CHAPTER 3 MULTI AGENT ROBOT CONTROL BASED ON TYPE-2 FUZZY AND ANT COLONY OPTIMIZATION

Transcription

1 41 CHAPTER 3 MULTI AGENT ROBOT CONTROL BASED ON TYPE-2 FUZZY AND ANT COLONY OPTIMIZATION 3.1 INTRODUCTION This chapter is to focus on an Agent based approach to Multi robot control using type -2 fuzzy and Ant colony optimization. Type -2 fuzzy interval controllers was applied to the autonomous robot in order to handle uncertainty in a better way and ant colony optimization technique has been used for an optimized path planning in traffic environment with more number of robots. Both Agents based and type-2 fuzzy together with Ant Colony Optimization technique is used to achieve second level of intelligence. Each sensor is treated as independent agent but it can be interconnected through interface agent. Old sensors can be taught new tricks to improve the level of intelligence of sensor agents. UV sensor/ir sensor with RF range of signals can be used to detect long distance objects. Camera based vision system act as a camera sensor agent to detect nearer objects. GSM modem can be used as another agent and hence it can be used to control the robot through SMS. GPS kit interfaced with GSM modem can be used to locate the position of the robot. All the sensor agents will offer additional intelligence/ functionality by integrating microprocessors or microcontrollers. The Main focus is on Multi Agent and type-2 fuzzy logic based approach to Multi Robot control. Since fuzzy is the best tool to address common challenges and incorporate human procedural knowledge into the

2 42 control algorithm, sensor agents are used to find objects with its distance from the robot. With camera to monitor the path, robot can be controlled through software by watching the video. Robot can also be controlled through SMS, position of the robot can be received by using hand set, GPS and GSM modem. Sensor agents are embedded with microcontroller to provide another level of intelligence (Efraim Turban and Aronson 2003). The type-2 fuzzy paradigm is a lesser known and a lesser exploited area of fuzziness. Type-2 fuzzy methods provide second order uncertainties allowing fuzzy systems to truly deal with real world uncertainty. Type - 2 fuzzy is applied to individual system and ant colony is applied to a group of systems (Colorni et al 1991). Ant Colony Optimization (ACO) is a meta heuristic for combinatorial optimization, and is a part of the Swarm Intelligence approach. ACO is used to plan the global route based on the static environment information (Tan Guan Zheng et al 2007).The total approach improves current system. GPS and GSM provide another level of security. 3.2 TYPE - 2 FUZZY LOGIC Type-2 fuzzy sets and systems generalize type-1 fuzzy sets and systems so that more uncertainty can be handled. From the very beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something that seems to contradict the word fuzzy, since that word has the connotation of lots of uncertainty. So, what does one do when there is uncertainty about the value of the membership function? The answer to this question was provided in 1973 by the inventor of fuzzy sets, (Zadeh 1973), when he proposed more sophisticated kinds of fuzzy sets, the first of which he called a type-2 fuzzy set. A type-2 fuzzy set lets us incorporate uncertainty about the membership function into fuzzy set theory, and is a way to address the above criticism of type-1 fuzzy sets head-on, and, if there is no uncertainty, then a type-2 fuzzy

3 43 set reduces to a type-1 fuzzy set, which is analogous to probability of reducing to determinism when unpredictability vanishes. In order to symbolically distinguish between a type-1 fuzzy set and a type-2 fuzzy set, a tilde symbol is put over the symbol for the fuzzy set. A denotes a type-1 fuzzy set, whereas à denotes the comparable type-2 fuzzy set. The membership function of a general type-2 fuzzy set, Ã, is threedimensional, where the third dimension is the value of the membership function at each point on its two-dimensional domain which is called as footprint of uncertainty (FOU). For an interval type-2 fuzzy set, third-dimension value is the same everywhere, which means that no new information is contained in the third dimension of an interval type-2 fuzzy set. So, for such a set, the third dimension is ignored, and only the FOU is used to describe it. It is for this reason that an interval type-2 fuzzy set is sometimes called a first-order uncertainty fuzzy set model, whereas a general type-2 fuzzy set (with its useful third-dimension) is sometimes referred to as a second-order uncertainty fuzzy set model. Figure 3.1 Type - 2 fuzzy logic

4 44 The FOU represents the blurring of a type-1 membership function, and is completely described by its two bounding functions (Figure 3.1), a lower membership function (LMF) and an upper membership function (UMF), both of which are type-1 fuzzy sets. Consequently, it is possible to use type-1 fuzzy set mathematics to characterize and work with interval type-2 fuzzy sets Interval Type-2 Fuzzy Logic Systems Type-2 fuzzy sets are finding very wide applicability in rule-based fuzzy logic systems (FLSs) because they let uncertainties to be modeled by them whereas such uncertainties cannot be modeled by type-1 fuzzy sets. A block diagram of a type-2 FLS is depicted in Figure 3.2. This kind of FLS is used in fuzzy logic control, fuzzy logic signal processing, rule-based classification, etc., and is sometimes referred to as a function approximation application of fuzzy sets, because the FLS is designed to minimize an error function. The following discussions, about the four components in rule-based FLS, are given for an interval type-2 FLS, because to-date they are the most popular kind of type-2 FLS; however, most of the discussions are also applicable for a general type-2 FLS (Figure 3.2). Rules: Are either provided by subject experts or are extracted from numerical data, are expressed as a collection of IF-THEN statements, e.g. IF temperature is moderate and pressure is high, then rotate the valve a bit to the right. Fuzzy sets are associated with the terms that appear in the antecedents (IF-part) or consequents (THEN-part) of rules, and with the inputs to and the outputs of the FLS. Membership functions are used to describe these fuzzy sets, and in a type-1 FLS they are all type-1 fuzzy sets, whereas in an interval type-2 FLS at least one membership function is an interval type-2 fuzzy set.

5 45 Figure 3.2 Fuzzy type 1 reductions An interval type-2 FLS lets any one or all of the following kinds of uncertainties are quantified: 1. Words that are used in antecedents and consequents of rules because words can mean different things to different people. 2. Uncertain consequents because when rules are obtained from a group of experts, consequents will often be different for the same rule, i.e. the experts will not necessarily be in agreement. 3. Membership function parameters because when those parameters are optimized using uncertain (noisy) training data, the parameters become uncertain. 4. Noisy measurements because very often it is such measurements that activate the FLS. In Figure 3.2, measured (crisp) inputs are first transformed into fuzzy sets in the Fuzzifier block because it is fuzzy sets and not numbers that activate the rules which are described in terms of fuzzy sets and not numbers. Three kinds of fuzzifiers are possible in an interval type-2 FLS. When measurements are:

6 46 Perfect, they are modeled as a crisp set; Noisy, but the noise is stationary, they are modeled as a type-1 fuzzy set; and, Noisy, but the noise is non-stationary, they are modeled as an interval type-2 fuzzy set (this latter kind of fuzzification cannot be done in a type-1 FLS). After measurements are fuzzified, the resulting input fuzzy sets are mapped into fuzzy output sets by the Inference block. This is accomplished by first quantifying each rule using fuzzy set theory, and by then using the mathematics of fuzzy sets to establish the output of each rule, with the help of an inference mechanism. If there are M rules, then the fuzzy input sets to the Inference block will activate only a subset of those rules, where the subset contains at least one rule and usually fewer than M rules. Inference is done one rule at a time. So, at the output of the Inference block, there will be one or more fired-rule fuzzy output sets. In a type-1 FLS, output processing, called Defuzzification, maps a type-1 fuzzy set into a number. There are many ways for doing this, e.g., compute the union of the fired-rule output fuzzy sets (the result is another type-1 fuzzy set) and then compute the center of gravity of the membership function for that set; compute a weighted average of the center of gravities of each of the fired rule consequent membership functions; etc. It is more complicated for an interval type-2 FLS to de defuzzify, since to go from an interval type-2 fuzzy set to a number (usually) requires two steps. The first step, called type-reduction, is where an interval type-2 fuzzy set is reduced to an interval-valued type-1 fuzzy set. There are as many type-reduction methods as there are type-1 defuzzification methods. An algorithm developed by Colorni et al (1991), now known as the KM (Karnik

7 47 Mendel)Algorithm is used for type-reduction. Although this algorithm is iterative, it is very fast. The second step of Output Processing, which occurs after typereduction, is still called defuzzification. Because a type-reduced set of an interval type-2 fuzzy set is always a finite interval of numbers, the defuzzified value is just the average of the two end-points of this interval. It is clear from Figure 3.2 that there can be two outputs to an interval type-2 FLS-crisp numerical values and the type-reduced set. The latter provides a measure of the uncertainties that have flowed through the interval type-2 FLS, due to the (possibly) uncertain input measurements that have activated rules whose antecedents or consequents or both are uncertain. Just as standard deviation is widely used in probability and statistics to provide a measure of unpredictable uncertainty about a mean value, the typereduced set can provide a measure of uncertainty about the crisp output of an interval type-2 FLS. 3.3 ANT COLONY OPTIMIZATION Ant Colony Algorithms are inspired by the behavior of natural ant colonies, in the sense that they solve their problems by multi agent cooperation using indirect communication through modifications in the environment. Natural, or real, ants release a certain amount of pheromone while walking, and each ant prefers (probabilistically) to follow a direction which is rich of pheromone. This simple behavior explains why ants are able to adjust to changes in the environment, such as new obstacles interrupting the currently shortest path. This can be illustrated as follows: When an ant colony following a shortest path between a food source and the nest (Figure 3.3) gets interrupted by an obstacle appearing somewhere in the path, when the ants reach the obstacle they will randomly choose some way around

8 48 it (right, left, over or under) (Figure 3.4). If we assume that the only way around the obstacle is either right or left, we can safely assume that approximately half of the ants will go right and the other half left, as illustrated below: Figure 3.3 Ant foraging behavior The ants that happen to pick the shorter path will obviously create a strong trail of pheromone a lot faster than the ones choosing a longer path (Figure 3.5). This will cause more and more ants to choose the shorter path until eventually all ants have found the shortest path. Figure 3.4 Ant moves in random Ant Colony Algorithms attempt somehow to apply similar techniques in order to solve real life problems. The main idea is to use

9 49 repeated and often recurrent simulations of artificial ants (mobile agents inspired by real ant behavior) to generate new solutions to the problem at hand. The ants use information collected during past simulations to direct their search and this information is available and modified through the environment. Many different artificial ant algorithms have been implemented and no universal definition of an artificial ant fits them all. Figure 3.5 Ant moves in the shortest path 3.4 MULTI AGENT SYSTEMS A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Examples of problems which are appropriate to multi-agent systems research include online trading, disaster response, and modeling social structures (Figure.3.6) characteristics: The agents in a multi-agent system have several important

10 50 Autonomy: the agents are at least partially autonomous Local views: no agent has a full global view of the system, or the system is too complex for an agent to make practical use of such knowledge Decentralization: there is no designated controlling agent (or the system is effectively reduced to a monolithic system) Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined human-agent teams. Figure 3.6 Multi agent architecture System Block Diagram Figure 3.7 shows the block diagram of the system. Tachometer is used to get the speed of the robot in ground. GPS will gives the position of the

11 51 robot. GSM modem is used to communicate with the robot through SMS. Ultrasonic sensor is used to detect the far object, and from the transmitting and receiving pulse time, distance of the object from the robot was calculated. Totally this ultrasonic sensor with a microcontroller is considered as sensor agent. Camera is used to get the near field video, and the video information can be sent to any laptop using (Ben Shneiderman 2007) wireless LAN. Hence watching the video robot can be controlled through software in the laptop. Figure 3.7 System architecture There is a stepper motor to control the steering wheel and a DC motor to make control over brake of the vehicle. Now the heart of the system is known as guidance system, which basically gets information from all the sources/agents and take decision or give control signal. Here we apply Ant colony optimization algorithm and this section is basically treated as interface agent with a high speed processor.

12 Camera Based Control Figure 3.8 shows the basic block diagram of camera based control.here the wireless LAN is used collect the video from the robot. Video can be stored in the database for future reference, at the same time real-time video will be displayed. Now the robot can control through software by watching the video. Control signal again has to go through wireless LAN. Figure 3.8 Block diagram of camera based control Figure 3.9 GPS and GSM based tracking

13 GPS and GSM Based Tracking/ Control Figure 3.9 shows GPS and GSM based robot tracking. In this system GPS module and modem with microcontroller will be in robot. The position of robot will be available on second handset. Also robot can be controlled / moved to any track through SMS. 3.5 IMPLEMENTATION ISSUES The proposed system has basically a multi-agent architecture, and is going to be a better application of embedded system. So the internal RAM becomes a challenge over here, because of complex algorithm like Ant Colony Optimization as optimization technique. Microcontroller used is 89C51 with built in flash memory. The collision avoidance technique used is edge detection methods with some enhancement Multi-Agent System Using Interface Agent Figure 3.10 depicts the simplified diagram representing the multiagent system using an interface agent. The agents basically interact with the other components of the system by manipulating information on the interface agent. The information on the interface agent may represent facts, assumptions, and deductions made by the system during the course of solving the problem. The system has five independent agents: Sensor agent, Camera agent, GSM modem agent, Wireless LAN agent and Motor Drive agent. And some agents are allowed to read / write information on the interface agent. Each one of the five agents executes their tasks independently using information on interface agent and posts any result back to the interface agent.

14 54 Figure 3.10 Multi agent system using interface agent Intelligent Agent An Intelligent Agent is a Computer Program that help a user with routine Computer tasks A human Agent represents a person and interacts with others to accomplish a predefined task. Intelligent agent is a software entity that carries out some set of operations on behalf of user or another program with some degree of independence. Autonomous Agents are Computational systems inhabit some complex dynamic environment, sense and act autonomously in this environments.

15 Teaching an Old Sensor with New Methods Intelligent or smart sensors traditionally refer to sensors offering additional functionality Level of Intelligence provided by the integration of microprocessors, microcontrollers or application specific integrated circuits within the sensing element itself. We consider an intelligent sensor to be a system of primary sensing elements and associated software modules acting as a single entity. The software architecture addresses the following issues (Dimosthenis Karatzas et al 2007). Specifies the functionality of the software modules. Defines how the modules are interconnected. Describes how data propagate between the modules. Explain how additional, specialized software modules can be incorporated to tackle application specific tasks. Here we achieved second level of intelligence. There are 4 levels of intelligent Level 0 -> these agents retrieve documents for a user, under straight order. Eg: Web Browser Level 1-> these agent provide user initiated searching facility for finding relevant web pages. Eg :Search engine

16 56 Level 2 -> These agents maintain user profiles, then they monitor internet information and notify the users whenever relevant information is found. Eg : Web watcher Level ->3 Agents at this level have a learning and deductive component of user profile to help a user who cannot formalize a query or specify a target for a search Eg: DiffAgent(CMU) Collision Avoidance Technique- Enhanced Edge-Detection Method One of the popular obstacle avoidance methods is based on edgedetection. In this method, an algorithm tries to determine the position of the vertical edges of the obstacle and then steer the system around either one of the "visible" edges. The line connecting two visible edges is considered to represent one of the boundaries of the obstacle. This method was used in very early research (Borenstein and Koren 1989), as well as in several other works (Borenstein and koren 1989), using ultrasonic sensors for obstacle detection.a disadvantage with implementations of this method is that the system stops in front of obstacles to gather sensor information. However, this is not an inherent limitation of edge-detection methods; it is possible to overcome this problem with the use of intelligent sensor (ie sensor integrated with microcontroller). In this system all sensors are intelligent agents. Here we are using a ring of seven ultrasonic sensors. Each sensor is separated by an angle of 30 degree. Figure 3.11 shows the sensor ring, direction of the object can be obtained from the angle of particular sensor. Distance of the object from the system will be calculated from the time of transmitting and receiving pulse. Distance is then categorized as near or medium or far, depending on which decision can be taken. We use a microcontroller to control the sensors and will also run enhanced edge detection algorithm.

17 57 Figure 3.11 Sensor ring Type-2 Fuzzy Sets A type-2 fuzzy set (Arkin 1987) like a type-1 fuzzy set contains elements that belong to a degree. In a type-2 fuzzy set the degree of belonging of an element is expressed as a type-1 fuzzy number within that (Dicekerson and Kosko 1994). This means the degree to which an element belongs to the set is uncertain, that is it is characterized by a possibility distribution. Type-2 fuzzy inference systems use IF-THEN rules to make inferences from a given set of variables and a rules base Planning Versus Reactivity Early robot architectures followed the classical Artificial Intelligence model of sense-plan-act. This involved constructing and maintaining a world model. A reasoning process using current perceptions and the world model then decides upon an action to be taken, which is then implemented on by the actuators. This planning methodology is in essence a horizontal decomposition of the mobile robot problem (Figure 3.12).

18 58 Inputs From Sensors Perceptions Modelling Planning Task Execution Motor Control Outputs To Actuators Figure 3.12 Planning methodology Figure 3.13 Working environment Consider the Figure 3.13 The robot has come across an obstacle and will turn to move in either of two possible directions. The obstacle avoidance behavior, if left to its own devices would choose direction 1 as the obstacle and is slightly to the left of the robot. The goal seeking behavior however wants to turn to direction 2 as this takes the robot toward the goal. The architecture however requires the behaviors to not only state the most desirable direction, but to state the desirability of every suggested direction. Obstacle avoidance finds direction 2 desirable, but direction1 slightly more desirable. Goal seeking finds direction 2 desirable but direction 1 undesirable.

19 59 Command fusion will take these into account and in this case decide upon direction 2. This process is however a fuzzy process, as such these directions are stated as fuzzy sets. Once command fusion has taken place, the final command must be defuzzified. At this point it would be possible for the crisp value to fall between the two directions, causing the robot to travel straight ahead and crash into the obstacle. The context rules used in the command fusion process are there to prevent events such as this from happening (Dubois and Prade 1982). The overall control process is depicted in Figure The reactive architecture used is command fusion and then the fuzzy extension of command fusion. Figure shows how reactive vehicle control architectures have been developed and how fuzzy methods have been integrated in this area. Figure 3.14 The control process for the extended command fusion method

20 Ant Colony Optimization (ACO) for Robot Navigation ACO (Dorigo et al 1999) is a class of algorithms, whose first member, called Ant System, was initially proposed by (Colorni et al 1991).The main underlying idea, loosely inspired by the behavior of real ants, is that of a parallel search over several constructive computational threads based on local problem data and on a dynamic memory structure containing information on the quality of previously obtained result. The collective behavior emerging from the interaction of the different search threads has proved effective in solving combinatorial optimization (CO) problems, we use the following notation (Maniezzo 1999). A combinatorial optimization problem is a problem defined over a set C = c 1, c 2..., c n of basic components. Given this, the functioning of an ACO algorithm can be summarized as follows. A set of computational concurrent and asynchronous agents (a colony of ants) moves through states of the problem corresponding to partial solutions of the problem to solve. They move by applying a stochastic local decision policy based on two parameters, called trails and attractiveness. By moving, each ant incrementally constructs a solution to the problem. When an ant completes a solution, or during the construction phase, the ant evaluates the solution and modifies the trail value on the components used in its solution. This pheromone information will direct the search of the future ants. Figure 3.15 shows how ants avoid obstacle ant take a decision. In this system treat each agent as an ant, the information from each ant can apply in optimization algorithm (Mohamad et al 2006).

21 61 Figure 3.15 Ant system structure for robotic navigation Perceptions-To-Action Loop Figure 3.16 shows the basic principle and action of overall system, i.e. system learns the environment through sensors, then performs the computation and act accordingly. Figure 3.16 Perceptions-to-action loop

22 RESULTS AND DISCUSSION For a test drive and experimentation a three wheel demo model has been assembled. The back two wheels are connected DC motor and front Wheel is a free rotating wheel. The demo model is available in Appendix 1. Figure 3.17 shows how the robot moves with direction desirability selection for far obstacle and near obstacle. Here if obstacle is far then goal will be treated as direction desirability. The reason is that, it is not emergency to avoid far obstacle, because in dynamic environment obstacle itself may be moving away. If obstacle is near it is urgent to avoid obstacle, so in this case obstacle avoidance is treated as direction desirability. Figure 3.17 Photograph of real time direction desirability selection In Figure 3.18 shows the Performance Evaluation the system,the parameters chosen are sensor output, distance between the robots, speed, motor input for particular time period. These measures shows, how the robot avoid an obstacle, or what happen if the system met with an obstacle. The sensor output shows 5 V when it detects an obstacle. Motor speed is kept normal for Once the obstacle (Robot) comes 20cm (only for testing purpose), then speed reduced, i.e., the robot slowdown and take a deviation. After the robot avoided the obstacle and it will come to normal speed.

23 63 Figure 3.18 Performance evaluation of multi robot system In Table 3.1 shows the comparative performance of type-1 fuzzy logic robot navigation with Type - 2 and ant colony optimization, response time for Type -2 system is much lower than the type-1 fuzzy system.

24 64 Table 3.1 Comparative study of robot response time for fuzzy and type-2 fuzzy Memory (Kb) Fuzzy and AVH Time in sec for Unknown environment ACO and Type-2 fuzzy Time in sec for Unknown environment CONCLUSION The multi-agent and type 2 fuzzy with intelligent sensor approach provides an extra level intelligence. Here almost we achieved level 3intelligence. Ant colony optimization is very effective in global path planning.intelligent sensor and enhanced edge detection method with sensor ring provides a better result in obstacle detection. Camera based control act as a better means for near objects. The use of Wireless LAN and GSM modem gives a good remote access/control over the system. GPS based positioning can provide another level of security for the system.