Frontiers in Robotics, Automation and Control Part 14 pdf

Development of Rough Terrain Mobile Robot using Connected Crawler -Derivation of sub-optimal number of crawler stages- 383 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 100 200 300 400 500 Num ber of G enerations Step Height [m] 2 stages 3 stages 5 stages 6 stages 7 stages 8 stages 9 stages 10 stages 4 stage s Fig. 8. Transition of the climb-able step height derived by GA (2 ~ 10 stages) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 12345678910 Number of stages Step Height [m] Fig. 9. Relationship between the number of stages and climb-able step height Frontiers in Robotics, Automation and Control 384 2.4 Introducing expected value to climb a step We introduce an expected value to climb a step to clarify the sub-optimal number of stages. Although the mobility improves by increasing the number of stages, failure probability of system also increases, because a connected crawler mechanism is one of the complex mechanical systems. It is considered that the relation between mobility and the number of stages is trade-off relation. Therefore, by introducing expected value which contains failure probability, sub-optimal number of stages is derived. To derive the expected value, a certain probabilistic values P and the maximum step height h max of each number of links are needed. PhE × = max (3) This certain probabilistic value P shows the probability to climb a step. Then we adopt the robot availability (rate of operation) as this certain probabilistic values. How can we derive the availability of the system? This problem is categorized into the field of reliability engineering. And it is almost impossible to derive availability of a complex system like a robot precisely. Therefore there is a Fault Tree Analysis for deriving availability of such complex system. Fault Tree Analysis is a method to analyze faults and troubles, and is called FTA. For analyzing frequency of troubles, this method traces the risk of the cause theoretically and adds each probability of trouble. This method is one of the top down analysis method. The failure probability is derived by following steps. 1. First the undesirable event is defined. 2. The cause of the undesirable event is extracted. 3. The FAULT TREE is generated by using logic symbol. 4. The each failure probability is assigned. The value which derived by FTA is the failure probability of the system. Then the availability P a is derived following relationship between failure probability P f and availability P a . fa PP − = 1 (4) In order to derive availability, we set following assumption for robot conditions. z The joint has an optical encoder and a DC motor. z The motor for driving a crawler is in each link. z The failure probability of the optical encoder is 0.0155. z The failure probability of the DC motor is 0.00924. Mentioned failure probabilities above are determined by the reference (C. Carreras et al,2001). From the view point of availability engineering the Fault Tree of the connected crawler is shown in Fig. 10. Development of Rough Terrain Mobile Robot using Connected Crawler -Derivation of sub-optimal number of crawler stages- 385 ROBOT FA ILU R E C1 FA ILS J1 FA ILS CM 1CM 1 CS1 JS1 JM 1 C2 FA ILS J2 FA ILS CS2 JS2 JM 2 CM 2 CN FA ILS JN- FA ILS 1 CSN JSN- 1 JM N- 1 CM N Fig. 10. Fault Tree for n-stages connected crawler robot Here, J means joint, C is a crawler, M means DC motor, S means optical encoder. Therefore MJ1 refers to the DC motor on joint J1. SJ1 refers to the optical encoder on joint 1. CM1 represents the DC motor for driving crawlers on link one, C1. AlsoCS1 means the optical encoder on link one. By combining these value using OR logic, the failure probabilities of link 1 system or joint 1 system are derived. And each failure probability of joint and crawler is combined by OR logic, then the total failure probability of the robot is derived. By using Fig. 10, the availabilities of connected crawler robot are derived which are shown in Fig. 11. 0 0.2 0.4 0.6 0.8 1 12345678910 Num ber of links Availability Fig. 11. The availability of each number of stages on connected crawler robot. 2.5 Sub-optimal number of stages Previous section showed the availability of each number of stages in Fig. 11. Therefore the expected value of climbing a step can be now derived by using equation (3). Fig. 9. is used for h max . The results are show in Fig. 12. From Fig. 12., it turned out that the peak of expected value of connected crawler is from 2 to 5 links. In case of more than 6 links, the expected value is decreased. Therefore the sub- optimal number of stages is 2 to 5. Frontiers in Robotics, Automation and Control 386 0 0.2 0.4 0.6 0.8 1 1.2 1.4 12345678910 N um ber of links Expected V alue Fig. 12. Expected value of connected craweler 3. Constructing the prototype In the previous section, we have been able to obtain the sub-optimal number of crawler stages, that is 2 to 5. Based on this conclusion, we have designed and developed the prototype of connected crawler robot. It is shown in Fig. 13. The length is 0.59 m, width is 0.130 m, mass is 1.28 kg. 0.59[m] 0.13[m] Fig. 13. Prototype of connected crawler robot Development of Rough Terrain Mobile Robot using Connected Crawler -Derivation of sub-optimal number of crawler stages- 387 3.1 Mechanical structure Our mechanism has 5 connected stages with the motor-driven crawler tracks on each side (Fig. 14). RC-servo motors are used for driving joints between the stages. The left and right crawlers are driven by 4 DC motors independently, while the 5 crawlers on each side are driven by a motor simultaneously. The output of each motor is transmitted to the sprockets of the three or two crawlers through several gears (Fig.15). RC servo for joints Motors for crawler Fig. 14. The driving structure (Color indicates driving relationship between motors and crawlers) Fig. 15. Transmission of motor outputs to the crawlers 3.2 Control structure The control architecture is hierarchical structure by connecting master controller and servo unit (Fig .16, and Fig. 17). The servo units control low level task: crawler velocity and joint angle by PID control law. Each servo unit consists of one microcontroller (PIC16F873) and 2 DC motor drivers (TA8440H). One microcontroller is installed to control two RC-servo units for the joint control, where RC-servo is controlled only by PWM signal. Master controller controls high level task: such as calculating robot trajectory. Table.3 shows the communication data format. The command sent by master controller consists of 3 bytes. First byte indicates mode ID and motor ID. The mode ID distinguishes 2 kinds of control modes: position control and velocity control. The motor ID is used for selecting motor to control. Second byte shows the data depends on control modes. The third byte is checksum. Frontiers in Robotics, Automation and Control 388 Fig. 16. The control system Servo unit for crawlers S ervo unit for joints Fig. 17. The servo units 1 byte 2 byte 3 byte Data 1 Data 2 Check Sum 7 6 5 4 3 2 1 0 Mode=0~2 ID=0~7 0~254 Data1 | Data2 Table 3. Communication data format 4. Experiments The climbing step experiment is conducted to verify the performance of our prototype. The height of step is 0.23 m. The master controller sends instructions to each actuator through servo units. Li-Polimer battery (1320mAh, 11.1V) is embedded to the robot for supplying electric power. In this experiment, PC is used as master controller. The USB cable is used for connecting robot to PC. The result is shown in Fig. 18. As we can observe, the robot can climb up a step. Therefore the mobility of this robot is confirmed. Development of Rough Terrain Mobile Robot using Connected Crawler -Derivation of sub-optimal number of crawler stages- 389 5. Conclusion This chapter showed sub-optimal number of crawler stages for connected crawler robot, through deriving the relationship between the number of stages and maximum climb-able step height and expected value to climb a step. After that, it proposed the actual connected crawler robot, and indicated basic experimental result. The conclusions of this chapter are as follows. z A joint angle function was approximated by Fourier series and parameters were searched by GA. z Due to fusion of GA and ODE, it has been possible to consider the interactions between robot and environment. z The relationship between the number of crawler stages and mobility performance was cleared. z Though mobility performance was raised by increasing the number of stages. However its increasing rate was small in comparison between before 5 stages and after 6 stages. z To clarify sub-optimal number of stages, the expected value to clime a step was introduced. z The peak of expected value is from 2 to 5 links. z Therefore the sub-optimal number of stages is 2 to 5. z By basic experimental results, the mobility of the prototype was confirmed. Fig. 18. Experimental results Frontiers in Robotics, Automation and Control 390 References C.H. Lee, S. H. Kim, S. C Kang, M.S.Kim, Y.K. Kwak (2003). ”Double –track mobile robot for hazardous environment applications”, Advanced Robotics, Vol. 17, No. 5, pp 447-495, 2003 K. Osuka, H. Kitajima (2003). "Development of Mobile Inspection Robot for Rescue Activities:MOIRA", Proceedings of the 2003 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, pp3373-3377, 2003 Mohammed G.F.Uler (1997). "A Hybrid Technique for the Optimal Design of Electromagnetic Devices Usign Direct Search and Genetic Algorithms"IEEE Trans. on Magnetics, 33-2, pp1931-1937, 1997 R. Smith, "Open Dynamics Engine", http://ode.org/ S. Hirose (2000). "Mechanical Designe of Mobile Robot for External Environments", Journal of Robotics Society of Japan, Robotics Society of Japan, vol.18, No.7, pp904-908, 2000 (in Japanese) S. Kawaji et al, (2001). "Optimal Trajectory Planning for Biped Robots"The Transactions of the Institute of Electrical Engineers of Japan. C, vol.121, No.1, pp282-289, 2001 (in Japanese) S. Kobayashi et al, (1995). "Serarch and Learning by Genetic Algorithms"Journal of Robotics Society of Japan, vol.13, No.1, pp57-62, 1995 (in Japanese) T. Inoh et al (2005). "Mobility of the irregular terrain for resucue robots"10th Robotics symposia pp 39-44, 2005 (in Japanese) T. Takayama, et al (2004). Name of paper. "Development of Connected Crawler Vehicle "Souryu-III" for Rescue Application "Proc. of 22nd conference of Robotics Society of Japan CD-ROM, 3A16, 2004 (in Japanese) Y. Yokose et al (2004). "Minimization of Dissipated Energy of a Manipulator with Coulomb Friction using GA Increasing the Calculated Genetic Information Dynamically" Transaction of JSCES, Paper No.20040024, 2004 (in Japanese) Y.Yokose V.Cingosaki, H.Yamashita (2000). "Genetic Algorithms with Assistant Chromosomes for Inverse Shape Optimization of Electromagnetic devices" IEEE Trans. on Magnetics, 36-4, pp1052-1056, 2000 C. Carreras, I. D. Walker (2001). ”Interval Methods for Fault-Tree Analysis in Robotics”, Transaction on Reliability, Vol. 1, pp. 1-11, 2001 21 Automatic Generation of Appropriate Greeting Sentences using Association System Eriko Yoshimura 1 , Seiji Tsuchiya 2 , Hirokazu Watabe 1 , Tsukasa Kawaoka 1 1 Doshisha University, 2 The University of Tokushima 1,2 Japan 1. Introduction When we humans start a conversation, we are greeting at first. If computer and robot are greeting like us, they can communicate smoothly with us because the next subject comes easily after greeting. That is to say, greeting conversation plays an important part to smooth communications in speaking. In this report, we describe a method of increase the number of appropriate greeting sentences for conversation and selecting sentences based on the situation by machine. Many of conversation system tend to use templates. Lots of chatter bots (Eliza, A.L.I.C.E., Ractor, Verbot, Julia etc.) have been developed. For example, Eliza(Weizenbaum, J 1965) which is one of the well-known system acts for counselling by a personification therapist agent. Eliza does not evaluate an answer of a partner for the reply. It memorizes only a part of the content that the partner spoke in the past and replies by using the word. It is prepared for several kinds patterns about the topic. Like these, as for the natural language processing, task processing type conversation (e.g. automatic systems for tourist information and reservations) becomes the mainstream. However, even under the limited situation, it is known that it is difficult to make a knowledge base of all response case. Moreover, a method using only the prepared template makes monotonous reply and a reply except sentences made by a designer don't appear. So, to make various sentences automatically by machine is important, more than the method to select sentences designer prepared. We herein propose an intelligent greeting processing by which a machine generates various reply sentences automatically by obtaining information about the surrounding state and then generating the best conversation response based on the situation. All sentences are extended automatically from a small quantity of model sentences by using the concept base which is kind of natural-language ontology/concept networks. Simply mechanical extension of conversation sentences makes many improper sentences. So, the proposed method uses language statistics information to delete the improper sentences. In addition, for greetings conversation, we suggest "status space" expressing a certain situation. This is a model to give a weight to sentences automatically by taking the consequence at two Frontiers in Robotics, Automation and Control 392 points of view that the appropriate selection that a human being performs unconsciously is classified in. 2. Requirements for Conversation Sentence The greeting conversation is a “no insistence conversation” that does not cause argument or discussion. In the present paper, such greeting conversation sentences are synthesized automatically by machine. The requirements for automatic conversation sentence synthesis by machine as follows: 1) Grammatical consistency 2) No contradiction in meaning 3) The use of usual words 4) Situation adaptability “Grammatical consistency” refers to sentences in which no grammatical mistakes are found. “No contradiction in meaning” refers to sentences that have a reasonable meaning, e.g., the sentence “The sun is so bright tonight.” is not reasonable from the point of view of time. “The use of usual words” indicates words used in daily life, including colloquialisms. “Situation adaptability” refers to sentences that do not contradict reality. For example, "It's a rainy today" contradicts the reality if the weather is fair. Therefore, it is necessary to meet these requirements after a mechanical synthesis. The proposed system is constructed using the Japanese language and so is adapted to the characteristics of the Japanese language and Japanese culture. 3. Greeting Conversation System Figure 1 shows the structure of the greeting conversation system proposed in the present paper. The greeting conversation system obtains inputs of the surrounding information and input sentence and then outputs greeting sentences. There are fixed pattern greetings, e.g., “Good morning” and “Hello” and greetings for starting a conversation, e.g., “It’s been raining all day.” and “It’s very hot, isn’t it?”: Human: “Good morning.” System: “Good morning. It’s very hot, isn’t it?” The former output is a fixed pattern greeting, the system can output sentence matching of the situation or input-sentences by a fixed pattern knowledge base. This problem can easily be solved. Thus, the latter greeting for starting a conversation is considered in the present paper. Greeting sentences indicate the latter. The proposed method extends the small-scale template database of greeting sentences. Suitable sentences are then selected automatically from the extension template considering the situation. This is achieved by using an association knowledge system that will be described later herein. [...]... public static final int max (final int v1, final int v2, final int c2){ if (v1+v2 > c2) return v1+v2-c2; return 0; } public static final int min (final int v1, final int v2, final int c2){ if (v1+v2 < c2) return v1+v2; return c2; } } Fig 1 The water jugs domain definition Remark: An external function can be written in any host programming language in this example we show functions written in java language,... the definition of the water jugs domain using the above extensions to the planning domain definition language Example-1: The water jugs domain definition using condition list and external functions (define (domain Jugs) (:requirements :typing :fluents :external) (:types jug int) (:functions ((capacity ?j - jug) – int) ((fill ?j - jug) – int)) (:external java (and ( (path (int max (int, int, int)) (c:/javaplan/WaterJug.max))... the original model sentence “It’s a beautiful mountain” produces the extension sentence “It’s a beautiful climax.” In Japanese, the word mountain has many meanings, including mountain, climax, and important event In this case, the original element word, mountain expresses the meaning ‘mountain’ Climax is an extension of mountain, but the extension is to a meaning that differs from the original element... PDDL3.1 version) Inspiring from the continuous extensions to PDDL, we propose our first extension that concerns the data representation, in which we introduce the concept of using non-invertible functions to update the numerical and nonnumerical data throughout a planning process This type of functions allows the integration and the handling in an easy way of uncertainty as well as of temporal and numerical... planning problems 2 Apply functions in planning Using functions in planning has been studied in Functional Strips (Geffner, 1999) and FSTRIPS (Schmid et al., 2002) Functional Strips has argued that the generated literals can be reduced by replacing relations by functions We are still supporting this idea in our extension, but our main interest in functions is their ability to handle complex numerical and. .. become impossible For this reason, we propose an incremental instantiation of actions for numeric values To allow incremental instantiation, we introduce the concepts of implicit 408 Frontiers in Robotics, Automation and Control parameters and implicit conditions in order to maintain the precondition edges of the actions that relate them to numeric facts Definition-3: A numeric variable that belongs to... process Therefore, if Graphplan fails to find a set of consistent action instances at a certain level and backtracking becomes useless, then Graphplan expands an additional planning graph level and restarts its search until finding a solution or until reaching a saturated graph that can not be expanded any more 4.2 Graphplan adaptation for integrating external functions In algorithm-1 we present the adaptation... starts by calling an initialization subroutine (see Fig.2) in which the construction of the planning graph begins by setting the first fact level to the initial state of the planning problem, then by testing if the goal conditions are satisfied at the initial state to return an empty plan, otherwise the algorithm instantiates the propositional variables of all the actions of the planning domain initialization()/*... written in a different programming language like the C++ for example A special parameter should be set to allow the planner to know which interpreter should call to execute the functions as it is the case for functions written in Java or if the functions are directly executable as for functions written in C or in C++ Frontiers in Robotics, Automation and Control 406 3.3 Planning problem definition... existing numeric variables in N(s) 3.2 Action definition An action a is defined as a tuple (args, con, pre, eff), where: args is the list of arguments made by variables which represent constant symbols in S and/ or numeric variables in N con is the list of constraints The constraints are tested before the instantiation of the actions to avoid instantiating actions with incoherent arguments Constraints . herein propose an intelligent greeting processing by which a machine generates various reply sentences automatically by obtaining information about the surrounding state and then generating. proposed in the present paper. The greeting conversation system obtains inputs of the surrounding information and input sentence and then outputs greeting sentences. There are fixed pattern greetings,. integration and the handling in an easy way of uncertainty as well as of temporal and numerical knowledge into planning. As non-invertible functions can be only applied in forward traversal in a search