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A Novel Infrastructure Design of Industrial Autonomous System

In this era, the integrated technologies in a unit bring huge benefits for customers. In this paper, a novel design of structure for autonomous system is illustrated. It is superior by combining the specifications of various types in commercial market. As a result, numerous advantages of systems are converged into unified unit. The design of autonomous platform consists of hardware components and infrastructure. Later, the modeling of autonomous system is simulated on computer to fit with proposed design. The implementation of control scheme into model is to drive autonomous system to complete mission. From simulation results, it can be seen that the proposed strategy is feasible, effective and capable in reality.


Introduction
In every production site, the occupations for loading, unloading and transporting of cargoes or materials are always to use much labor resource. These jobs are not complex, repeated actions and lack of intelligence. Sometimes, worker feels boring or emotion-based productivity. The managers worry about their work-force in each quarter of year. While the high operation cost reduces the competitive ability of company, the faster processing of bill will meet customer satisfaction. The automatic solution should be considered in these situations. The autonomous system is automated transporters that have been developed to support the industrial enterprises gain high productivity with minimum cost. The automated solution could be adjusted size of system due to needs, effective execution, adaptable producing schedule or scalable model. In last decades, there are large numbers of accomplished investigation to use autonomous system in industry. Most of them focus on designing automated vehicle which plays an important role in autonomous system. Depending on each specific application, the core of system could be divided into many kinds of sub-vehicles.
For automated warehouse management, several famous models have been declared firstly. In [1,2], the area of storehouse is divided into many sections such as charging station, delivering station, importing station or location of storing cargo. The man operators are at delivering station or importing station while autonomous vehicles carry shelves inside. In large distribution center, this model is more productive than traditional methods. The path planning for these applications in presence of obstacles could be A* based on vision [3,4] or Dijkastra [5] shortest path algorithm. However, this type is appropriate for small freights in shelves, logistics system and lacks of automatic feeder actuators.
In the other of load transportation applications which involve heavy weight or feeder component, more than one shelf, it is usually to extend the functionalities of traditional autonomous system. The towing-vehicle such as truck or forklift [6] gains more advantages. In [7], the module-based system permits to modify the number of robots and pallets to lessen operating cost and consuming time. The software architecture is flexible to add more modules while it still keeps the system's structure. Researchers in [8,9] present a robotic forklift for intelligent warehouse. Three algorithms are implemented in forklift, routing algorithm, local path planning algorithm and an auto-localization algorithm. However, this research can meet serious problems in real time control due to the order of path planning steps before sending to robot. Also, it has not validated in experimental multi robotic forklifts scenario.
Recently, the new trend of integrated autonomous vehicle occurs in most of industries. The automated devices work in logistics applications while it still maintains the capable operation in material delivering applications. To gratify the requirements of customers, the developers must integrate various functions into one unit though the benefits of physical dimension, light weight and cheap price are kept. In this paper, the novel design of industrial autonomous system with multifunction is investigated. The system parameters such as size, load capacity, operation mode, communication or tracking method ensure the usage in various fields. Section 2 consists of the description of system and specifications. The hardware structure of autonomous system is illustrated in Section 3. The content of modeling and simulation is described in following part, Section 4. Several results of proposed design in Section 5 are demonstrated to verify in industries. Finally, conclusions and future works to develop are carried out.

System Parameters and Specifications
In this system, it is necessary to provide a powerful management to process all of peripheral devices. In Figure 1, the connection diagram between micro-processor and other components is illustrated. The high-performance ARM Cortex M4 32-bit operating at max frequency of 168 MHz is considered as core of CPU module. This embedded processor features a floating-International Journal of Fuzzy Logic and Intelligent Systems, vol. 13, no. 1, March 2013 For automated warehouse management, several famous models have been declared firstly. In [1,2], the area of storehouse is divided into many sections such as charging station, delivering station, importing station or location of storing cargo. The man operators are at delivering station or importing station while autonomous vehicles carry shelves inside. In large distribution center, this model is more productive than traditional methods. The path planning for these applications in presence of obstacles could be A* based on vision [3][4][5] or Dijkastra [6] shortest path algorithm. However, this type is appropriate for small freights in shelves, logistics system and lacks of automatic feeder actuators.
In the other of load transportation applications which involve heavy weight or feeder component, more than one shelf, it is usually to extend the functionalities of traditional autonomous system. The towing-vehicle such as truck or forklift [7] gains more advantages. In [8], the module-based system permits to modify the number of robots and pallets to lessen operating cost and consuming time. The software architecture is flexible to add more modules while it still keeps the systems structure. Researchers in [9, 10] present a robotic forklift for intelligent warehouse. Three algorithms are implemented in forklift, routing algorithm, local path planning algorithm and an auto-localization algorithm. However, this research can meet serious problems in real time control due to the order of path planning steps before sending to robot. Also, it has not validated in experimental multi robotic forklifts scenario.
Recently, the new trend of integrated autonomous vehicle occurs in most of industries. The automated devices work in logistics applications while it still maintains the capable operation in material delivering applications. To gratify the requirements of customers, the developers must integrate various functions into one unit though the benefits of physical dimension, light weight and cheap price are kept. In this paper, the novel design of industrial autonomous system with multifunction is investigated. The system parameters such as size, load capacity, operation mode, communication or tracking method ensure the usage in various fields. Section II consists of the description of system and specifications. The hardware structure of autonomous system is illustrated in Section III. The content of modeling and simulation is described in following part, Section IV. Several results of proposed design in Section V are demonstrated to verify in industries. Finally, conclusions and future works to develop are carried out.

System Parameters and Specifications
In this system, it is necessary to provide a powerful management to process all of peripheral devices. In Fig. 1, the connection diagram between micro-processor and other components is illustrated. The high-performance ARM Cortex M4 32-bit operating at max frequency of 168MHz is considered as core of CPU module. This embedded processor features a Floating-point unit (FPU) single precision which supports all single-precision data processing instructions. Some key specifications such as 1 Mbyte Flash, 192 Kbytes SRAM, 3 channels A/D 12bit, 2 channels D/A 12-bit offer a wide range of applications. The two DC servo motors with power 60W and max speed 70 rpm are able to drive whole system. Due to the different speeds among servo motors, the autonomous vehicle can move easily and conveniently. There are several ways to track the reference trajectory. Generally speaking, using line following sensor or magnetic tracking sensor are two popular methods. To enhance the competition of this system in market, line following sensor becomes the best solution to implement. The micro-controller can control electric cylinder to lift up or down cargo with max load 200kg. Moreover, this robot is equipped with electric lock which is capable to hold a powerful electromagnetic force to drive followers. Additionally, the human operator must stand at start point and target location to pick up freight. The operating cost will be increased in traditional system. In this design, mobile robot will be run from beginning to ending steps lonely. Thanks to roller actuators, the automated ability of robot can be improved.
The system coefficients and operating information are listed as Table 1. The physical size is large enough to carry various loads while it still maintains the flexible execution. In some cases, the suitable height of this vehicle helps its move underneath the shelves. Hence, it can work anywhere in map regarding to dimension. In industry, the working route consists of two kinds: uni-direction and bi-direction. To deal with   numerous maps, in this work, the autonomous vehicle should move in forward and reverse path. Furthermore, in some specific situations such as material transportation in clothing or textile industry, robot extricates one or more shelves. As a result, the power of motor and tires of wheels are studied to optimize selection.

Description of Proposed Design
In Fig. 2, the whole vehicle is shown to overview the structure. Basically, robot can be divided into four sections: base layer, middle layer, feeder layer and lifting layer. Each layer plays own role such that base layer is strong enough to bear the body mass and load. The linear slider leans on middle layer to orientate vertical direction while the feeder layer hangs rubber rollers to provide cargo automatically. Top layer or lifting layer is elevated by electric cylinder and directly contacts to cargo.
The driving mechanism consists of two driving wheels on side, castor-type wheels in front and back. In Fig. 3, this structure avoids the slipping phenomenon, guarantees flexible motion and all wheels in contact with a certain surface. Two powerful motors are connected with shafts of wheels via gear boxes. The directional movement depends on the difference  between speed of left wheel and right wheel. The following path sensors are located at center of bottom layer. In both sides of forward and backward, it provides bi-directional driving ability in map. Besides, RFID reader module on this layer helps to navigate via tags which are placed at each intersection. By reaching a crossroad, vehicle reads identification numbers of tags and returns to host computer. Therefore, server could track the following path and generate the proper trajectory.
The structure of ground vehicle is demonstrated in Fig. 4. The electric cylinder mainly provides lifting force to elevate freight while four slider actuators linearly preserve vertical movement. Under the electric piston, there is a load-cell to evaluate the weight level of goods. As a result, the automated vehicle is able to carry out proper control signal smoothly. Furthermore, it is enhanced the advanced functions via the feeding mechanism. Whenever mobile vehicle stops at destination, without supporters, it automatically delivers freight to storage. In both of head and tail, the electromagnets are attached to lead the behind carriages. In this case, the overall vehicle-load plays a role as tractor-trailer system.

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Research on Novel Infrastructure Design of Industrial Autonomous System | 52 Figure 2. Overview of proposed autonomous vehicle.
The system coefficients and operating information are listed as Table 1. The physical size is large enough to carry various loads while it still maintains the flexible execution. In some cases, the suitable height of this vehicle helps its move underneath the shelves. Hence, it can work anywhere in map regarding to dimension. In industry, the working route consists of two kinds: uni-direction and bi-direction. To deal with numerous maps, in this work, the autonomous vehicle should move in forward and reverse path. Furthermore, in some specific situations such as material transportation in clothing or textile industry, robot extricates one or more shelves. As a result, the power of motor and tires of wheels are studied to optimize selection.

Description of Proposed Design
In Figure 2, the whole vehicle is shown to overview the structure. Basically, robot can be divided into four sections: base layer, middle layer, feeder layer and lifting layer. Each layer plays own role such that base layer is strong enough to bear the body mass and load. The linear slider leans on middle layer to orientate vertical direction while the feeder layer hangs rubber rollers to provide cargo automatically. Top layer or lifting layer is elevated by electric cylinder and directly contacts to cargo.
The driving mechanism consists of two driving wheels on side, castor-type wheels in front and back. In Figure 3, this structure avoids the slipping phenomenon, guarantees flexible motion and all wheels in contact with a certain surface. Two powerful motors are connected with shafts of wheels via gear boxes. The directional movement depends on the difference between speed of left wheel and right wheel. The following path sensors are located at center of bottom layer. In both sides of forward and backward, it provides bi-directional driving ability in map. Besides, RFID reader module on this layer helps to navigate via tags which are placed at each intersection. By aps, in this work, the autonomous vehicle should ward and reverse path. Furthermore, in some speons such as material transportation in clothing or stry, robot extricates one or more shelves. As a ower of motor and tires of wheels are studied to lection.

ription of Proposed Design
, the whole vehicle is shown to overview the strucally, robot can be divided into four sections: base le layer, feeder layer and lifting layer. Each layer ole such that base layer is strong enough to bear the and load. The linear slider leans on middle layer to rtical direction while the feeder layer hangs rubber ovide cargo automatically. Top layer or lifting layer  between speed of left wheel and right wheel. The following path sensors are located at center of bottom layer. In both sides of forward and backward, it provides bi-directional driving ability in map. Besides, RFID reader module on this layer helps to navigate via tags which are placed at each intersection. By reaching a crossroad, vehicle reads identification numbers of tags and returns to host computer. Therefore, server could track the following path and generate the proper trajectory.
The structure of ground vehicle is demonstrated in Fig. 4. The electric cylinder mainly provides lifting force to elevate freight while four slider actuators linearly preserve vertical movement. Under the electric piston, there is a load-cell to evaluate the weight level of goods. As a result, the automated vehicle is able to carry out proper control signal smoothly. Furthermore, it is enhanced the advanced functions via the feeding mechanism. Whenever mobile vehicle stops at destination, (a)  rous maps, in this work, the autonomous vehicle should in forward and reverse path. Furthermore, in some speituations such as material transportation in clothing or industry, robot extricates one or more shelves. As a , the power of motor and tires of wheels are studied to ize selection.

Description of Proposed Design
Fig. 2, the whole vehicle is shown to overview the struc-Basically, robot can be divided into four sections: base middle layer, feeder layer and lifting layer. Each layer own role such that base layer is strong enough to bear the mass and load. The linear slider leans on middle layer to ate vertical direction while the feeder layer hangs rubber s to provide cargo automatically. Top layer or lifting layer ated by electric cylinder and directly contacts to cargo. e driving mechanism consists of two driving wheels on castor-type wheels in front and back. In Fig. 3, this  between speed of left wheel and right wheel. The following path sensors are located at center of bottom layer. In both sides of forward and backward, it provides bi-directional driving ability in map. Besides, RFID reader module on this layer helps to navigate via tags which are placed at each intersection. By reaching a crossroad, vehicle reads identification numbers of tags and returns to host computer. Therefore, server could track the following path and generate the proper trajectory.
The structure of ground vehicle is demonstrated in Fig. 4. The electric cylinder mainly provides lifting force to elevate freight while four slider actuators linearly preserve vertical movement. Under the electric piston, there is a load-cell to evaluate the weight level of goods. As a result, the automated vehicle is able to carry out proper control signal smoothly. Furthermore, it is enhanced the advanced functions via the feeding mechanism. Whenever mobile vehicle stops at destination, without supporters, it automatically delivers freight to storage. In both of head and tail, the electromagnets are attached to lead the behind carriages. In this case, the overall vehicle-load plays   numerous maps, in this work, the autonomous vehicle should move in forward and reverse path. Furthermore, in some specific situations such as material transportation in clothing or textile industry, robot extricates one or more shelves. As a result, the power of motor and tires of wheels are studied to optimize selection.

Description of Proposed Design
In Fig. 2, the whole vehicle is shown to overview the structure. Basically, robot can be divided into four sections: base layer, middle layer, feeder layer and lifting layer. Each layer plays own role such that base layer is strong enough to bear the body mass and load. The linear slider leans on middle layer to orientate vertical direction while the feeder layer hangs rubber rollers to provide cargo automatically. Top layer or lifting layer is elevated by electric cylinder and directly contacts to cargo.
The driving mechanism consists of two driving wheels on side, castor-type wheels in front and back. In Fig. 3, this structure avoids the slipping phenomenon, guarantees flexible motion and all wheels in contact with a certain surface. Two powerful motors are connected with shafts of wheels via gear  between speed of left wheel and right wheel. The following path sensors are located at center of bottom layer. In both sides of forward and backward, it provides bi-directional driving ability in map. Besides, RFID reader module on this layer helps to navigate via tags which are placed at each intersection. By reaching a crossroad, vehicle reads identification numbers of tags and returns to host computer. Therefore, server could track the following path and generate the proper trajectory.
The structure of ground vehicle is demonstrated in Fig. 4. The electric cylinder mainly provides lifting force to elevate freight while four slider actuators linearly preserve vertical movement. Under the electric piston, there is a load-cell to evaluate the weight level of goods. As a result, the automated vehicle is able to carry out proper control signal smoothly. Furthermore, it is enhanced the advanced functions via the feeding mechanism. Whenever mobile vehicle stops at destination, without supporters, it automatically delivers freight to storage. In both of head and tail, the electromagnets are attached to lead the behind carriages. In this case, the overall vehicle-load plays a role as tractor-trailer system. reaching a crossroad, vehicle reads identification numbers of tags and returns to host computer. Therefore, server could track the following path and generate the proper trajectory.
The structure of ground vehicle is demonstrated in Figure 4. The electric cylinder mainly provides lifting force to elevate freight while four slider actuators linearly preserve vertical movement. Under the electric piston, there is a load-cell to evaluate the weight level of goods. As a result, the automated vehicle is able to carry out proper control signal smoothly. Furthermore, it is enhanced the advanced functions via the feeding mechanism. Whenever mobile vehicle stops at destination, without supporters, it automatically delivers freight to storage. In both of head and tail, the electromagnets are attached to lead the behind carriages. In this case, the overall vehicle-load plays a role as tractor-trailer system.

Modeling of Proposed System
To figure the performance of proposed system, the kinematic modeling in Figure 5 describes the geometrical relationship of parameters. We consider that geometric center C and the

Modeling of Proposed System
To figure the performance of proposed system, the kinematic modeling in Fig. 5 describes the geometrical relationship of parameters. We consider that geometric center C and the center of gravity are coincided. q = x, y, θ T symbolize the coordination of vehicle in workspace. where • θ : an angle between vertical axially length and Ox 0 .
• θ P : an angle between Ox 0 and tangent.
• x P , y P : desired coordinate of CoG.
• x C , y C : coordinate of CoG.
The kinematic equation of autonomous vehicle at center point C, CoG and point P of trajectory are illustrated correspondingly.
where • w P : desired angular velocity at reference point.
• d : distance between center point of vehicle and midd point of driving wheels.
The error modeling e = e 1 e 2 e 3 T is defined as th alteration between CoG and desired point on reference path.
Take the first derivative order of equation (4), we have Therefore, the stable controller is proposed for this design o autonomous vehicle. v = v P cose 3 + k 1 (e 1 + a) w = k 2 e 2 v P + w P + k 3 v P sine 3 (6 In the sense of Lyapunov stability, the function candida V should be positive definite and continuous in domain. A equilibrium point, the function V must be zero. Consider Lyapunov function candidate as following.
With respect to time, take the derivative (7), we obtaiṅ • L: distance between wheels.
• θ P : an angle between Ox 0 and tangent.
• x P , y P : desired coordinate of CoG.
• x C , y C : coordinate of CoG.
The kinematic equation of autonomous vehicle at center point C, CoG and point P of trajectory are illustrated correspondingly. where • v : linear velocity of mobile platform.
• w : angular velocity of mobile platform.
• v P : desired linear velocity at reference point.
• w P : desired angular velocity at reference point.
• d : distance between center point of vehicle and middle point of driving wheels.
The error modeling e = e 1 e 2 e 3 T is defined as the alteration between CoG and desired point on reference path.
Take the first derivative order of Eq. (4), we have Therefore, the stable controller is proposed for this design of autonomous vehicle.
In the sense of Lyapunov stability, the function candidate V should be positive definite and continuous in domain. At equilibrium point, the function V must be zero. Consider a Lyapunov function candidate as following: With condition ∀k 2 > 0 , if e = e 1 e 2 e 3 T equals to zero, then V = 0. Otherwise e = 0 , then V > 0. With respect to time, take the derivative (7), we obtaiṅ Using stabilizing controller from Eq. (6), we havė It can be seen clearly thatV 0, ∀k 1 , k 2 , k 3 > 0. The tracking error converges to zero asymptotically when v R > 0.   Using stabilizing controller from equation (6), we havė It can be seen clearly thatV 0, ∀k 1 , k 2 , k 3 > 0. The tracking error converges to zero asymptotically when v R > 0. The velocity control input in (6) could guarantee the system is asymptotically stable as time goes to infinity when d is zero. If vehicle stays in parallel with reference trajectory, then e 1 = e 3 = 0 and e 2 = 0. At that time, equation (6) of velocity control might not force e 2 to zero. end point. However, the autonomous vehicle would round from start point to evaluate performance o situations. In reality, there are unique directiona bidirectional paths depending on applications. Th design is able to work in both cases. The tracking si trajectory is shown as Fig. 9 dividing into sub-sec as A-B-C, C-D-E-F, F-CG and C-G-A. In each sect tracks black line while it returns signals (tracking err current status and so on) to host computer. RFID at every point to navigate current coordination. Th selected based on experimental operator but satisfyi value, k 1 = 1.3, k 2 = 80, k 3 = 1.8. As a result condition is ensured by Lyapunov approach.    Using stabilizing controller from equation (6), we havė It can be seen clearly thatV 0, ∀k 1 , k 2 , k 3 > 0. The tracking error converges to zero asymptotically when v R > 0. The velocity control input in (6) could guarantee the system is asymptotically stable as time goes to infinity when d is zero. If vehicle stays in parallel with reference trajectory, then e 1 = e 3 = 0 and e 2 = 0. At that time, equation (6) of velocity control might not force e 2 to zero.
In practical model, it is necessary to gain the tracking error e end point. However, the autonomous vehicle would round from start point to evaluate performance o situations. In reality, there are unique directiona bidirectional paths depending on applications. Th design is able to work in both cases. The tracking si trajectory is shown as Fig. 9 dividing into sub-sec as A-B-C, C-D-E-F, F-CG and C-G-A. In each sect tracks black line while it returns signals (tracking err current status and so on) to host computer. RFID at every point to navigate current coordination. Th selected based on experimental operator but satisfy value, k 1 = 1.3, k 2 = 80, k 3 = 1.8. As a result condition is ensured by Lyapunov approach.  The velocity control input in (6) could guarantee the system is asymptotically stable as time goes to infinity when d is zero. If vehicle stays in parallel with reference trajectory, then e 1 = e 3 = 0 and e 2 = 0. At that time, Eq. (6) of velocity control might not force e 2 to zero.
In practical model, it is necessary to gain the tracking error e to prove the effective drive. Consider that vehicle moves with reference speed, hence, e 1 = 0. The tracking sensors performs the e 2 error along normal direction with movement.
To compute the tracking error e 3 , let vehicle move a small distance d s . Assume that from point R to point R' on trajectory, it is nearly linear.

Results of Research
To validate the flexible motion and feasible control algorithm, the autonomous vehicle is trialed on working map as Figure 8.  Using stabilizing controller from equation (6), we havė It can be seen clearly thatV 0, ∀k 1 , k 2 , k 3 > 0. The tracking error converges to zero asymptotically when v R > 0. The velocity control input in (6) could guarantee the system is asymptotically stable as time goes to infinity when d is zero. If vehicle stays in parallel with reference trajectory, then e 1 = e 3 = 0 and e 2 = 0. At that time, equation (6) of velocity control might not force e 2 to zero.
In practical model, it is necessary to gain the tracking error e to prove the effective drive. Consider that vehicle moves with reference speed, hence, e 1 = 0. The tracking sensors performs the e 2 error along normal direction with movement.
To compute the tracking error e 3 , let vehicle move a small distance d s . Assume that from point R to point R on trajectory, it is nearly linear.

Results of Research
To validate the flexible motion and feasible control algorithm, the autonomous vehicle is trialed on working map as Fig. 8. There are two significant points, for example, start point and   In A-B-C section as Fig. 10, at B point, there is a sudden changing direction to fetch bigger tracking errors. Though, from A to B or from B to C, the errors fluctuate gradually as Fig. 11. Whenever vehicle is in corner, speeds of vehicle in Fig.

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Research on Novel Infrastructure Design of Industrial Autonomous System | 54  Using stabilizing controller from equation (6), we havė It can be seen clearly thatV 0, ∀k 1 , k 2 , k 3 > 0. The tracking error converges to zero asymptotically when v R > 0. The velocity control input in (6) could guarantee the system is asymptotically stable as time goes to infinity when d is zero. If vehicle stays in parallel with reference trajectory, then e 1 = e 3 = 0 and e 2 = 0. At that time, equation (6) of velocity control might not force e 2 to zero.
In practical model, it is necessary to gain the tracking error e to prove the effective drive. Consider that vehicle moves with reference speed, hence, e 1 = 0. The tracking sensors performs the e 2 error along normal direction with movement.
To compute the tracking error e 3 , let vehicle move a small distance d s . Assume that from point R to point R on trajectory, it is nearly linear.

Results of Research
To validate the flexible motion and feasible control algorithm, the autonomous vehicle is trialed on working map as Fig. 8. There are two significant points, for example, start point and round from start point to evaluate performance on different situations. In reality, there are unique directional paths and bidirectional paths depending on applications. The proposed design is able to work in both cases. The tracking simulation of trajectory is shown as Fig. 9 dividing into sub-sections, such as A-B-C, C-D-E-F, F-CG and C-G-A. In each section, vehicle tracks black line while it returns signals (tracking errors, speeds, current status and so on) to host computer. RFID tags locate at every point to navigate current coordination. The gains are selected based on experimental operator but satisfying positive value, k 1 = 1.3, k 2 = 80, k 3 = 1.8. As a result, the stable condition is ensured by Lyapunov approach.  Using stabilizing controller from equation (6), we havė It can be seen clearly thatV 0, ∀k 1 , k 2 , k 3 > 0. The cking error converges to zero asymptotically when v R > 0. e velocity control input in (6) could guarantee the system is ymptotically stable as time goes to infinity when d is zero. vehicle stays in parallel with reference trajectory, then e 1 = = 0 and e 2 = 0. At that time, equation (6) of velocity ntrol might not force e 2 to zero.
In practical model, it is necessary to gain the tracking error e prove the effective drive. Consider that vehicle moves with ference speed, hence, e 1 = 0. The tracking sensors performs e e 2 error along normal direction with movement.
To compute the tracking error e 3 , let vehicle move a small stance d s . Assume that from point R to point R on trajectory, is nearly linear.

Results of Research
To validate the flexible motion and feasible control algorithm, e autonomous vehicle is trialed on working map as Fig. 8. ere are two significant points, for example, start point and design is able to work in both cases. The tracking simulation of trajectory is shown as Fig. 9 dividing into sub-sections, such as A-B-C, C-D-E-F, F-CG and C-G-A. In each section, vehicle tracks black line while it returns signals (tracking errors, speeds, current status and so on) to host computer. RFID tags locate at every point to navigate current coordination. The gains are selected based on experimental operator but satisfying positive value, k 1 = 1.3, k 2 = 80, k 3 = 1.8. As a result, the stable condition is ensured by Lyapunov approach.  In A-B-C section as Fig. 10, at B point, there is a sudden changing direction to fetch bigger tracking errors. Though, from A to B or from B to C, the errors fluctuate gradually as Fig. 11. Whenever vehicle is in corner, speeds of vehicle in Fig.

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Research on Novel Infrastructure Design of Industrial Autonomous System | 54 There are two significant points, for example, start point and end point. However, the autonomous vehicle would follow one round from start point to evaluate performance on different situations. In reality, there are unique directional paths and bidirectional paths depending on applications. The proposed design is able to work in both cases. The tracking simulation of trajectory is shown in Figure 9 dividing into sub-sections, such as A-B-C, C-D-E-F, F-CG and C-G-A. In each section, vehicle tracks black line while it returns signals (tracking errors, speeds, current status and so on) to host computer. RFID tags locate at every point to navigate current coordination. The gains are selected based on experimental operator but satisfying positive value, k 1 = 1.3, k 2 = 80, k 3 = 1.8. As a result, the stable condition is ensured by Lyapunov approach.
In A-B-C section as Figure 10, at B point, there is a sudden changing direction to fetch bigger tracking errors. Though, from A to B or from B to C, the errors fluctuate gradually as Figure 11. Whenever vehicle is in corner, speeds of vehicle in Figure 11. Results of tracking error for ABC section on working map.
12 decrease to 1.25 m/s to maintain the errors not larger than expected. The system keeps driving rule for differential vehicle model such that velocity of right wheel must be greater than left wheel to turn left direction.  Contrarily, in E-F-C section as Fig. 16, vehicle obligates turn right at corner F. Therefore, the e2 and e3 error are peaked at that time in Fig. 17. After that, they alter slightly in bounded condition. During its execution, the system is forced to carry out a decision at point C. The host computer orders its to continue in linear trajectory. It is also confirmed in Fig. 18 where speeds of vehicle vary. If the latency time appears in communication, maybe, the worse performance could be made. Finally, autonomous vehicle passes C-G-A section as Fig. 19 before it closes a loop. In this case, the following errors in Fig. 20 continuously mutate due to driving control scheme. In Fig. 21, at each turn, the speeds of wheels are altered to pilot whereas slower velocity of vehicle helps to keep tracking.

Conclusions
In this paper, a mechanical architecture for autonomous ground vehicle is introduced to handle multi functions in practical Figure 11. Results of tracking error for ABC section on working map. Figure 11. Results of tracking error for ABC section on working map.
12 decrease to 1.25 m/s to maintain the errors not larger than expected. The system keeps driving rule for differential vehicle model such that velocity of right wheel must be greater than left wheel to turn left direction.  Contrarily, in E-F-C section as Fig. 16, vehicle obligates turn right at corner F. Therefore, the e2 and e3 error are peaked at that time in Fig. 17. After that, they alter slightly in bounded condition. During its execution, the system is forced to carry out a decision at point C. The host computer orders its to continue in linear trajectory. It is also confirmed in Fig. 18 where speeds of vehicle vary. If the latency time appears in communication, maybe, the worse performance could be made. Finally, autonomous vehicle passes C-G-A section as Fig. 19 before it closes a loop. In this case, the following errors in Fig. 20 continuously mutate due to driving control scheme. In Fig. 21, at each turn, the speeds of wheels are altered to pilot whereas slower velocity of vehicle helps to keep tracking.   igure 11. Results of tracking error for ABC section on working ap.

Conclusions
2 decrease to 1.25 m/s to maintain the errors not larger than xpected. The system keeps driving rule for differential vehicle odel such that velocity of right wheel must be greater than eft wheel to turn left direction.
igure 12. Results of speeds (red: left wheel; green: right wheel) for BC section on working map.
The same circumstance on C-D-E section as Fig. 13, the reatest errors occur at D point when vehicle is in turn. The aximum e2 error is approximately 10mm whilst e3 is about 23 degree as Fig. 14. If vehicle deviates from reference path,  several times to transpire the calibrated process.
Contrarily, in E-F-C section as Fig. 16, vehicle obligates turn right at corner F. Therefore, the e2 and e3 error are peaked at that time in Fig. 17. After that, they alter slightly in bounded condition. During its execution, the system is forced to carry out a decision at point C. The host computer orders its to continue in linear trajectory. It is also confirmed in Fig. 18 where speeds of vehicle vary. If the latency time appears in communication, maybe, the worse performance could be made. Finally, autonomous vehicle passes C-G-A section as Fig. 19 before it closes a loop. In this case, the following errors in Fig. 20 continuously mutate due to driving control scheme. In Fig. 21, at each turn, the speeds of wheels are altered to pilot whereas slower velocity of vehicle helps to keep tracking.

Conclusions
In this paper, a mechanical architecture for autonomous ground vehicle is introduced to handle multi functions in practical 12 decrease to 1.25 m/s to maintain the errors not larger than expected. The system keeps driving rule for differential vehicle model such that velocity of right wheel must be greater than left wheel to turn left direction.   several times to transpire the calibrated process.
Contrarily, in E-F-C section as Fig. 16, vehicle obligates turn right at corner F. Therefore, the e2 and e3 error are peaked at that time in Fig. 17. After that, they alter slightly in bounded condition. During its execution, the system is forced to carry out a decision at point C. The host computer orders its to continue in linear trajectory. It is also confirmed in Fig. 18 where speeds of vehicle vary. If the latency time appears in communication, maybe, the worse performance could be made. Finally, autonomous vehicle passes C-G-A section as Fig. 19 before it closes a loop. In this case, the following errors in Fig. 20 continuously mutate due to driving control scheme. In Fig. 21, at each turn, the speeds of wheels are altered to pilot whereas slower velocity of vehicle helps to keep tracking.

Conclusions
In this paper, a mechanical architecture for autonomous ground vehicle is introduced to handle multi functions in practical Contrarily, in E-F-C section as Figure 16, vehicle obligates turn right at corner F. Therefore, the e 2 and e 3 error are peaked at that time in Figure 17. After that, they alter slightly in bounded condition. During its execution, the system is forced to carry out a decision at point C. The host computer orders its to continue in linear trajectory. It is also confirmed in Figure 18 where speeds of vehicle vary. If the latency time appears in communication, maybe, the worse performance could be made. Finally, autonomous vehicle passes C-G-A section as Figure 19 before it closes a loop. In this case, the following errors in Figure 20 continuously mutate due to driving control scheme. In Figure 21, at each turn, the speeds of wheels are altered to pilot whereas slower velocity of vehicle helps to keep tracking.

Conclusions
In this paper, a mechanical architecture for autonomous ground vehicle is introduced to handle multi functions in practical factory. The proposed design is feasible, low cost and complies industrial standards while it still maintains flexible motion. The proposed controller ensures that tracking error converges to zero when time goes to infinity. From the results of research, it could be achievable applications in logistics, manufacturing industry and education.

Conflict of Interest
No potential conflict of interest relevant to this article was reported.