- Email: [email protected]
Design and Development of Autonomous Wrestling Robots
IFAC [:0[>
Copyright Cl IFAC Mechatronic Systems, California, USA, 2002
Publications www.elsevier.comllocatelifac
Design and Development of Autonomous Wrestling Robots S.S. Ge, Z.L. Ruan and T.H. Lee Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 [email protected]
Abstract In this paper, we present the basic architecture of our autonomous sumo-wrestling robots and the implementations. The issues discussed include the design objectives, the mechanical and the electronics hardware and the software. The software system includes the combat strategy used by the robots. The sumowrestling robot is an ideal platform for robotics research on object detection. control. decision-making and strategic planning. Copyright © 2002 IFAC Keyword: Mobile Robot. Digital Signal Processor. Fuzzy Logic
preliminary controller intelligence built-in.
1. INTRODUCTION
The advances in computation, communication, and micro-e1ectro-mechanical systems make the promise of building autonomous robot a real possibility. An autonomous intelligent system can sense, learn and interact with the environment. The problem fascinates many researchers as it is challenging, complex and inter-disciplinary.
with
simple
2. SUMO-WRESTLING COMPETITION The objectives of the event are to emulate a real-life situation in the "battle field", and to stimulate the research in the area. In the popular sumo-wrestling competition, the robots are required to push their opponents out of the arena like in real-life sumowrestling sports. In this contest, the robot is in a partially unstructured environment as the state and manoeuvre of its opponents are unknown. The robot also has the danger of falling out of the platform when manoeuvring at the edge or corner of the platform. Thus, the robot must have built-in intelligence to make the right decisions in all possible scenarios. In a direct confrontation, the robot must be powerful enough to counteract its opponents by harnessing its strength and attaching the weakness of the opponent, and at the same time must be durable. The sumo-wrestling robot is an integration of power, intelligence and robustness.
In the project, two types of robots are developed: wheeled and tracked vehicles which are both nonholonomic systems and hard to control. Intelligent control strategies shall be investigated, implemented and tested on the two types of platform. Several autonomous robots have been design and developed in our Lab. Preliminary intelligence is built-in in the robots which are capable of searching for enemies, engaging, escaping or avoiding a fight. The paper is organised as discusses the competition mechanical designs of the two in Section 3. In Section 4, the design is presented in detail.
design
follow. Section 2 requirements. The robots are presented electronics hardware Section 5 gives the
In the sumo-wrestling competition, the arena is a rectangle of size 2xlm2. There are two box marks as the starting point of two robots. The centerline is for
7(,7
Table 2 Electrical and Mechanical comparison
indicating purpose. In addition, there are several constraints regarding the robot constructions: (i) The weight must be less than IOkg. (ii) The size must be smaller than 30cmx30cm. (iii) The robot must not anchor itself on the arena. These constraints are to ensure comparable physical conditions between two robots and a fair game.
Wheeled Robot Movable shovel
In analyzing the rule and regulation, the few critical factors for winning the combat are summarized in Table 1.
Mancuverability
Sensors
Strategy
Weapon
Wheels
Caterpillar Tracks
Motion system
Contact sensors on the shovel
Contact sensors on the front chassis
Detect contact with the opponent
1 ultrasonic sensor
2 ultra sonic sensor
Detect the distance of the opponent and trace it
Table1 Critical Factors Grip
, ~'
The grip on the arena is essential here since it directly relates to the pushing force of the robot. The grip is affected by the driving mechanism and the materials used. The maneuverability determines if the robot is able to attack at the retract in right time or disadvantageous position This is to help to robot to detect its opponents and make the right decision . The robot must have the good strategy to win.
Purpose
Tracked Robot
Infrared sensors
Infrared sensors ;
Detect the edge of the platform
Incremental Encoder
Incremental Encoder
Detect the absolute motion of the robot
Motor Driver
Motor Driver
Drive the DC motors
DSP controller
DSP controller
The brain
Both robot employ differential driving system, i.e., two DC motors are used to drive each side of the robot wheels. Since the caterpillar system has bigger surface contact area, it is able to reduce the chance of losing grip. However, it also suffers from slow turning speed. Fig. I shows the two robots, where on the left is the tracked robot and on the right is wheeled robot.
3. MECHANICAL DESIGN As different design has cons and pros, robots of two different mechanical designs (tracked and wheeled robots) are developed in order to emulate the actual competition scenarios and at the same time help to understand the problem, and to improve the intelligence of the robots. The tracked robot uses caterpillar track as the driving mechanism in order to maximize the grip on the arena. The trade-off is the loss of maneuverability, i.e. it is not able to move fast and turn fast. One the other hand, for better manoeuvrability, the other robot is built on wheels whieh can turn and run faster. In order to compensate for the inferior performance on grip, the robot is equipped with a movable shovel in the front like those on the scrapers. During the combat, the robot tries to intrude its shovel underneath the opponent and lift it up as much as it can. A portion of opponent's weight is asserted to the robot and therefore the robot's friction is increased. Table 2 summarizes the sensors and electronics used in the two robots.
Fig. 1 Sumo Wrestling Robots
4. ELECTRONICS DESIGN The OSP card designed in the Student Project Lab is to be used as the brain as it is readily available and have been used in many other projects. The nice thing about the OSP cards is that it is scalable in terms of I/O numbers, memories, motor drivers, and infrared sensor arrays. In particular, the motion controller cards have been implemented using a TMS320C31 floating point OSP to provide the computational power required in motion control. A
768
Figure 3 shows the architectural overview of the controller.
set of DSP-compatible motion control peripherals has been developed using both Field Programmable Gate Array (FPGA) and programmable Application Specific Integrated Circuit (pASJC) devices . Numerous modular prototypes have been made using 4 or 6 layers PCBs. The cards designed are fully scalable, i.e. its functionality is expandable by adding sub-modules: • I/O modules to provide the required number of 110 channels (analog and digital); • Sensor modules to interface with different sensors including ultrasonic, infrared sensors, encoders; • Communication modules for parallel port, RS-232 and wireless modern; and • Miniature motor driver modules for the actual motor drive.
, - - - + 12-bi! AOCI 12·bi! AOC2 Aa
-"'-
I - - - - + II-bil PWM2 Stepper Moto< Control
l6-b. [lItr. Encoder 1 Serial Inttrface
l6-bjt lnet. Encoder 3 e1
Fig. 3 Architectural Overview of DSP Controller 4.2. Motor Drivers The motor driver amplifies the PWM signals from the controller and feeds them to the DC motors. Figure 4 shows the picture of the driver module.
Fig. 4 DC Motor Driver Module
4.1. DSP Cards The DSP controller has the following features that make it suitable for autonomous mobile robot (Ruan, Ge and Lee. 2001).
• • • • • • •
16-00 111<1'. Encoder 2
Expansion Card Slot
The modular design approach makes the design of the hardware as easy as patching up building blocks for different customer requirements. In addition, tailored made embedded systems using different leading edge processors can also be easily designed as specified by the customers. As the design of cards is generic, it can also be used for other purposes such as signal processing and image processing.
• •
_
1 - -- + II·bic PWM 1
4.3. Sensors The sensors used in the robot include the infrared sensor, ultrasonic sensor, limit switch and incremental encoder (Everet!, 1995). • Infrared sensor The infrared sensor is used for two purposes, namely detecting opponent and edge of the arena. The infrared sensor emits infrared ray and detects the reflections. Therefore, it can be used to detect opponent at close range. The edge of the arena can be detected by using the same principle. By placing the infrared sensors at the corner of the robot, the sensors will be triggered when one corner of the robot is out of the arena. Figure 5 shows the infrared sensor module.
32-bit floating point DSP, 20MIPS 128Kx32 bit SRAM, zero wait state operation 4M bit flash ROM Two 12-bit ADC Three 16-bit incremental encoder inputs Two ll-bit PWM outputs 16-bit digital inputs Real-time micro kernel and 110 library Control library functions
The picture of the actual design of the DSP card is shown in Figure 2.
Fig 5 Infrared Sensor Module • Fig.2 DSP Card
769
Ultrasonic Sensor The ultrasonic senor is working by emitting ultrasonic waves and detecting any echoes.
Therefore, it can be used for detect opponent robot in the long range. By placing two ultrasonic sensors, the robot is able to trace the opponent's motion and hence take action earlier (Veelaert and Bogaerts, 1995). •
•
head toward the target. The controller turns the robot by driving the wheels on two sides at different speed, which is highly nonlinear because of the high friction incurred. A fuzzy logic controller is used here to take the advantage of nonlinearity. The inputs of the fuzzy logic controller are the readings of the two ultrasonic sensors. The output is the difference in the speeds of the two side wheels. Table 3 shows the fuzzy logic rules. T able 3 . Fuzz L OglC ' R u Ies
Limit Switch The limit switch is mechanical sensor. It is used to detect the physical contact. For the "brutal force' robot, the limit switches are placed behind the front panel of the robot so that the robot knows the best direction to push at. For the "maneuverability" robot, the limit switches are placed on the shovel so that it knows whether the shovel is underneath the opponent.
~ x2
NE ZE PE
..
NE
ZE
PE
NB .NB
NE ZE PE
PE PB PS
NE
The robot runs in a very noisy environment. The signals read from the sensors have to be filtered. Butterworth lower-pass filter is currently being used in the actual implementation.
Incremental Encoder The incremental encoder is used to measure rotational velocity and displacement. It is used here to if the robot is pushed away by the opponent. If the robot is moving forward and the incremental encoder shows that the robot speed is negative, the robot knows that the opponent is advantageous and evasive action should be taken.
S.2 Combat Strategy The combat can be seen a series of actions for the robot to handle different situations. For example, when the opponent is within the sight, the robot must charge or activate the weapon. When the opponent is out of sight, the robot must start scanning to find it. Therefore, a state-machine type of controller structure is used. In the other word, the robot's strategy is described using a state-machine.
4.4 Actuators The actuators used here are DC motors. They are driving the robot as well as the shovel. PWM drivers are used to drive the DC motors 4.5 Power The robot uses two sets of batteries. One set is 6V for the power of sensors and controller. The other is 12V for the power of DC motors and driver.
Before using the state machine to construct the strategy, all the possible states of the robot are to be enumerated. Each state defines its own action. When the robot is in a state, the corresponding actions are performed. Table 4 shows all the possible actions.
5. CONTROLLER DESIGN
Table 4. Actions for Sumo-Wrestling Robots
5. 1 Low-level Control Algorithms The low-level control algorithms are to facilitate primary actions of the robot, e.g. moving forward at certain speed or track the opponent robot. There are two low-level control algorithms used, namely the motion controller and the target tracking controller (Ge, Wang, Lee and Zhou, 2001 ). The motion controller is to control the DC motors running at the specified speed. The conventional PlO controller with anti-windup configurations is implemented. This is because during combat situation, the control output may reach its saturated level because of the high friction encountered. This is to prevent instability of the controller. The target-tracking controller takes the reading from the two ultrasonic sensors and adjusts the robot to
770
NO.
Action Name
DeSCription
A1
STOP
STOP at position
A2
MOVE
Move forward at the specified speed. Positive is forward.
A3
ROTATE
Rotate at the specified line speed. Positive is clockwise
A4
Follow
Lock down and follow the opponent
AS
Release Shovel
Release the weapon
A6
Retract Shovel
Retract the weapon
the
current
These are the elementary actions. Some of them can be combined to form complex actions. For example, combining A I and A3 will get the action that is rotating on the spot; combining A2 and A3 will get the action that is turning. Table 5 lists the possible states. Table 5 posslble states for Sumo-wrest InQ Robots Description Action State
NO.
Scan
SI
S2
Pursue
Scanning around for opponent
Moving slowly forward and rotate periodically
Lock the opponent and ready to charge
Moving toward the target and release weapon
S3
Charge
Charge the opponent
Moving fast to target
S4
Retreat
Pushed back by the opponent
Moving backward fast and rotate fast
S5
Scan out
During scan, part of the robot is out of the platform
Moving backward and rotate to the opposing side
During combat, part of the robot is out of the platform
Moving forward and rotate to the opposing side.
Combat out
S6
Fig 6 Strategy state diagram of gladiator robot
One advantage of the state machine is the scalability. It can be easily expanded to implement much complex strategy by providing more states and more actions. Moreover, each state may contain sub-states that handle the situations more specifically. In this way, it allows some level of hierarchical design (Fujimura and Sarnet, 1989 ).
6. CONCLUSION & FURTHER RESEARCH In this paper, the sumo-wrestling robot competition has been firstly discussed, and then the design and development of two autonomous sumo-wrestling robots are presented. The robots make use of statemachine to describe the strategy of intelligence, which is very scalable and able to be expended to much more complex strategies for real-life situations. The sumo-wrestling competition provides a real-life testing arena for intelligence, survival chance and mission success, which are very important for the utmost deployment of autonomous robots in the battle fields. In making the system truly intelligent, research at the high abstract level will also be investigated. Representation and manipulation of information are to be investigated using the following most suitable tools: matter-element theory for non-compromising and non-compatible problems, neural networks, Bayesian belief networks, or fuzzy linguistic models for uncertain inputs. Variable hierarchical structures will be investigated in effective organizing the many basic behaviours at different levels.
The transition between states is triggered by some external events, i.e. sensor readings. • S I ~S2: Find opponent from the ultrasonic sensor. • •
S I ~S5: Detect one part is out of platform. S2~S3:
Detect
opponent
for
contact
sensors •
S2~S5:
•
S3~S4: Detect negative incremental encoder
•
S3~S 1:
Lose contact with opponent
•
S4~S I :
Lose contact with opponent
•
S5~S 1:
All parts are inside the platform.
•
S6~S3:
All parts are inside the platform.
Detect one part is out of platform speed
from
Further research will also be carried out dynamic path planning for mobile robots in a dynamic environment with targets and obstacles moving. This is a very challenging task, yet an essential part of an intelligent vehicle. The robots developed and the know-how gained will be very useful for the work in this direction.
Figure 6 shows the strategy used by the gladiator robot.
771
7. REFERENCES
Function Method, Ph.D Dissertation, University of Missouri-Columbia.
Borenstein, J. and Koren. 1991. "The Vector Field Histogram --- Fast obstacle Avoidance for Mobile Robots", IEEE Trans. Robotic. Autom., 7(3): 278-288.
Khosla, P. and R. Volpe, 1988. "Superquadric Artificial Potentials for Obstacle Avoidance and Approach", Proc. IEEE Con! Robotic. Autom., pp.I778-1784.
Canny, J. F. and M. C. Lin, 1999. "An Opportunistic Global Path Planner", Proc. of IEEE Conf. Robotic. Autom., pp. 1554-1559.
Kim, J. O. and P. K. Khosla, 1992. "Real-time Obstacle Avoidance Using Harmonic Potential Functions", IEEE Trans. Robotic. Autom., 8(3): 338-349.
Chuang, J. H. and N. Ahuja, 1998, "An Analytically Tractable Potential Field Model of Free Space and Its Application in Obstacle Avoidance", IEEE Trans. Sys. Man. Cybernetics.-- Part B, 28(5): 729-736.
Ko, N. Y. and B. H. Lee, 1996. "Avoidability Measure :jn Moving Obstacle Avoidance Problem ~nd. , Its Use for Robot Motion Planning", Prlic. IEEEIRSJ Int. Con! Intell. Robots and Sys., Vol. 3, pp. 1296 -1303 .
Everett, H.R., 1995. Sensors for Mobile Robots: Theory and Applications, Wellesley, Mass.
Latombe, J. 1991. Robot Motion Planning, Kluwer Academic, Boston,
Fujimura, K. and H. Samet, 1989. "A Hierarchical Strategy for Path Planning Among Moving Obstacles", IEEE Trans. Robotic. Autom., 5(1): 61-69.
Ruan, Z.L., S.S. Ge and T.H. Lee, 2001 "Design and Implementation of an Embedded Controller System for Mobile Robot," Proceedings of Int. Con! on Computational Intelligence. Robotics and Autonomous Systems (c/RAS), pp. 182-187, Nov. 28-30, Singapore.
Ge S. S. and Y. J. Cui, 2000 "New Potential Functions for Mobile Robot Path Planning", IEEE Transactions on Robotics Automation, Vol. 16, No. 5, pp.615 -620.
Shih, C. L. and T. Lee, 1990. "A Unified Approach for Robot Motion Planning with Moving Polyhedral Obstacles", IEEE Trans. Sys. Man. Cyb., 20(4): 903-915.
Ge, S.S. and Y.J.Cui, "Dynamic Motion Planning for Mobile Robots Using Potential Field Method" To Appear in Autonomous Robots, Vol. 13, no. 3, 2002
Shiller, Z. and Y.R. Gwo, 1991. "Dynamic Motion Planning of Autonomous Vehicles", IEEE Trans. Robotic. Autom. I 7(2): 241-249.
Ge S.S., J. Wang, T.H. Lee and G.Y. Zhou, 2001, "Adaptive robust stabilization of dynamic nonholonomic chained systems," Journal of Robotic Systems, Vol. 18, no. 3, pp.119-133.
Sun, Z., S.S. Ge, and T.H. Lee, 2001, "Stabilization of nonholonomic chained systems via nonregular Feedback Linearization," Systems and Control Letters, vol. 44, pp. 279 -289.
Ge S.S., Z.P. Wang and T.H. Lee, 2002 "Adaptive stabilization of uncertain nonholonomic systems by output feedback," To appear in Proceedings of IFAC Conference on Mechatronic Systems, Berkeley, USA.
Veelaert, P. and W. Bogaerts, 1995. "Ultrasonic potential field sensor for obstacle avoidance", it IEEE Trans. Robotic. Autom., 15(4), pp. 774-779.
Gil de Lamadrid, J.F. and M. Gini, 1990, "Path Tracking Through Uncharted Moving Obstacles", IEEE Trans. Sys. Man, Cyb., 20(6): 1408-1422. Huo
Watanabe, K., 1998. "Control of an Omnidirectional Mobile Robot", Proceedinds of 2nd Int. Con! Knowledge-Based Intell. Electro. Sys., pp. 5160.
W. and S.S. Ge, 2001, "Exponential Stabilization of Nonholonomic Systems: An ENI Approach," International Journal of Control, Vo1.74, No.15, pp. 1492-1500.
Hussien, B, 1989. Robot Path Planning and Obstacle Avoidance by Means of Potential
772
Copyright Cl IFAC Mechatronic Systems, California, USA, 2002
Publications www.elsevier.comllocatelifac
Design and Development of Autonomous Wrestling Robots S.S. Ge, Z.L. Ruan and T.H. Lee Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576 [email protected]
Abstract In this paper, we present the basic architecture of our autonomous sumo-wrestling robots and the implementations. The issues discussed include the design objectives, the mechanical and the electronics hardware and the software. The software system includes the combat strategy used by the robots. The sumowrestling robot is an ideal platform for robotics research on object detection. control. decision-making and strategic planning. Copyright © 2002 IFAC Keyword: Mobile Robot. Digital Signal Processor. Fuzzy Logic
preliminary controller intelligence built-in.
1. INTRODUCTION
The advances in computation, communication, and micro-e1ectro-mechanical systems make the promise of building autonomous robot a real possibility. An autonomous intelligent system can sense, learn and interact with the environment. The problem fascinates many researchers as it is challenging, complex and inter-disciplinary.
with
simple
2. SUMO-WRESTLING COMPETITION The objectives of the event are to emulate a real-life situation in the "battle field", and to stimulate the research in the area. In the popular sumo-wrestling competition, the robots are required to push their opponents out of the arena like in real-life sumowrestling sports. In this contest, the robot is in a partially unstructured environment as the state and manoeuvre of its opponents are unknown. The robot also has the danger of falling out of the platform when manoeuvring at the edge or corner of the platform. Thus, the robot must have built-in intelligence to make the right decisions in all possible scenarios. In a direct confrontation, the robot must be powerful enough to counteract its opponents by harnessing its strength and attaching the weakness of the opponent, and at the same time must be durable. The sumo-wrestling robot is an integration of power, intelligence and robustness.
In the project, two types of robots are developed: wheeled and tracked vehicles which are both nonholonomic systems and hard to control. Intelligent control strategies shall be investigated, implemented and tested on the two types of platform. Several autonomous robots have been design and developed in our Lab. Preliminary intelligence is built-in in the robots which are capable of searching for enemies, engaging, escaping or avoiding a fight. The paper is organised as discusses the competition mechanical designs of the two in Section 3. In Section 4, the design is presented in detail.
design
follow. Section 2 requirements. The robots are presented electronics hardware Section 5 gives the
In the sumo-wrestling competition, the arena is a rectangle of size 2xlm2. There are two box marks as the starting point of two robots. The centerline is for
7(,7
Table 2 Electrical and Mechanical comparison
indicating purpose. In addition, there are several constraints regarding the robot constructions: (i) The weight must be less than IOkg. (ii) The size must be smaller than 30cmx30cm. (iii) The robot must not anchor itself on the arena. These constraints are to ensure comparable physical conditions between two robots and a fair game.
Wheeled Robot Movable shovel
In analyzing the rule and regulation, the few critical factors for winning the combat are summarized in Table 1.
Mancuverability
Sensors
Strategy
Weapon
Wheels
Caterpillar Tracks
Motion system
Contact sensors on the shovel
Contact sensors on the front chassis
Detect contact with the opponent
1 ultrasonic sensor
2 ultra sonic sensor
Detect the distance of the opponent and trace it
Table1 Critical Factors Grip
, ~'
The grip on the arena is essential here since it directly relates to the pushing force of the robot. The grip is affected by the driving mechanism and the materials used. The maneuverability determines if the robot is able to attack at the retract in right time or disadvantageous position This is to help to robot to detect its opponents and make the right decision . The robot must have the good strategy to win.
Purpose
Tracked Robot
Infrared sensors
Infrared sensors ;
Detect the edge of the platform
Incremental Encoder
Incremental Encoder
Detect the absolute motion of the robot
Motor Driver
Motor Driver
Drive the DC motors
DSP controller
DSP controller
The brain
Both robot employ differential driving system, i.e., two DC motors are used to drive each side of the robot wheels. Since the caterpillar system has bigger surface contact area, it is able to reduce the chance of losing grip. However, it also suffers from slow turning speed. Fig. I shows the two robots, where on the left is the tracked robot and on the right is wheeled robot.
3. MECHANICAL DESIGN As different design has cons and pros, robots of two different mechanical designs (tracked and wheeled robots) are developed in order to emulate the actual competition scenarios and at the same time help to understand the problem, and to improve the intelligence of the robots. The tracked robot uses caterpillar track as the driving mechanism in order to maximize the grip on the arena. The trade-off is the loss of maneuverability, i.e. it is not able to move fast and turn fast. One the other hand, for better manoeuvrability, the other robot is built on wheels whieh can turn and run faster. In order to compensate for the inferior performance on grip, the robot is equipped with a movable shovel in the front like those on the scrapers. During the combat, the robot tries to intrude its shovel underneath the opponent and lift it up as much as it can. A portion of opponent's weight is asserted to the robot and therefore the robot's friction is increased. Table 2 summarizes the sensors and electronics used in the two robots.
Fig. 1 Sumo Wrestling Robots
4. ELECTRONICS DESIGN The OSP card designed in the Student Project Lab is to be used as the brain as it is readily available and have been used in many other projects. The nice thing about the OSP cards is that it is scalable in terms of I/O numbers, memories, motor drivers, and infrared sensor arrays. In particular, the motion controller cards have been implemented using a TMS320C31 floating point OSP to provide the computational power required in motion control. A
768
Figure 3 shows the architectural overview of the controller.
set of DSP-compatible motion control peripherals has been developed using both Field Programmable Gate Array (FPGA) and programmable Application Specific Integrated Circuit (pASJC) devices . Numerous modular prototypes have been made using 4 or 6 layers PCBs. The cards designed are fully scalable, i.e. its functionality is expandable by adding sub-modules: • I/O modules to provide the required number of 110 channels (analog and digital); • Sensor modules to interface with different sensors including ultrasonic, infrared sensors, encoders; • Communication modules for parallel port, RS-232 and wireless modern; and • Miniature motor driver modules for the actual motor drive.
, - - - + 12-bi! AOCI 12·bi! AOC2 Aa
-"'-
I - - - - + II-bil PWM2 Stepper Moto< Control
l6-b. [lItr. Encoder 1 Serial Inttrface
l6-bjt lnet. Encoder 3 e1
Fig. 3 Architectural Overview of DSP Controller 4.2. Motor Drivers The motor driver amplifies the PWM signals from the controller and feeds them to the DC motors. Figure 4 shows the picture of the driver module.
Fig. 4 DC Motor Driver Module
4.1. DSP Cards The DSP controller has the following features that make it suitable for autonomous mobile robot (Ruan, Ge and Lee. 2001).
• • • • • • •
16-00 111<1'. Encoder 2
Expansion Card Slot
The modular design approach makes the design of the hardware as easy as patching up building blocks for different customer requirements. In addition, tailored made embedded systems using different leading edge processors can also be easily designed as specified by the customers. As the design of cards is generic, it can also be used for other purposes such as signal processing and image processing.
• •
_
1 - -- + II·bic PWM 1
4.3. Sensors The sensors used in the robot include the infrared sensor, ultrasonic sensor, limit switch and incremental encoder (Everet!, 1995). • Infrared sensor The infrared sensor is used for two purposes, namely detecting opponent and edge of the arena. The infrared sensor emits infrared ray and detects the reflections. Therefore, it can be used to detect opponent at close range. The edge of the arena can be detected by using the same principle. By placing the infrared sensors at the corner of the robot, the sensors will be triggered when one corner of the robot is out of the arena. Figure 5 shows the infrared sensor module.
32-bit floating point DSP, 20MIPS 128Kx32 bit SRAM, zero wait state operation 4M bit flash ROM Two 12-bit ADC Three 16-bit incremental encoder inputs Two ll-bit PWM outputs 16-bit digital inputs Real-time micro kernel and 110 library Control library functions
The picture of the actual design of the DSP card is shown in Figure 2.
Fig 5 Infrared Sensor Module • Fig.2 DSP Card
769
Ultrasonic Sensor The ultrasonic senor is working by emitting ultrasonic waves and detecting any echoes.
Therefore, it can be used for detect opponent robot in the long range. By placing two ultrasonic sensors, the robot is able to trace the opponent's motion and hence take action earlier (Veelaert and Bogaerts, 1995). •
•
head toward the target. The controller turns the robot by driving the wheels on two sides at different speed, which is highly nonlinear because of the high friction incurred. A fuzzy logic controller is used here to take the advantage of nonlinearity. The inputs of the fuzzy logic controller are the readings of the two ultrasonic sensors. The output is the difference in the speeds of the two side wheels. Table 3 shows the fuzzy logic rules. T able 3 . Fuzz L OglC ' R u Ies
Limit Switch The limit switch is mechanical sensor. It is used to detect the physical contact. For the "brutal force' robot, the limit switches are placed behind the front panel of the robot so that the robot knows the best direction to push at. For the "maneuverability" robot, the limit switches are placed on the shovel so that it knows whether the shovel is underneath the opponent.
~ x2
NE ZE PE
..
NE
ZE
PE
NB .NB
NE ZE PE
PE PB PS
NE
The robot runs in a very noisy environment. The signals read from the sensors have to be filtered. Butterworth lower-pass filter is currently being used in the actual implementation.
Incremental Encoder The incremental encoder is used to measure rotational velocity and displacement. It is used here to if the robot is pushed away by the opponent. If the robot is moving forward and the incremental encoder shows that the robot speed is negative, the robot knows that the opponent is advantageous and evasive action should be taken.
S.2 Combat Strategy The combat can be seen a series of actions for the robot to handle different situations. For example, when the opponent is within the sight, the robot must charge or activate the weapon. When the opponent is out of sight, the robot must start scanning to find it. Therefore, a state-machine type of controller structure is used. In the other word, the robot's strategy is described using a state-machine.
4.4 Actuators The actuators used here are DC motors. They are driving the robot as well as the shovel. PWM drivers are used to drive the DC motors 4.5 Power The robot uses two sets of batteries. One set is 6V for the power of sensors and controller. The other is 12V for the power of DC motors and driver.
Before using the state machine to construct the strategy, all the possible states of the robot are to be enumerated. Each state defines its own action. When the robot is in a state, the corresponding actions are performed. Table 4 shows all the possible actions.
5. CONTROLLER DESIGN
Table 4. Actions for Sumo-Wrestling Robots
5. 1 Low-level Control Algorithms The low-level control algorithms are to facilitate primary actions of the robot, e.g. moving forward at certain speed or track the opponent robot. There are two low-level control algorithms used, namely the motion controller and the target tracking controller (Ge, Wang, Lee and Zhou, 2001 ). The motion controller is to control the DC motors running at the specified speed. The conventional PlO controller with anti-windup configurations is implemented. This is because during combat situation, the control output may reach its saturated level because of the high friction encountered. This is to prevent instability of the controller. The target-tracking controller takes the reading from the two ultrasonic sensors and adjusts the robot to
770
NO.
Action Name
DeSCription
A1
STOP
STOP at position
A2
MOVE
Move forward at the specified speed. Positive is forward.
A3
ROTATE
Rotate at the specified line speed. Positive is clockwise
A4
Follow
Lock down and follow the opponent
AS
Release Shovel
Release the weapon
A6
Retract Shovel
Retract the weapon
the
current
These are the elementary actions. Some of them can be combined to form complex actions. For example, combining A I and A3 will get the action that is rotating on the spot; combining A2 and A3 will get the action that is turning. Table 5 lists the possible states. Table 5 posslble states for Sumo-wrest InQ Robots Description Action State
NO.
Scan
SI
S2
Pursue
Scanning around for opponent
Moving slowly forward and rotate periodically
Lock the opponent and ready to charge
Moving toward the target and release weapon
S3
Charge
Charge the opponent
Moving fast to target
S4
Retreat
Pushed back by the opponent
Moving backward fast and rotate fast
S5
Scan out
During scan, part of the robot is out of the platform
Moving backward and rotate to the opposing side
During combat, part of the robot is out of the platform
Moving forward and rotate to the opposing side.
Combat out
S6
Fig 6 Strategy state diagram of gladiator robot
One advantage of the state machine is the scalability. It can be easily expanded to implement much complex strategy by providing more states and more actions. Moreover, each state may contain sub-states that handle the situations more specifically. In this way, it allows some level of hierarchical design (Fujimura and Sarnet, 1989 ).
6. CONCLUSION & FURTHER RESEARCH In this paper, the sumo-wrestling robot competition has been firstly discussed, and then the design and development of two autonomous sumo-wrestling robots are presented. The robots make use of statemachine to describe the strategy of intelligence, which is very scalable and able to be expended to much more complex strategies for real-life situations. The sumo-wrestling competition provides a real-life testing arena for intelligence, survival chance and mission success, which are very important for the utmost deployment of autonomous robots in the battle fields. In making the system truly intelligent, research at the high abstract level will also be investigated. Representation and manipulation of information are to be investigated using the following most suitable tools: matter-element theory for non-compromising and non-compatible problems, neural networks, Bayesian belief networks, or fuzzy linguistic models for uncertain inputs. Variable hierarchical structures will be investigated in effective organizing the many basic behaviours at different levels.
The transition between states is triggered by some external events, i.e. sensor readings. • S I ~S2: Find opponent from the ultrasonic sensor. • •
S I ~S5: Detect one part is out of platform. S2~S3:
Detect
opponent
for
contact
sensors •
S2~S5:
•
S3~S4: Detect negative incremental encoder
•
S3~S 1:
Lose contact with opponent
•
S4~S I :
Lose contact with opponent
•
S5~S 1:
All parts are inside the platform.
•
S6~S3:
All parts are inside the platform.
Detect one part is out of platform speed
from
Further research will also be carried out dynamic path planning for mobile robots in a dynamic environment with targets and obstacles moving. This is a very challenging task, yet an essential part of an intelligent vehicle. The robots developed and the know-how gained will be very useful for the work in this direction.
Figure 6 shows the strategy used by the gladiator robot.
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7. REFERENCES
Function Method, Ph.D Dissertation, University of Missouri-Columbia.
Borenstein, J. and Koren. 1991. "The Vector Field Histogram --- Fast obstacle Avoidance for Mobile Robots", IEEE Trans. Robotic. Autom., 7(3): 278-288.
Khosla, P. and R. Volpe, 1988. "Superquadric Artificial Potentials for Obstacle Avoidance and Approach", Proc. IEEE Con! Robotic. Autom., pp.I778-1784.
Canny, J. F. and M. C. Lin, 1999. "An Opportunistic Global Path Planner", Proc. of IEEE Conf. Robotic. Autom., pp. 1554-1559.
Kim, J. O. and P. K. Khosla, 1992. "Real-time Obstacle Avoidance Using Harmonic Potential Functions", IEEE Trans. Robotic. Autom., 8(3): 338-349.
Chuang, J. H. and N. Ahuja, 1998, "An Analytically Tractable Potential Field Model of Free Space and Its Application in Obstacle Avoidance", IEEE Trans. Sys. Man. Cybernetics.-- Part B, 28(5): 729-736.
Ko, N. Y. and B. H. Lee, 1996. "Avoidability Measure :jn Moving Obstacle Avoidance Problem ~nd. , Its Use for Robot Motion Planning", Prlic. IEEEIRSJ Int. Con! Intell. Robots and Sys., Vol. 3, pp. 1296 -1303 .
Everett, H.R., 1995. Sensors for Mobile Robots: Theory and Applications, Wellesley, Mass.
Latombe, J. 1991. Robot Motion Planning, Kluwer Academic, Boston,
Fujimura, K. and H. Samet, 1989. "A Hierarchical Strategy for Path Planning Among Moving Obstacles", IEEE Trans. Robotic. Autom., 5(1): 61-69.
Ruan, Z.L., S.S. Ge and T.H. Lee, 2001 "Design and Implementation of an Embedded Controller System for Mobile Robot," Proceedings of Int. Con! on Computational Intelligence. Robotics and Autonomous Systems (c/RAS), pp. 182-187, Nov. 28-30, Singapore.
Ge S. S. and Y. J. Cui, 2000 "New Potential Functions for Mobile Robot Path Planning", IEEE Transactions on Robotics Automation, Vol. 16, No. 5, pp.615 -620.
Shih, C. L. and T. Lee, 1990. "A Unified Approach for Robot Motion Planning with Moving Polyhedral Obstacles", IEEE Trans. Sys. Man. Cyb., 20(4): 903-915.
Ge, S.S. and Y.J.Cui, "Dynamic Motion Planning for Mobile Robots Using Potential Field Method" To Appear in Autonomous Robots, Vol. 13, no. 3, 2002
Shiller, Z. and Y.R. Gwo, 1991. "Dynamic Motion Planning of Autonomous Vehicles", IEEE Trans. Robotic. Autom. I 7(2): 241-249.
Ge S.S., J. Wang, T.H. Lee and G.Y. Zhou, 2001, "Adaptive robust stabilization of dynamic nonholonomic chained systems," Journal of Robotic Systems, Vol. 18, no. 3, pp.119-133.
Sun, Z., S.S. Ge, and T.H. Lee, 2001, "Stabilization of nonholonomic chained systems via nonregular Feedback Linearization," Systems and Control Letters, vol. 44, pp. 279 -289.
Ge S.S., Z.P. Wang and T.H. Lee, 2002 "Adaptive stabilization of uncertain nonholonomic systems by output feedback," To appear in Proceedings of IFAC Conference on Mechatronic Systems, Berkeley, USA.
Veelaert, P. and W. Bogaerts, 1995. "Ultrasonic potential field sensor for obstacle avoidance", it IEEE Trans. Robotic. Autom., 15(4), pp. 774-779.
Gil de Lamadrid, J.F. and M. Gini, 1990, "Path Tracking Through Uncharted Moving Obstacles", IEEE Trans. Sys. Man, Cyb., 20(6): 1408-1422. Huo
Watanabe, K., 1998. "Control of an Omnidirectional Mobile Robot", Proceedinds of 2nd Int. Con! Knowledge-Based Intell. Electro. Sys., pp. 5160.
W. and S.S. Ge, 2001, "Exponential Stabilization of Nonholonomic Systems: An ENI Approach," International Journal of Control, Vo1.74, No.15, pp. 1492-1500.
Hussien, B, 1989. Robot Path Planning and Obstacle Avoidance by Means of Potential
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