transfer functions, the sum FRF (the sum of the FRFs representative of the overall system behavior) was calculated and then both a time and frequency domain algorithms were used to extract vibration modes. The methodology is valid only for conditions where the system has a sufficiently linear behavior. This has been verified by comparison of subsets of measured and simulated datasets confirming the assumption. Coherence functions have been introduced and they resulted acceptable for all impact tests (system sufficiently linear). The vibrational responses recorded during the impact tests have been compared to ones recorded with vehicle prototype travelling; the pairs of sets have resulted comparable. As a consequence, the time-dependent forces experienced during the impact testing and during real travel are similar and the assumption of linearity extends to the case of travel of the vehicle. Next, by simulation of the vehicle travelling, the loads in the chassis have been calculated and they resulted comparable to the ones applied with the impact testing [24]. Fig. 10a shows the total FRF of the system in the frequency range 2 to 20 Hz and the stabilization diagram obtained with an identification algorithm in the frequency domain to extract the experimental mode shapes. In the 2–20 Hz frequency bandwidth, 12 modes were extracted (other poles in the stabilization diagram did not provide wellinitiated modes and were excluded from the modal model); Table 2 shows for each mode the value of the frequency, the damping, and a short description of the mode shape. At the lower frequencies, the vibrating modes are characterized by rigid motions of the suspended masses of the vehicle on the suspensions (frequencies from 3.07 to 9.53 Hz, see sliders positioned in correspondence of the modes extracted in the frequency band highlighted in blue). For greater frequencies in the deformations the flexural contributions of the frame structure become very evident (see cursors in the frequency band highlighted in yellow). At higher values of frequency, a more complex dynamics of the overall system has been found. To determine the numeric model of vibration modes, a linearization of the equation of motion is performed and eigenvalues and Table 2 Modes extracted from the overall structure in the range 2–20 Hz and description. Mode

Frequency [Hz]

Damping [%]

Mode shape description

1 2 3 4 5 6 7 8 9 10 11 12

3.07 4.84 4.95 6.13 6.97 9.53 9.79 9.95 14.01 16.91 17.08 19.76

2.2 4.2 4.7 3.2 2.0 1.1 3.3 0.16 0.18 1.24 5.9 2.9

Rolling of the vehicle; roll axle under the wheels plane Rigid motion associated with yaw Vehicle pumping, flexural contribution of front vertical uprights Roll of the vehicle with axle near the center of gravity Pitch of the vehicle with the transverse axis close to the center of gravity Roll of the vehicle; torsional contribution of vertical uprights Flat flexural characteristics (lower frame): deformation of the loading bed in the longitudinal direction Torso-flexion of the loading bed, bending of the platform in two-vented Torsion of the floor and two-vent bending of the upper part of the frame Flexion of the front and rear struts in phase with a torsional component of the loading bed and roof Torsion of the entire structure Flexion to one belly that most involves the frame loading bed

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Fig. 11. Vibration mode at 9.794 Hz obtained from simulation (a) and from experimental modal analysis (b).

eigenvectors are extracted. The software shows through animations the computed solutions, showing the mode shapes of the flexible bodies inserted in the multibody model. For this particular condition, the theoretical-experimental correlation was evaluated using the modal experimental data and it was possible to update the model and validate it. A good agreement between the simulation and the experimental data was found for the most significant vibration modes. As an example, the comparison of the corresponding simulation and experimental modes is reported for an extracted mode in Fig. 11. A time frequency analysis of the operational vibration measured on the structure of the chassis during vehicle travel (different maneuvers on different types of paths), highlighted components consistent with those identified with the modal analysis allowing to validate the analysis (Fig. 12); during the motion, the contributions of the rigid motions (4–10 Hz) and the bending ones of the frame structure (>10 Hz) are present in the vibrational response of the vehicle. It seems that the model realized and then validated can be considered a very powerful forecasting tool to deepen aspects of the dynamics of the prototype and for the definition of control systems that improve the functioning in every possible operating condition. 4. Dynamic analysis of the virtual vehicle The full vehicle model comprises vehicle mass and inertia, front and rear suspensions, a rack and pinion steering system, and the wheel assembly [25–28]. A general formulation method, according to the multibody approach, based upon the Lagrange's equation for constrained systems is employed for the derivation of equations of motion for all subsystems of the vehicle [29]. The vehicle has been simulated during some basic maneuvers following the method in [22]. The focus is on vehicle safety and stability in the congested and obstacle-rich environment where the delivery van needs to follow trajectories with numerous tight turns.

Fig. 12. Time frequency analysis operational vibration signal measured during a sequences of vehicle maneuvers apt to excite most vibration modes; low frequency contents seem related to modes of chassis masses on suspensions (see cursor at 3.8 Hz) while higher frequency contents (cursors at 10.2 and 22.6 Hz) to flexional frame vibration modes. 9

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Fig. 13. Forces on the four wheels (a); and sideslip velocity (b); when the vehicle performs a double lane-change carrying no boxes.

The maximum driving speed of the vehicle is 40 km/h (12 m/s). Although this is low compared to common trucks, the FURBOT van may have problems of stability on small-radius turns typical of the urban restricted areas of delivery. The vehicle may tip over due to a high offset center of mass at full load: the axle track is relatively short, 1.4 m, with respect to the vehicle height of 2.1 m, while the load may be concentrated in either one or two pallets. During the design of the vehicle, the simulations briefly presented here have been used to analyze load and vehicle stability by analyzing the danger of a tip-over during turns and the margin available for emergency correction of the trajectory in the event of obstacle avoidance. Three driving situations have been simulated, evaluating the stability and the distribution of the forces among the four wheels in each case. The scenarios are: double lane-change, minimum radius turn, and acceleration on a circular trajectory. In each simulation, the friction coefficient for the contact between the wheels and the ground is set equal to 0.8 (dry road). For each scenario, three loading cases have been analyzed: no load (i) and maximum load (900 kg) with the center of mass at a height of 0.9 m (ii) or 0.7 m (iii). 4.1. Double lane-change The trajectory is defined according to the ISO3888 double lane-change test. The driving speed is assumed constant and equal to 12 m/s. The simulation is aimed at assessing the distribution of the total load of the vehicle among the four wheels. The reaction force on each wheel in the case (i) is shown in Fig. 13a, while Fig. 14 shows the frame of the simulation in which the vehicle performs the second lane change. As it can be seen, the vehicle preserves its stability along the trajectory: in fact, the normal forces on the wheels are always greater than zero along the whole trajectory and no global sideslip is observed during the simulation (see Fig. 13b where it is possible to notice sideslip velocity stays below 0.08 m/s). The behaviour is similar when the vehicle is loaded according to conditions (ii) and (iii) with different values of amplitude. 4.2. Minimum steering radius The aim of this analysis is to find the minimum steering radius that the vehicle can perform at the maximum speed of 40 km/h without tipping over. Since the FURBOT vehicle also provides assistance to the driver, preventing dangerous maneuvers, an

Fig. 14. Reaction forces at the four wheels when the vehicle performs the second lane change. 10

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Fig. 15. Spiral trajectory (Furbot on the left of the picture, motion left to right along the spiral).

automatic limit on the steering radius can be imposed depending on the driving speed. In order to determine this value, a spiral trajectory has been simulated (Fig. 15). The vehicle engages it at the end of a rectilinear path, and follows the curve until stability is compromised. The (maximum) initial steering radius of curvature on the spiral is 25 m. For the load case (i), the vehicle sideslips after 14.3 s at a steering radius of 15.5 m. In case (ii) the vehicle tips over after 13.5 s, that is, when the steering radius is equal to 16.9 m; no global sideslip occurs in this case. In case (iii), the vehicle loses stability after 13.9 s, at a steering radius equal to 16.1 m; in this case, before tipping over, the vehicle sideslips, leaving the prescribed trajectory. From the comparison between load cases (ii) and (iii), it is immediately clear that a proper distribution of the load inside the box can significantly improve the stability of the vehicle: as for any other vehicle, the stability is strongly influenced by the position of the center of mass. Among the three cases investigated, (ii) can be considered the worst. In Fig. 16, the reaction forces versus time are plotted in the load case (ii). Fig. 17 shows the reaction forces for each wheel (blue arrows) at a generic point of the spiral trajectory in which the vehicle is stable. As expected, the two wheels on the right side support most of the total weight of the vehicle during the whole simulation. As already said, after 13.5 s the vehicle tips over. This can be observed also from Fig. 16, since after 13.0 s, the reaction forces on the left wheels are null and 0.5 s later the solver stops the simulation due to the overturn. The same analysis has been conducted in the case of a lower vehicle velocity (10 m/s) and analogous and coherent results have been obtained. 4.3. Circular trajectory at increasing speed A circular trajectory is prescribed (see Fig. 18) and the vehicle runs along it increasing its speed linearly from 5 m/s up to the value of speed at which instability occurs. The velocity increases slowly enough to neglect the effects of the acceleration during the simulation. The radius of the circle is equal to 12 m, which is a significant steering radius for a vehicle circulating in the city center. In the load case (i), the vehicle sideslips when it reaches the speed 9.65 m/s, without tipping over. Similarly, in the load case (ii), the maximum speed allowed is 8.5 m/s but in this case the vehicle tips over. The reaction forces on the four wheels are represented in Fig. 19.

Fig. 16. Forces acting on the four wheels when the vehicle runs along a spiral trajectory (load case ii). 11

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Fig. 17. Vehicle loaded (ii) along the spiral trajectory.

Fig. 18. Circular trajectory of 12 m radius (Furbot on the left of the picture, motion left to right and counter clockwise along the circle).

Fig. 19. The vehicle tips over along the circular trajectory with load conditions (ii).

Like in the two previous sets of analyses, it has been seen that a lower center of mass allows improving the stability: in the load case (iii), the maximum speed reached before the vehicle loses its stability is equal to 8.88 m/s. In this case, the vehicle does not tip over, but it sideslips far from the trajectory. Table 3 shows the synthesis of the results obtained from the previous analyzes with some parameters that identify the dynamic behavior of the vehicle (roll angle, critical speed and radius) and the structural stress (Von Mises stress). 12

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Table 3 Basic maneuvers results - no load (i) and maximum load (900 kg) with the center of mass at a height of 0.9 m (ii) or 0.7 m (iii). The vehicle: (1) sideslips; (2) tips over. Maneuver

Max Roll angle [deg]

Critical speed [m/s]

Critical radius [m]

Von Mises strees [MPa]

Double lane-change

–

–

Minimum steering radius (12 m/s)

2.3 (i) 8.5 (ii) 5.7 (iii) –

–

Minimum steering radius (10 m/s)

–

–

Circular trajectory (R = 12 m) at increasing speed

–

9.65 (i) – (1) 8,5 (ii) – (2) 8.88 (iii) – (1)

15.5 16.9 16.1 14.3 16.1 15.1

6.25 (i) 18.5 (ii) 15.7 (iii) 9.59 (i) 20.2 (ii) 17.8 (iii) 9.0 (i) 18.0 (ii) 15.6 (iii) 7.84 (i) 20.3 (ii) 17.7 (iii)

(i) – (1) (ii) – (2) (iii) – (1) (i) - (1) (ii) - (2) (iii) - (1)

The results seem consistent with each other and underline how the load arrangement influences the driving characteristics of the vehicle. Moving from a height of the load center of 0.7 m to 0.9 m, in all the cases considered the condition of instability of the vehicle at side loads seems to pass from sideslip to tip over. The greater structural loads on the frame have generally been achieved for the full load conditions with the center of gravity in the highest position. It is possible to obtain the structural conditions of the frame during the entire simulation, allowing to identify the most critical moments and display the most stressed areas at these moments (see Fig. 20, referred to the case of double lane change, maximum load 900 kg with the center of mass at a height of 0.9 m). 4.4. Braking stability test In the case of braking maneuvers, the analyses have been performed for different values of deceleration and refer to a straight line trajectory. It was considered the heaviest condition i.e. full load with the center of mass at a height of 0.9 m and assuming that the vehicle brakes only with the rear axle. The frame FEM model allowed evaluating the contribution of the frame flexibility in this condition. Frame deformation in a full-load extreme braking maneuver characterized by high deceleration is very low and negligible compared to the change of extension of the suspensions (Fig. 21, shows the frame deformations at the most critical time instant of the simulation). Different cases of deceleration have been also simulated; Fig. 22 reports some results in terms of vehicle velocity and pitch angle (blue trace -> maximum deceleration, black trace -> minimum deceleration; red and green -> intermediate values). Suspension behavior and operational extensions of the suspensions during the braking maneuvers have been calculated in simulation (see Fig. 23); acceptable values have been obtained and so the geometry and configuration of the suspensions used in the physical prototype results validated.

Fig. 20. Stress Von Mises distribution on frame in correspondence of the instant of maximum solicitation (t = 7.81 s) (doable lane-change, load case ii). 13

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Fig. 21. Forces tire-ground (a) and frame deformation during an extreme braking maneuver (b).

Fig. 22. Vehicle speed (a) and pitch angle (b) traces during braking maneuvers (load case ii).

Fig. 23. Suspension strokes during the braking maneuvers: front (a) and rear (b) suspensions.

The variations of the contact forces ground-tire are shown in Fig. 24. This is used to check the transfer of load levels during braking. It is important that no contact forces become too low, in order to avoid wheel locking. The suspensions behavior was also analyzed in an asymmetrical braking obtained considering different friction coefficients for left and right tire (e.g. left tires -> high friction coefficient -> dry ground; right tires -> low friction coefficient -> wet ground). In this condition different braking forces on the two wheels of the same axle can induce asymmetry and induce vehicle instability. The results show that the vehicle behavior is good also in this case, Fig. 25: steer correction is very small and this underlines the good suspension performance also in this case. 5. Driving assistant filters Through the virtual prototype, it was possible both to develop and to perform preliminary checks of possible controls, intended as 14

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Fig. 24. Normal contact forces between tire and ground during the braking maneuvers: front (a) and rear (b) suspension.

Fig. 25. Steering angle comparison during braking - blue trace -> symmetrical, red trace -> asymmetrical braking conditions (a); tire longitudinal force for low (blue trace) and high (red trace) friction coefficient (b).(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

filters between driver and vehicle, which can be implemented in the system. For this purpose, a 0D multi-domain software was used, where the controls, interfaced through a co-simulation with the 3D multibody model, were realized. The scheme is of the master-slave type and the master role is covered by the multibody solver. During the co-simulation, the two softwares communicate to each other the involved data. In the multibody model it is therefore necessary to insert position, speed or acceleration sensors that communicate this information to the 0D code and actuator elements that exert forces and torques coming from the control on the multibody system. Some preliminary results are reported where the effectiveness of different strategies to preserve the load and to protect the vehicle is evaluated. The way the filters are used is shown in Fig. 26 and the following refers to this schematics. The first considered strategy acts on the vehicle speed in case the centrifugal field, due to the curvature of the path, can be such as to make the load move from the frame. In fact, for reasons of flexibility and speed of loading and unloading, it is assumed that the pallet is simply resting on the forks and this remains integral with the vehicle during travel due to the presence of friction [1]. It is assumed to be able to evaluate the value of the tangential and normal forces exerted by the pallet on the fork (for example using load cells with appropriately oriented directions) and use their ratio (suitably averaged) as feedback modifying the value of the target speed of the vehicle. An example is shown below where it is assumed that the vehicle, after an initial transitory phase (consisting of a straight section and a speed ramp), is describing a spiral trajectory in the direction of the decreasing curvature radii at constant speed equal to 8 m/s (t> 20 s). Fig. 27 shows that in the absence of control (red dotted line), the value of the ratio increases as the radius of curvature of the trajectory decreases until reaching a limit value corresponding to the loss of the static condition of the pallet resting on the vehicle forks and therefore the relative motion of this with the consequent loss of the load. Fig. 27b indicates the value of the vehicle's progress rate which remains almost constant. The blue trace refers to a condition in which there is a control capable of appropriately limiting the speed of the vehicle so that the static condition of the pallet on the forks is not lost. In this case, the previous ratio between tangential and normal force discharged from the pallet onto the forks is used as a reference. The control that intervenes on the speed of the vehicle by braking it, determines a decreasing trend of the velocity in the instant whose ratio reaches the value 0.36 15

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Fig. 26. Architecture of the driving assistance filters and the way they operate on the vehicle control.

Fig. 27. (a) Ratio between tangential friction force and normal reaction between pallets and forks; (b) speed profile followed by the vehicle with and without load control; at the instant 12.5 the vehicle ends the straight line of trajectory and enters the spiral; near the moment 20 s the vehicle ends the transient and reaches the reference speed equal to 8 m/s. The intervention of the control takes place near the instant 25 s where there is a reduction in speed that allows the ratio to be kept within a limit level.

(it is assumed that a value higher than 0.38 implies the loss of the load). Fig. 28 shows, with reference to the same time instant, the vehicle without control (more advanced shape with the pallet that is about to be lost) and the vehicle with control (backward silhouette with pallet still resting correctly on the fork). The reduction in speed operated by the control makes the vehicle in the second condition more back. In this case it is considered a control able to modify the speed profile initially set in order to avoid the loss of load. Alternative solutions could act on the values of stiffness, preload and damping of the suspension modifying the attitude of the vehicle while driving and its effectiveness can always be analyzed with the model. Furbot can be driven by low-qualified personnel (drivers of a fleet of freight delivery usual to drive commercial vans and that did not receive a specific training to the FURBOT vehicle) and therefore can be in situations that could damage its structure or significantly reduce its useful life. A further task of the control intended as a filter between the driver and the vehicle is therefore also that of intervening in the event of incorrect maneuvers which may lead to the achievement of critical conditions. In the presence of maneuvers on the road with significant irregularities, the vehicle must be able, regardless of the driver's request, to adjust the speed value so as to not compromise its integrity and possibly also the load it carries. For this purpose, a control acting on the vehicle speed has been implemented in the model and validated through simulation; its aim is to reduce the speed appropriately in the case where the "root mean square" (rms) value of the vibration on a point of the frame, 16

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Fig. 28. Vehicle FURBOT in the two conditions without and with the control of the stability of the load (the system with the active control is delayed because it adopts a reduced speed profile).

representative of the state of structural stress of the entire vehicle, exceeds a certain level. In case a size has been chosen linked to the dynamic state of the system and for the calculation a reference has been assumed in the vicinity of the right front wheel hub considered significant of the vibrational response of the vehicle as it is directly exposed to the forcing linked to the rolling of the wheels. For the verification, a simulation was considered where the vehicle in full load conditions at a speed of 6 m/s runs a straight path with irregularities that become not negligible in the final part and such to exceed the level of acceptability of the vibration evaluated at the reference target. In case a parameter has been chosen linked to the dynamic state of the system and for the calculation a reference has been assumed in the vicinity of the right front wheel hub considered significant of the vibrational response of the vehicle as it is directly exposed to the generalized forces linked to the rolling of the wheels. For the verification, a simulation was considered where the vehicle in full load conditions at a speed of 6 m/s runs a straight path with irregularities that become not negligible in the final part and such as to exceed the level of acceptability of the vibration evaluated at the reference target. Fig. 29a compares the trend of the rms value of the acceleration in the case where the strategy is active (blue trace) and not (red trace). It is noted that in the presence of the control, exceeding the threshold value of the vibration equal to 1 g, used as feedback, activate the braking system of the vehicle that reduces the forward speed by lowering the rms level until it falls below the threshold value. Fig. 29b shows the trends of vehicle speed in the two cases. Fig. 30 shows the vehicle in an instant of the simulation (t = 9 s) where the control has been activated and the vehicle is reducing the speed to bring the vibration within the rms limit value. The displayed vectors are representative of the asphalt wheel contact force: it is noted that a braking phase is under way on the rear wheels which exchange a force with a high tangential component opposite to the motion with the road. The same strategy is related to the load so that it does not suffer damage due to excessive levels of vibration during transport. In

Fig. 29. Acceleration measured on the vehicle chassis (a): the rms value (obtained on an integration time of 1 s) is used by the control to limit the speed value; (b) vehicle velocity profile (strategy is active -> blue trace) and not -> red trace).(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 17

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Fig. 30. Passage of the vehicle from a smooth track to one with high irregularity leading to the intervention of the control that limits the speed (note how the rear axle wheels are braking the vehicle).

this case it is advisable to provide in additional information on the material being transported. Depending on whether it is fragile or not, the reference in the control and the location of the feedback signal must be appropriately set [30]. Similarly, variables related to the state of static stress of the frame (not visible using a vibrational target as in the previous case) can be used in the control loop to obtain also a strategy capable of preserving the vehicle during dangerous maneuvers. A solution could use strain gages to monitor the deformation state of the frame; these can be arranged in such a way as to maximize the combined response at the static deformations that the structure can assume in critical operating conditions. In the case analyzed by means of the simulation, the sum of the absolute value of the deformation of two opposite points on the front crosspiece of the chassis, considered representative of the torsion state, was used as feedback and it was evaluated how to intervene and slow down the vehicle in the case where this exceeds the limit of acceptability. About the result obtained from the virtual prototype in the case of variable curvature traversed at constant speed, it is noted that above a certain deformation, the intervention of the control avoids the further increase of the torsional deformation by appropriately slowing down the vehicle through the action of the braking system. Fig. 31 shows the trends of the torsion angle of the chassis and of the vehicle speed in the presence and absence of the control. It is noted that in the moment in which the control intervenes, a slight damped oscillatory component is generated in the response of the system. In Fig. 32 the static deformation of the frame is displayed at the moment of activation of the control. The deformation pattern, returned by the simulation software, in the considered critical conditions, can provide useful information on the best positions where to install the feedback sensors in order to make the whole control more effective and sensitive. In this case the criticality of the condition must necessarily be identified through static quantities as dynamic quantities such as the rms value of the vibration would be insensitive and therefore inadequate to detect this state of stress. On the contrary, the use of a static feedback does not allow monitoring of damage conditions deriving from dynamic loads that generate a state of fatigue stress. A sufficiently effective self-protection of the vehicle is deemed to combine dynamic feedback (to check for fatigue damage) and static feedback (to avoid excessive stress conditions which can lead to break the structure). The two proposed solutions can therefore be adopted and operated synergistically for this purpose.

Fig. 31. Torsion angle measured on a crosspiece at the front axle of the vehicle frame in the case of variable curvature trajectory and constant speed (a): the value is used by the control to limit the speed (b). 18

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Fig. 32. Deformation of the frame when torque is maximum: the use of a strain gauge signal as feedback seems adequate considering the good sensitivity of the structural response of the frame to the operating loads in critical conditions (189/186 nodes on transversely opposite platforms).

6. Conclusion The paper proposes a method for the design and development of vehicles supposed to be robotized and able at some extent to make decisions about travel and maneuvers. This is presented referring to techniques in use for the design of traditional commercial vehicles during the phase of definition of the suspensions and assessment of the performance before detailed design undergoes. All this is not done by the teams designing robotic vehicles and the result is performance lower than achievable (typically the robotic vehicle has oversized chasses design referring to quasi static operational conditions). Through the realization of a virtual prototype, several simulations have been performed during the design process (for instance static structural and modal finite element analyses). In particular, the stability of the vehicle in different loading and driving conditions has been analyzed by means of multibody simulations. The results obtained will be used also to implement in the vehicle strategies to mitigate risks from dangerous operations leading in particular to loss of stability. The use of the model allows also verifying the functionality of controls that can be implemented in the vehicle; in this specific case, the operation of some for the protection of the load and of the vehicle itself was analyzed. It is expected that the model developed is a powerful predictive tool that can be further used for subsequent developments and the verification and development of further advanced control systems and strategies. Acknowledgments This research paper shows the results for the FURBOT project (FP7-SST-2011-RTD-1) funded and supported by the European Commission. References [1] A. Dinale, R. Molfino, P. Huang, M. Zoppi, A new robotized vehicle for urban freight transport, The 15th International Conference on Harbor, Maritime and Multimodal Logistics Modelling and Simulation, 2013, Sep. [2] E.M. Cepolina, The packages clustering optimisation in the logistics of the last mile freight distribution, Int. J. Simulat. Process Model. 11 (6) (2016) 468–478. [3] M. Bruning, W. Schonewolf, Freight transport system for urban shipment and delivery, Integrated and Sustainable Transportation System (FISTS), 2011 IEEE Forum on, 2011, June, pp. 136–140. [4] G.Z. Burzio, G. Gallardo, Fideus new vehicle solutions for goods deliveries in urban area, Prooceding of the 13th ITS World Congress, 2006, p. 8. [5] M. Dell'Amico, W. Deloof, S. Hadjidimitriou, G. Vernet, W. Schoenewolf, CityLog - sustainability and efficiency of city logistics: the M-BBX (modular bentobox system). integrated and sustainable transportation system (FISTS), 2011 IEEE Forum on, 2011, June, pp. 132–135. [6] M. Aimo Boot, G. Burzio, CITY MOVE: New Concept For Urban Delivery Vehicles, Transport Research Arena Europe, 2010, June. [7] Deutsche Post DHL Group2016, 'Robotics in logistics: aA DPDHL perspective on implications and use cases for the logistics industry', DHL Trend Research, DHL Customer Solutions and Innovation. [8] F. Negrello, P. Silvestri, A. Lucifredi, J.E. Guerrero, A. Bottaro, Preliminary design of a small-sized flapping UAV: II. Kinematic and structural aspects, Meccanica 51 (6) (2016) 1369–1385, https://doi.org/10.1007/s11012-015-0309-7. [9] J. Zhang, X. Li, R. Li, Multi-body dynamics modeling and simulation analysis of a vehicle suspension based on graph theory, J. Beijing Inst. Tech. (English Ed.) 27 (4) (2018) 518–526. [10] A. Khadr, A. Houidi, L. Romdhane, Design and optimization of a semi-active suspension system for a two-wheeled vehicle using a full multibody model, Proceed. Inst. Mech. Eng. Part K J. Multi-body Dynam. 231 (4) (2017) 630–646. [11] Silva Diniz, D.D., De Carvalho, C.C., Silva, A.A.D. “Computational simulation of vertical dynamics for an off-road vehicle by using multibody models” (2017), SAE Technical Papers, 2017-November. [12] A. Bonci, R. De Amicis, S. Longhi, G.A. Scala, A. Andreucci, Motorcycle lateral and longitudinal dynamic modeling in presence of tyre slip and rear traction, 21st International Conference on Methods and Models in Automation and Robotics, 2016, pp. 391–396 MMAR 2016, art. no. 7575167. [13] KM Manoj, G Prasanna, D Anindya, Mathematical models for designing vehicles for ride comfort, Proceedings of the 2nd international conference on research into design, Bangalore, India, 7-9 January 2009, pp. 168–175. [14] E. Kutluay, H. Winner, Validation of vehicle dynamics simulation models. A review, Veh. Syst. Dynam. 52 (2) (2014) 186–200.

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