AN EFFICIENT CAR LIKE-MOBILE ROBOT DESIGN Wael M El-Medany*, Member of IEEE and IEEE Communications Society, Ahmed M Abuaesh ˚ and Noha M Abuaesh ª *Faculty of Engineering, Fayoum University, Egypt, and IT College, UOB, 32038 Bahrain Email:
[email protected],
[email protected]
Webpage: http://people.man.ac.uk/~mbgedwme/ Tel No: +973-39764964 ˚IT Center UOB, 32038 Bahrain ªMentor Graphics, Egypt
Abstract: This paper presents the hardware design and implementation of a car-like mobile robot that work in an efficient way. The robot is programmed to avoid colliding with obstacles in its way by the aid of ultrasonic sensors. It will change its path if the connected sensors detect an obstacle. After passing the obstacle, the robot will continue on its original path. The system was designed using VHDL in a high level design method. All parts of the design have been simulated and implemented using Xilinx tools. The system was tested in both simulation level using ModelSim and hardware level using Xilinx Spartan 3 AN development board. Key-Words: FPGA, CLMR, Car Robot, Mobile Robot
I. INTRODUCTION Car-Like Mobile Robot (CLMR) system technology is a relatively new technology that replaces human drivers in vehicles by Intelligent Transportations Systems (ITS). This new technology has many useful applications in various fields from military, civilian to industrial use. Even though traffic rules are strict and accurate; car accidents, road congestions and driving fatalities still take place on roads and highways [1] and [2]. Thus, technologies like the Intelligent Transportations Systems have been thought of and are being examined and tested. Robots are widely used in industrial sites, especially in hazard environments where human's presence is highly restricted [3] and [4]. This phenomenon costs companies, societies and governments lives and money. Hence, a robot that can function without direct human control will be very useful in places like nuclear plants or other dangerous industrial sites [5]. In some applications like home care, there is acute computational need for the mobile robot to instantaneously in response to newly acquired environmental information. This demands a real-time generation of a feasible path on contemporary PCs, perhaps via an ordered sequence of destination configurations [6]. Motion autonomy in Robotics may be defined as the ability for a robot to perform a given movement without any external intervention. It is a central problem in Robotics. Depending on the situation considered, motion autonomy is a goal more or less easy to reach. For instance, it is easier to achieve motion autonomy in the case of a manipulator
arm operating on an assembly line (a priori known, carefully engineered and highly predictable workspace) than in the case of a planetary rover (unknown, uncertain and little predictable environment) [7] and [8]. The control scheme for such mobile robots is traditionally decomposed into three subtasks: trajectory generation, position estimation and path tracking. A path consisting of straight lines and circular arc segments is easy to generate, but may not be suitable for a car-like mobile robot which has the non-holonomic constraint and its steering angles at the line-arc transition points have discontinuous curvature [9]. The common methodology for constructing feasible trajectories is by assembling arcs of simple curves [6] and [10]. Theoretical shortest path with bounded curvature synthesized by circular arcs of minimum radius tangentially connected by straight line segments was first presented to generate trajectories [6] and [10 14] for different environmental restrictions. Most of the CLMRs that have been implemented in real hardware were implemented using microcontroller or ASICs (Application Specific Integrated Circuits), but a few have been implemented using FPGAs (Field Programmable Gate Arrays). It has been proved that FPGA is the best choice for cost effective design [1517]. We proposed an efficient design for CLMR that achieved low cost for the hardware implementation and shortest path using Xilinx Spartan 3AN FPGA. The objectives of this research were to develop a CLMR vehicle that will somehow imitate the reactions and responses of an expert driver on a small scale. The idea can be thought of as an elementary Intelligent driver; a driver that avoids collisions and, hence, promotes safety on the roads. The Car-Like Mobile Robot is only another step among all the ITS projects that were implemented before. The materials in this paper are organized as follows: part two gives a general description for our CLMR system architecture; in part three the design flow for the CLMR is given; part four will discuss the sensor circuit; the simulation results and RTL schematic are discussed in part five; at the end in part six a conclusion will be given.
II. CLMR SYSTEM ARCHITECTURE The main part of the CLMR is the controller which has been designed using VHDL, and implemented using Xilinx Spartan 3 AN FPGA. The main function of the controller is to get signals from the sensor circuit and acts accordingly by sending output signals to the motor circuits. The controller has three input signals and four output signals as shown in the VHDL top level model of fig. 1. The input signals are clock, Enable, and Sensor_In; the output signals are Selector(1:0), F_Motor, L_Motor, and R_Motor. The selector out is a two bits multiplexed selector to select between the different sensors. The robot is programmed to avoid colliding with obstacles in its way by the aid of ultrasonic sensors. We are using three sensors front, left, and right as shown in the CLMR system architecture of fig. 2. The robot will change its path if the connected sensors detect an obstacle. After passing the obstacle, the robot will continue on its original path. Field Programmable Gate Array (FPGA) is used instead of microcontroller for the implementation of the main controller.
The FPGA used is the Xilinx first nonvolatile FPGA, which is Spartan 3AN This robot uses premanufactured components for the main vehicle. The vehicle is designed for indoors use only. For example, if the car encounters a hump, it will deal with it as an obstacle and thereby avoid it.
Fig. 1- VHDL Top Level Model
Sensors
Enable
Spartan 3AN FPGA
Steering Motor
Forward Motor Fig. 2 - CLMR System Architecture
After the enable signal is received (in state S0) and a time delay of 1.4 seconds has passed (in state S1), the car enters state S2. This state means that the car is moving forward. The car stays in this state as long as no obstacle is detected from the front sensor. If the front sensor detected an obstacle when moving forward (in sate S2), the car enters state S3. In S3, the car is stopped. The car will not take any action until the sensor’s reading is assured. S3 is only the first step to assure the sensor’s reading. To understand this, we should first understand the nature of the sensor’s signal, this will be clarified in section IV. To simplify the design flow an SM chart for the CLMR is shown in fig. 4. The CLMR controller check first for obstacle in front, then left, and then right. If there are obstacles in all ways the car will stop.
III. CLMR SYSTEM DESIGN FLOW In this section we are going to describe the design flow of our CLMR system. The operation of the CLMR system controller is described by the state graph of fig. 3, where S0 is the initial, and then we have another fourteen states from S1 to S14 as shown in fig. 3. In S0 the car is not moving nor is it sensing obstacles. In this state, the car is waiting for the enable signal to make it work. The car will remain in state S0 as long as the enable signal (En) is not received. Once En is detected, the car enters state S1. This state mainly represents a time delay of almost 1.4 seconds (70M/50MHz). We had to insert this time delay since the FPGA’s clock frequency is much faster than the enable circuit’s frequency. T'
En’
S0
En
S1
sel
Sel’
T
S2
Sel
S3
Sel’ Sel S14
S4 S1'S0 S8
Sel’ Sel
S13
S5
Sel’ Sel’ Sel’ C’ T
S12
S6
Sel C S11 S10 S1'S0
S1' S0'
S9
S1S0
S7 Sel T’
Fig. 3 - State graph for the CLMR
Fig. 4 – SM Chart for the CLMR
IV. THE SENSOR INTERFACE CIRCUIT Robots usually use either optical sensors or acoustic sensors. We chose to use ultrasonic sensors in our CLMR. Ultrasonic sensors are a type of acoustic sensor. Ultrasonic sensors can offer a number of advantages over optical methods. Most notably, they can be used for measuring distance to any surface, including glass and liquids. Ranges vary from a few millimeters to around ten meters, without increasing the size of the device. Thus, ultrasonic has a perfect range of sensing obstacle which is ideal for car implementation. Typically, an ultrasonic sensor sends a 'ping' and waits to hear an echo. Sound waves are generated by the transmitter and bounce off objects, returning an echo to the receiver. Fig. 5, shows the ultrasonic sensor circuit that has been used for our CLMR. Ultrasonic distance sensors measure the time for a pulse of sound to travel to a surface and return, then calculate distance from the estimated speed of sound. In addition, ultrasonic sensors are resistant to vibration, radiation, background light and noise. They are not affected by dust, dirt or high humidity. Unlike optical sensors, their performance is the same during day, night, glaring sunlight, darkness, rain, fog or snow. Finally, ultrasonic sensors are cheaper than optical sensors and are more available. One disadvantage of ultrasonic sensors is it does not feel sound absorbing materials like cloth. The sensors we used were originally meant to be fitted at the end of vehicles as a car reversing aid system. They are designated to feel objects as far as one meter and as close as 10 centimeters. Nevertheless, we can still make do with those dimensions.
Fig. 5 - The Sensor Circuit
V. CLMR SIMULATION RESULTS AND RTL SCHEMATIC In this section we are going to discuss the system resources and simulation results. Fig. 6 shows snapshot of part of the Register Transfer Level (RTL) schematic for the CLMR controller, this snapshot here only to give an idea about the complexity of the controller circuit. The RTL Schematic is generated after synthesizing the design using Xilinx ISE 10.1. It shows the components of the main controller, some of the important components are the state machine design, the sensing component, and the controlling component. The design resources give an idea about the device utilization, in terns it will give an idea about the complexity, and this will reflects the cost of the system. The number of slices in the design is 233 out of 5888, number of 4 input LUTs is 423 out of 11776, number of slice Flip Flops is 195 out of 11776, and the number of bonded IOBs is 8 out of 372. These resources for Spartan 3AN, targeting XC3S700AN-FGG484-4 device. Fig. 7 shows snapshot of the Simulation results for CLMR turns right if obstacles appeared on its front and left sides then goes back to its original path, the begins and stops moving upon receiving the enable signal, in the figure the turn right steering motor inverted and turn left steering motor inverted are both high, whether the sensor signal is low. The enable signal and forward motor signal are active high. Begins and stops are shown in the forward motor signal during the activation of the enable signal. The controller is sequentially changing the selector output signal to sequentially sense the three sensors.
Fig. 6 - Register Transfer Level Schematic for CLMR
Sensor_in Enable F_Motor L_Motor R_Motor Selector Clock
Fig. 7 - ModelSim Simulation Results
VI. CONCLUSION The hardware design and circuit implementation of CLMR has been introduced in this paper. They can function without direct of human control. The design has been described using VHDL and implemented in hardware using Xilinx Spartan 3AN FPGA. The CLMR system has been tested in both simulation and hardware level. The design has been synthesised and implemented using Xilinx ISE 10.1. The simulation has been done using ModelSim 5.6e. The target technology for the system design is XC3s200-5ft256 device from Xilinx. REFERENCES [1]. Belta C, Kumar V. Motion generation for formation of robots: A geometric approach. In: Proceedings of International Conference on Robotics and Automation. Seoul, Korea, 2001. [2]. Das A, Spletzer J, Kumar V, Taylor C. Ad hoc networks for localization and control. In: Proceedings of the 41st IEEE Conference on Decision and Control. Las Vegas, NV, USA, 2002. [3]. Spletzer J, Das A, Fierro R, Taylor C, Kumar V, Ostrowski J. Cooperative localization and control for multi-robot manipulation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. Hawaii, USA, 2001, p. 631-636. [4]. B. Muller, J. Deutscher, S. Grodde. Continuous
curvature trajectory design and feedforward control for parking a car, IEEE Transactions on Control Systems Technology, 15(3), 2007, p. 541−553. [5]. Seiichi Shin. A Future of Car Droved by Electronics, International Joint Conference SICE-ICASE’6, Oct. 2006, p 1-11 - 1-14. [6]. Tzu-Chen Liang, Jing-Sin Liu, Gau-Tin Hung, YauZen Chang, “Practical and flexible path planning for car-like mobile robot using maximal-curvature cubic spiral”, Robotics and Autonomous Systems 52 (2005) , p. 312–335. [7]. Th. Fraichard_, Ph. Garnier,”Fuzzy control to drive carlike vehicles”, Robotics and Autonomous Systems 34 (2001), p. 1–22. [8]. Li, T.-H.S.; Shih-Jie Chang; Yi-Xiang Chen, “Implementation of human-like driving skills by autonomous fuzzy behavior control on an FPGA-based car-like mobile robot”, Industrial Electronics, IEEE Transactions on Volume 50, Issue 5, Oct. 2003, P. 867 – 880. [9]. Dongbing Gu, Huosheng Hu, “Neural predictive control for a car-like mobile robot”, Robotics and Autonomous Systems 39 (2002) p. 73–86. [10]. F.G. Pin, H.A. Vasseur, “Autonomous trajectory generation for mobile robots with non-holonomic and steering angle constraints”, in: Proceedings of the IEEE International Workshop on Intelligent Motion Control, 1990, p. 295–299.
[11]. L.E. Dubins, “On curves of minimal length with a constraint on average curvature and with prescribed initial and terminal position and tangents”, Am. J. Math. 79 (1957), p. 497–516. [12]. J.A. Reeds, R.A. Shepp, Optimal paths for a car that goes both forward and backwards, Pac. J. Math. 145 (2) (1990). [13]. G. Desaulniers, On shortest paths for a car-like robot maneuvering around obstacles, Robot. Auton. Syst. 17 (1996), p. 139–148. [14]. G. Desaulniers, F. Soumis, An efficient algorithm to find a shortest path for a car-like robot, IEEE Trans. Robot. Autom. 11 (6) (1995), p. 819–828. [15]. A.P. Wang, C.S. Chen, H.Z. Sze, K.H. Tseng, Y.S. Kung. FPGA-Implementation of Inverse Kinematics and Servo Controller for Robot Manipulator, IEEE International Conference on Robotics and Biomimetics, 2006, p.1163-1168. [16]. Wael M El-Medany, Ahmed and Noha M Abuaesh, “Controller Design for Smart Car Robot and Implementation using Spartan 3 FPGA, Proceedings of the IET International Conference in Intelligent Systems CIS-2008, Bahrain, 1-3 December 2008 [17]. Anderson Correia1, Carlos H. Llanos1, Rodrigo W. Carvalho1 and Sadek A. Alfaro1, “A Platform Based on Reconfigurable Architectures and Virtual Instrumentation Applied to the Driving Automobile Problem”, Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation, Corfu Island, Greece, February 16-19, 2007, p 242-248
Dr. WAEL MOHAMED EL-MEDANY is an assistant professor at the department of Communications and Electrical Engineering, Fayoum University, Egypt. Currently he teaches at the department of Computer Engineering, University Of Bahrain. He holds a PhD degree from Manchester University, UK, June 1999, with a major in Electrical Engineering and a minor in Chip Design and its application in error control coding. He got his MSc degree in computer communications from Faculty of Electronic Engineering, Menoufia University, October 1991, and his BSc degree in Electronic Eng from the same University May 1987. His research interest in programmable ASIC design, Remote Sensing, Home Automation, and Intelligent Transportation System. Ahmed M Abuaesh: Ahmed is a network Engineer at the University of Bahrain, Bahrain. He graduated from the same university with a bachelor degree in computer engineering, 2006. Ahmed developed systems related to smart car technology; some of which are using the GPRS and OTA (Over The Air) technologies. Noha M Abuaesh: Noha graduated from the University of Bahrain with a bachelor degree in Computer Engineering, 2007. Noha has experience in developing systems using hardware languages and testing them using AVM and OVM methodologies. Noha currently works at Mentor Graphics, Egypt.
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