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Engineering Solid Mechanics 7 (2019) 313-330 Contents lists available at GrowingScience Engineering Solid Mechanics homepage: www.GrowingScience.com/esm A new method to improve passenger vehicle safety using intelligent functions in active suspension system Alireza Rezanooria*, Mohd Khairol Anuar Ariffina, Aidin Delgoshaeia, Nawal Aswan b. Abdul Jalila and Zamir Aimaduddin b. Zulkeflia a Department of Mechanical and Manufacturing Engineering, University of Putra, Serdang, 43400, Malaysia A R T I C L EI N F O Article history: Received 2 March, 2018 Accepted 26 June 2019 Available online 26 June 2019 Keywords: Active Suspension System Vehicle Height Readjusting Simulation Stabilizer ABSTRACT In this research a new electronic based mechanism for vehicle suspension system is designd. The aims are to improve passengers’ safety and comfort. The proposed system is developed for proactive rapid reaction of suspension system which can readjust the height of chassis while confronting with wrong conditions of driving such as unflatted road, rainy or snowy road profile. The results show that the proposed mechanism can successfully increase the stability of the car by readjusting the height of the the chassis and center of the gravity of vehicle while turning. © 2019 Growing Science Ltd. All rights reserved. 1. Introduction The term automotive was first used by Greek people and consists of 2 words auto (self) and motivus which means motions. Automotive industry covers a wide range of manufacturing and services companies for design, engineering, manufacturing, and sailing and after sailing services. Records that are reported by World Health Organization show that road traffic injuries caused 1.25 million deaths worldwide in the year 20101. Using this record, it can be concluded that 1 person dies every 25 seconds during that year. Table 1 indicates regional traffic that causes death in 2013. Of this third world countries and low income countries dedicated more share of this phenomena 24.1 per 100 000 than developed countries (9.2 per 100 000). For example Nigeria, Iran, Malaysia, Thailand and some other countries have maintained a big share than other countries. Table 2 compares some countries in terms of traffic death rate. Over a third of road traffic deaths in low- and middle-income countries are among pedestrians and cyclists. 1 https://en.wikipedia.org/wiki/List_of_countries_by_traffic-related_death_rate#cite_note-datatables-3- Retrived in 0.6.08. 2016. * Corresponding author. E-mail addresses: rezanoori.alireza.idg@gmail.com (A. Rezanoori) © 2019 Growing Science Ltd. All rights reserved. doi: 10.5267/j.esm.2019.6.005 314 Fig. 1. Qouta of countries share in terms of number of manufactured car (2016)- The image retrived from www.wikipedia in 9/22/2018 Table 1 List of regions by traffic yeilds to death2 Country World Africa Eastern Mediterranean Western Pacific South-east Asia Americas Europe Road fatalities per 100,000 inhabitants per year 17.4 26.6 19.9 17.3 17.0 15.9 9.3 Road fatalities per 100,000 motor vehicles 574 139 69 101 33 19 Total fatalities latest year (adjusted/estimated by WHO report) 1,250,000 246,719 122,730 328,591 316,080 153,789 84,589 Table 2 List of some countries by traffic yields death Country Australia Canada Denmark Germany Malaysia United States Turkey Thailand Road fatalities per 100,000 inhabitants per year 5.4 6.0 3.5 4.3 24.0 10.6 8.9 36.2 Road fatalities per 100,000 motor vehicles 7.3 9.5 6.7 6.8 29.9 12.9 37.3 74.6 Road fatalities per 1 billion vehicle-km 5.2 6.2 4 4.9 12.6 7.1 n/a n/a Total fatalities latest year (adjusted/estimated figures by WHO report) 1252 2114 196 3540 7129 34,064 6687 24,237 Year, data source (standard source: The WHO report 2015) 2013 2013 2013 2013 2013 2013 2013 2013 Fortunately, most of the countries now have long term policies to reduce the accidents. Fig. 2 shows the road safety in the year 2016. The information shows that the safety of the roads was significantly increased form the year 1992 to 2016. 2 https://en.wikipedia.org/wiki/List_of_countries_by_traffic-related_death_rate#cite_note-irtad2015-4- Retrived in 0.6.08. 2016. A. Rezanoori et al. / Engineering Solid Mechanics 7 (2019) 315 Fig. 1. Road safety evolution in EU 2. Active, semi active and passive suspension systems As mentioned before the main aim of this research is to design an advanced suspension system for the motor vehicle. An active suspension system known as Computerized Ride Control helps us adjust the system continuously when the road conditions are changing. Constantly monitoring and adjusting system artificially is executed by extension of the design parameters of the system, by means of that changing the system character on a continuing process. By applying modern sensors and microprocessors, the information will sense continuously and also change factors in system to react to changing road conditions. Active suspension suggests better handling, comfort ride, handling, quick respond and safety. Most of suspension systems in automotive industry use measurement system which is able to measure forces on the vehicle body on the same time of vehicle motion (YAMADA & Takayoshi, 2007) but most of time because of lack of adequate process speed or mechanical part operation speed, the slow sensor or controller cannot collect data and slow mechanical part such as Pneumatic, Hydraulic or Magnetic cannot perform commands in minimum time which result in less efficiency of system. Many companies are trying to invent and create new system by high efficiency, fast process and operation. It needs to study of a measuring system in order to evaluate the effect of vertical and horizontal forces and inequality of rough road which affect comfortableness, handling and most important safety of vehicle (Schofield et al., 2006). Information coming from this measuring system will process by controller and move or command to damper or effective part in suspension system therefore wheel and suspension system have to coincide with road profile and provide the stable and suspended body (Leegwater, 2007). Creation a system ables us to predict road profile and its condition is one of the important challenges in automotive industry. Vehicles equipped by this predictor technology can scan and explore all road condition such as roughness, height, snags and bump therefore the vehicle can decide easily how to react to the predicted condition by changing amount of damping coefficient or vertical position of suspension system. The result will be high handling, ride quality, safety, and comfortableness (Jeong et al., 1990). In a land vehicle, travel comfort and handling constancy oppose with each other creation the system hard for vehicles suspension system to follow them at the same time. In order to get better the vehicle act around this issue, many control designs are planned in the structure of computer controlled suspension system 316 such as active or semi-active suspension system. No matter how a road is smooth and flat because it is not a suitable place to move heavy vehicle with high-speed. Therefore the system should able to reduce impact, shock and vibration due to road conditions. The usual passive suspension systems innately result in cooperation between the quality of ride and handling. Good vehicle handling is because of an extremely damped suspension (Tamboli et al., 1999). A lower damped suspension may considerably improve the feeling of rid, but it can decrease the vehicle stability while Ride factor, Handling factor, Body Mount Optimization are others critical issues (Naude & Snyman, 2003). The semi-active suspension system computes the speed of vehicle vibration defined by lateral acceleration sensor as an output. The sensor is fixed on the vehicle body on upper level of the vehicle and makes enough force agreeing in amount of the vibration speed with an interchangeable lateral damper on the vehicle (Miller, 1986). Gordon et al. (1998) designed a system that is equipped with an electromagnetic valve which releases the force in the different direction of damping force. The important issue is that the failure part in system doesn’t cause to dangerous state because when the power switch is turned off, the damper function will act as a normal damper. Choi et al. (2000) designed a system where the objective was to cancel out pitch, heave, and roll. The varieties of inputs are needed for control system in Semi-Active suspensions to measure mentioned items such as Vehicle speed, Vertical acceleration, Brake condition, Lateral acceleration, Steering angle velocity, Vehicle level position, Steering angle position. Active suspension systems consist of components such as Electronic Control Unit, Changeable shock absorber, a series of sensors, an actuator atop each shock absorber. Controlling an active suspension system is based on amount of information which can be collected by some sensors located in different parts in the vehicle. The sensors begin to monitor the situation, check body motion, rotary-position wheel, and steering angle and sense excessive vertical motion and finally send this information to controller (ECU). The controller collects analyses and processes the data quickly in about 10 milliseconds. ECU sends a vital message to the servo coil spring. Following this an oil pump sends extra fluid to the servo and this process will increase spring tension, and the result will be decreasing Yaw, Body roll, Spring oscillation (Zaremba et al., 1997). A number of researches apply pre-control to command dynamic parts and increase the suspension efficiency (Morita et al., 1992). The laser beams can scan the road to provide a flexible and comfort car with perfectly responsive ride. The active PRE-SCAN suspension system reduces at least half of the shock and vibration because of sharp bumps or speed bumps before it ever effects on the cabin and dissipates noise (Jeong et al., 1990). One of the important tasks of suspension system is vehicle rollover prevention. The purpose of rollover prevention is to keep away from particular kind of accidents and to make the contact between tire and road surface optimal therefore improvement of vehicle handling (Schofield et al., 2006). Linear matrix inequalities used for multi-objective control for vehicle active suspension systems by proposing a load-dependent controller design approach. This method is then employed for a quarter-car model with active suspension system. One novel aspect of their research is designing controllers that gain matrix from the online available information that can be extracted from body mass using parameter-dependent Lyapunov function which help providing less conservative results comparing with previous approaches (Gao et al., 2006). Using fast tracking algorithms to import data from environment and analyze them is critical for scheduling controller system Delgoshaei et al. (2014). It is suggested a constrained control scheme for active suspensions with output and control constraints. The performance is used to measure ride comfort so that more general road disturbances can be considered. Time-domain constraints, representing requirements for: 1) good road holding which may have an impact on safety; 2) suspension stroke limitation; and 3) avoidance of actuator saturation, are captured using the concept of reachable sets and state–space ellipsoids. The proposed approach can potentially achieve the best possible ride comfort by allowing constrained variables free as long as they remain within given bounds. A state feedback solution to the constrained active suspension control problem is derived in the framework of linear matrix inequality (LMI) optimization and multi-objective control. Analysis and simulation results for a two-degree-of-freedom (2-DOF) quarter-car model show possible improvements on ride comfort, while respecting time-domain A. Rezanoori et al. / Engineering Solid Mechanics 7 (2019) 317 hard constraints (Chen et al., 2007). It is dealt with the problem of controlling active vehicle suspension systems in finite frequency domain which is useful for measuring the performance of ride comfort. They controlled the norm disturbance output using generalized Kalman–Yakubovich–Popov lemma (GKYPL), which is useful to improve the ride comfort. They found that entire frequency approach provide better vibration control comparing with finite frequency approach (Sun et al., 2010). To address a reliable fuzzy H∞ controller design for active suspension systems a Takagi-Sugeno (T-S) fuzzy model is used by focusing on sprung and unsprung mass variation, the actuator delay and fault and some other suspension performances. A quarter-car suspension model is also proposed by Li et al. (2011) to check the performance of the proposed method. They focused to robust sampled data H ∞ control for active vehicle suspension systems in a quarter car model. For this purpose, they employed an input-delay approach to transform the active vehicle suspension system into a delay continuous-time system. Gao et al. (2009) proposed a transferring method contains non-differentiable time-varying state delay and polytypic parameter uncertainties. Li et al. (2012) addressed an adaptive sliding-mode control problem for nonlinear active suspension systems considering varying sprung and unsprung masses, unknown actuator nonlinearity and suspension performances. To control the developed problem they proposed Takagi-Sugeno (T-S) fuzzy approach to describe the original nonlinear system using a nonlinearity sector. A spatial vehicle model is designed by Demić et al. (2006) which worked without filtered feedback of the control system to improve active suspension system. One significant aspect of their research was using stochastic parameters optimization of active suspension system. Such idea helped them to minimize sprung mass vibration and standard deviation of forces in vehicle handling and tire contact area. Computational-intelligence is reviewed involved approaches in active vehicle suspension control systems and also state of the art in fuzzy inference systems, neural networks, genetic algorithms (Cao et al., 2008). A polynomial model is proposed by Du et al. (2005) to determine the characters of a dynamic response in magneto-rheological (MR) damper. They showed that the proposed mechanism can realize the desired output in the open-loop control scheme. In addition, a static output feedback H∞ controller is designed to utilize measurable suspension deflection and sprung mass velocity as feedback signals for active vehicle suspension. A road-adaptive nonlinear control system is addressed by Huang et al. (2010) which is integrated with active suspensions. The proposed system continuously monitors suspension travel and adjusts the shape of the filter in a nonlinear manner to response the different road profiles. Zin et al. (2006) proposed an active suspension control mechanism to global chassis control using an adaptive 2 degrees of freedom gain-scheduled controller according to LPV/Hinfin theory. The method is proposed to increase both safety of comfort of the passengers. Some scientist focused on their ability to provide good road handling and increased passenger comfort as main criteria of designing a good vehicle suspension. Then, a fuzzy and adaptive fuzzy control is proposed by Sharkawy (2005) for automobile active suspension system. They found that active suspension control systems reduces undesirable effects by isolating car body motion from vibrations at the wheels that. An artificial intelligence Neuro-Fuzzy (NF) technique is proposed to design a robust controller for vehicle suspension system to reduce passenger’s discomfort and increasing handling of vehicle. Aldair et al. (2011) showed that the proposed mechanism has faster reaction to road vibration than other controllers by supplying control forces to suspension system when travelling on rough road. A novel energy-regenerative active suspension is proposed by Zheng et al. (2008) to regenerate electric power from the vibration that are generated by road unevenness. In continue a novel active system was designed to show the performance in ride comfort. It is discussed about the conflictions between and suspension deflection performances and ride comfort during the vibration control. In their research a non-linear model including L2 control of an active suspension system, which contains non-linear spring and damper elements is presented. The design method is based on the linear parameter varying model of the system. Their results show that the proposed method can increase bilinear damping characteristic and stiffening spring characteristic (Onat et al., 318 2009). Some researchers focused on designing an active car suspension that is working by a linear controller for improving the ride quality while maintaining good handling characteristics in confronting with road disturbance. The proposed method is then compared with robust H∞ controller, LQR controller and Fuzzy control (Kaleemullah et al., 2011). A robust controller for prevent confronting with rollover is designed to minimize lateral acceleration and roll angle. Yim (2012) argued that performance of the controllers can be improved if device is robust to the variation of the height of the center of gravity and the speed of the vehicle. Fuzzy logic is used to continuously control damping automotive suspension system. For this purpose Salem and Aly (2009) designed a quarter-car 2 degree-of-freedom system for four-wheel independent suspension systems. The aim is to support the vehicle body and increase ride comfort. An electromechanical wheel active suspension system is presented. Jonasson et al. (2008) used genetic algorithm for designing involving the control of the electric damper and its machine parameters. The results indicate that the proposed suspension can easily adopt its control parameters to obtain a better compromise of performance than passive methods. Delgoshaei et al. (2017) proposed a supervisod method to rapid analyzing the different types of input information. An adaptive backstepping controller is designed by Sun et al. (2012a) for active suspension method in the presence of parameter uncertainties to stabilize the attitude of vehicle and also improving ride comfort. A vibration control in vehicle active suspension systems is designed in the presence of parameter uncertainties where the aim is to stabilize the attitude of the vehicle and improve ride comfort. To solve the problem, a saturated adaptive robust control strategy is proposed (Sun et al., 2012b). It is argued that direct transcription problem dimension is often large, sparse problem structures and fine-grained parallelism. Therefore Allison et al. (2014) offered a new technique for combined physical and control system design. The proposed mechanism works based on a simultaneous dynamic optimization approach known as direct transcription, which transforms infinite dimensional control design problems into finite-dimensional nonlinear programming problems. Probabilistic metrics is considered for designing a robust Pareto multi-objective optimum vehicle vibration model. Simulating the system using genetic algorithm can help to analyze the system more effectively Delgoshaei et al. (2015). To solve the model a hybrid of multi-objective genetic algorithm and Monte Carlo simulation (Jamali et al., 2013). It is focused on the problem of vibration isolation for vehicle active suspension systems in the presence of uncertainties, external disturbances, actuator saturation, and performance constraints. To solve the problem Sun et al. (2014) offered an adaptive robust control technology to stabilize the attitude of vehicle in the presence of parameter uncertainties and external disturbances and covering actuator saturation and performance constraints.An assessment method is proposed by Zuo et al. (2013) for the power of vehicle suspension system. Then, the excitation from road irregularity is modeled by considering the concept of system H2 norm which is helpful for obtaining ride quality and road handling. It is focused on impacts of traffic conditions on active suspension energy regeneration for hybrid electric vehicles. For this purpose, Montazeri-Gh et al. (2012) designed a fuzzy-based active suspension system which is integrated with a combined battery-ultra capacitor energy storage system. Besides, the authors have also proposed an electromechanical mechanism for the active suspension energy regeneration, and the actuator dynamics and this mechanism's interactions with the ESS are modeled. Priyandoko et al. (2009) proposed a hybrid control technique applied to a vehicle active suspension system which is installed on a quarter-car model using skyhook and adaptive neuro active force control. The proposed mechanism consisted on 4 control systems which were innermost proportional-integral control; intermediate skyhook and active force control and outermost proportional– integral–derivative. To solve the experiments they used an adaptive neural network algorithm. H. Chen et al. (2002) designed a control scheme for active suspensions with output and control constraints. The proposed mechanism which is developed to measure ride comfort so that more general road disturbances can be considered is subjected to 2 main constraints which were good road holding that has an impact on safety and also suspension stroke limitation. The active suspension control system is worked based on LMI optimization and multi objective control. It is focused on the problem of output-feedback H∞ control for in an active suspension system. Their mechanism is installed on a quarter-car in order to increase ride comfort, road holding, suspension deflection, and maximum actuator control force. For this purpose they A. Rezanoori et al. / Engineering Solid Mechanics 7 (2019) 319 used Lyapunov theory and LMI approaches to formulate an admissible controllers (Li et al., 2013a). Li et al. (2013b) used Fuzzy control for dealing with the problem of sampled-data H∞ control in uncertain active suspension systems. Their method works based on state-feedback and output-feedback sampleddata controllers which helps a closed-loop dynamical systems to be more steady. They proposed 2 adaptive controls for active suspension systems in the presence of nonlinear dynamic conditions. Then Huang et al. (2015) developed a prescribed performance function to evaluate the transient and steadystate of the suspension system performance. Tables 3-5 sumarize some the features of the researches. Table 3 Comparing opted researches, their advantages and disadvantages Method Semi-active Suspension Features/ Advantage/ Disadvantages More Trustable/ Used more than other suspension systems/ can be modelled and solved by fuzzy systems/ Better results/ More realistic/ More complicated Used less than active suspension/ Not complicated/ less accuracy/ Simple Mechanism/ Passive Suspension Used less than active suspension/ Not complicated/ less accuracy Active Suspension Table 4 Details of Methods Used in Opted References of Research Contribution Row References Solution Offered Heuristics CRP Y Year Active 1 Aldair & Wang 2011 √ 2 Allison et al. 2014 √ 3 Allotta et al. 2008 4 Amirifar & Sadati 2006 Semi Active Passive √ √ adaptive fuzzy control √ √ √ √ LMI √ Fuzzy Chen et al. 2007 √ √ C.-J. Huang et al. 2010 √ √ Canale et al. 2006 Cao et al. 2008 √ √ √ 9 Demić et al. 2006 √ √ √ MPC 10 Du et al. 2005 √ √ √ Lyapunov 11 Gao et al. 2006 √ √ √ stochastic optimization 12 Gao et al. 2010 √ √ √ LPV/Hinfin √ √ LMI √ √ LQR √ Evolutionary Algorithm √ √ Genetic √ LMI Georgiou et al. 2007 14 Guglielmino et al. 2008 15 Chen et al. 2005 √ 16 Li et al. 2013 √ √ √ Hanafi 2010 18 Hong Chen & Guo 2005 Jamali et al. 2013 √ √ √ 19 20 Jonasson & Roos 2008 √ √ 21 Kaleemullah et al. 2011 √ 22 Kou & Fang 2007 √ 23 L. Sun et al. 2007 24 Li et al. 2012 MS MITM Crisp Miscellaneous √ √ √ Fuzzy Partitioning Minimize Maximize Similarity MD Dissimilarities Minimize Inter-cellular Material Movements √ LPV √ √ LMI √ √ Fuzzy √ √ Genetic √ √ √ FUZ P √ √ 17 CRP M 1/4 Car √ √ √ 13 1/2 Car √ 6 8 N Simulation √ 5 7 FUZ Employed/Designed Method H A Hierarchical Array-based NH MH Non-hierarchical Metaheuristics MDS Minimizing Distance MV Minimizing Voids 320 Table 5 Details of Methods Used in Opted References of Research (continued) Row Contribution References Solution Offered Heuristics CRP FUZ Y √ LMI √ √ Fuzzy ANN, Genetic Year Active 25 Li, Jing, & Karimi 2014 26 Li, Jing, Lam, et al. 2014 27 Lin et al. 28 Martins et al. 29 Semi Active Passive √ √ 2006 √ √ √ 2006 √ √ Montazeri-Gh et al. 2013 √ 30 Onat et al. 2009 √ √ 31 P. Chen & Huang 2005 √ √ 32 Poussot-Vassal et al. 2007 √ √ 33 Poussot-Vassal et al. 2008 √ √ 34 Poussot-Vassal et al. 2012 35 Priyandoko et al. 2009 36 Salem & Aly 2009 37 Savaresi et al. 2010 √ √ √ Genetic √ Fuzzy Logic √ √ √ √ √ N Employed/Designed Method √ Simulation 1/2 Car 1/4 Car √ √ NARX Neuro Active Control √ √ √ 38 Segla & Reich 2007 39 Sharkawy 2005 √ √ 40 Shirahatti et al. 2008 41 Stribrsky et al. 2007 42 Verros et al. 2005 43 W. Sun et al. 2011 √ √ 44 W. Sun et al. 2015 √ 45 W. Sun, Gao, et al. 2013 √ 46 W. Sun, Zhao, et al. 2013 √ √ 47 Wu et al. 2005 √ 48 Xuechun 2005 √ √ 49 Y. Huang et al. 2015 50 Yim 2012 √ √ √ 51 Z. Liu et al. 2006 √ √ 52 Zheng et al. 2008 √ √ 53 Zin et al. 2006 √ √ 54 Zuo & Zhang 2013 √ √ √ √ √ √ √ √ GKYPL √ NF √ √ Fuzzy √ √ T-S fuzzy √ √ T-S fuzzy √ √ adaptive robust control √ saturated adaptive robust Genetic √ √ √ √ √ 2 adaptive controls To the best knowledge of us, using electronic sensors for proactive rapid reactions by readjusting the hight of chasis while confronting with wrong conditions such as high speed, rainy or snowy road profile, sharp turns and short distance between cars are less developed for active suspension systems. 2.1 Analytical Comparison A review of the selected studies shows that in 75.9% of the investigated cases used active suspension systems, 9.26% selected semi-active suspension system and 9.26% developed passive methods. More than 20% used fuzzy concepts while 9.25% preferred neural networks. Almost 1.85% of researchers used half car simulator while 11.1% used quarter car simulator. Table 6 Presents a brief over statistical comparison between opted researches. A. Rezanoori et al. / Engineering Solid Mechanics 7 (2019) 321 Table 6 Statistical Comparison of Opted Researches Contribution Active (75.9%) Semi-active (9.26%) Passive (9.26%) Model Type Fuzzy (20%) Crisp (79.6%) Advanced Computation Heuristics (48.15%) 3. Research Methodology 3.1 Designing the proposed active suspension system In this section different parts of the model will be drawn by AutoCAD first. Afterward each of the sensors, modules and other parts will be selected and their function in the model explained. Then the model will be simulated by Matlab 1000 times and if the results of the proposed model seems good, then a prototype will be manufactured and afterward this model will be run for 100 times. The outcomes are then analyzed using statistical formulas. 3.2 Mechanism of the model This model is an active suspension mechanism which helps readjusting chasiss in order to increase the passengers safety and coformt. For this purpose, the function of the this active suspension model is set to increase car stability during raining or snowing profiles. For this purpose a mechanism is required to recognize the rain or snow and readjust the chassis vertically in order to decrease the hight of the vehicle which resulted in more stability. The other function of the system is minimizing the vehichle shakes while driving it on an unflatted road profile. For this function the model must have a mechanism to recognize the vertical and horizontal positions on a road profile and command the shock absorbers to readjust the chasiss. 3.3 Drawings Of The Model 3.3.1 Designing Parts In this section the proposed model is design by AutoCAD software. A 3D graphical view of the model is shown in Fig. 3 to Fig. 5. Fig. 2. A side view drawing of the model Fig. 3. A front view drawing of the model Fig. 4. A 3D view drawing of the model In continue the drawings of some parts are shown in 3 side views and the 3D view of the parts are shown by Fig. 6 to Fig. 11. 322 Fig. 5. A Camshaft Road profile Simulator Fig. 6. Main holder force pressure sensor with Syringe Fig. 7. Force pressure sensor holder2 Drawing Fig. 8. Force pressure sensor holder 1 with Syringe Drawing Fig. 9. Holder Wheel String Drawing Fig. 10. Bumper 2 Drawing 3.3.2 Sensors Table 7 shows the list of sensors that will be used in model. Table 7 List of sensors that will be used in the model Sensors and Modules Snow/Rain Detector Digital Temperature and Humidity Sensor module Ultrasonic Module 10DOF Nine Axis IMU Module Force Sensor FSR406 Infrared Correlation photoelectric sensor AB phase Incremental Rotary Encoder Application Recognizing rain and snow measuring humidity and temperature measuring distance measuring in 9 degree freedom measuring the pressure Type 1 DHT22 1 HC-SR04 L3G4200D+ADXL345+HMC5883L +BMP085 FSR406 1 measuring position measuring orbital position In continue each of the sensors will be explained briefly. Quantity in model 1 2 14 AB phase Encoder 3