A method to manage the transition from the principle solution towards the controller design of advanced mechatronic systems

Cheng Yee LowPaderborn, Univ., Diss., 200

Mobile Robot Path Planning using Q-Learning with Guided Distance and Moving Target Concept

Classical Q-learning algorithm is a reinforcement of learning algorithm that has been applied in path planning of mobile robots. However, classical Q-learning suffers from slow convergence rate and high computational time. This is due to the random decision making for direction during the early stage of path planning. Such weakness curtails the ability of mobile robot to make instantaneous decision in real world application. In this study, the distance aspect and moving target concept were added to Q-learning in order to enhance the direction decision making ability and bypassing dead end. With the addition of these features, Q-learning is able to converge faster and generate shorter path. Consequently, the proposed improved Q-learning is able to achieve average improvement of 29.34-94.85%, 18.29-29.69% and 75.76-99.50% in time used, shortest distance and total distance used, respectively

Modified Q-learning with distance metric and virtual target on path planning of mobile robot

Path planning is an essential element in mobile robot navigation. One of the popular path planners is Q-learning – a type of reinforcement learning that learns with little or no prior knowledge of the environment. Despite the successful implementation of Q-learning reported in numerous studies, its slow convergence associated with the curse of dimensionality may limit the performance in practice. To solve this problem, an Improved Q-learning (IQL) with three modifications is introduced in this study. First, a distance metric is added to Q-learning to guide the agent moves towards the target. Second, the Q function of Q-learning is modified to overcome dead-ends more effectively. Lastly, the virtual target concept is introduced in Q-learning to bypass dead-ends. Experi�mental results across twenty types of navigation maps show that the proposed strategies accelerate the learning speed of IQL in comparison with the Q-learning. Besides, performance comparison with seven well-known path planners indicates its efficiency in terms of the path smoothness, time taken, shortest distance and total distance used

Design an Interfacing Tracking System in Rehabilitation Therapies Between The Elbow Joint of The Human Arm and The Prosthetic Arm

Myoelectric prostheses have seen an increased application in clinical practice and research, due to their potential for good functionality and versatility. Yet, myoelectric prostheses still suffer from a lack of intuitive control and haptic feedback, which can frustrate users and lead to abandonment. To address this problem, a prosthetic arm was designed to help the amputees, who unfortunately lost their upper limb. Then, the prosthetic arm was equipped with a hybrid haptic feedback stimulation system to compensate for the missing sensation and enable the amputees to easily perform their normal life activities. The tracking system between the elbow joints of the human and the prosthetic arms was required to accomplish the experimental tests with the able-body subjects. Accordingly, this study is a platform for the main project. The major problem is to synchronize the movements of the prosthetic arm’s elbow joint with the human arm’s elbow joint within a high response, acceptable accuracy, and low error. Therefore, the PID controller was used to control the tracking system and the flexible bending sensor was attached to the volunteer’s elbow joint to record its rotational movements. The results verified the functionality of the proposed tracking system to synchronize the joints movements and enable the prosthetic arm to follow the movements of the volunteer’s arm within 0.062 sec. Finally, the effectiveness of the proposed elbow joints tracking system to synchronize the motions of the volunteer and he prosthetic arms was concluded. &nbsp

Mobile robot path planning using q-learning with guided Distance

In path planning for mobile robot, classical Q-learning algorithm requires high iteration counts and longer time taken to achieve conver-gence. This is due to the beginning stage of classical Q-learning for path planning consists of mostly exploration, involving random di-rection decision making. This paper proposed the addition of distance aspect into direction decision making in Q-learning. This feature is used to reduce the time taken for the Q-learning to fully converge. In the meanwhile, random direction decision making is added and activated when mobile robot gets trapped in local optima. This strategy enables the mobile robot to escape from local optimal trap. The results show that the time taken for the improved Q-learning with distance guiding to converge is longer than the classical Q-learning. However, the total number of steps used is lower than the classical Q-learning

Transmission Shaft Performance Using Static Simulation for Brushing Simulator

The brushing simulator assists researchers and dentists in conducting investigations on human teeth and plaque removal. In this paper, the development of a brushing simulator is studied. Which focuses on the life expectancy of shaft material used in the development process in the future. The aim of this study is to analyse the stress distribution of the aluminium and brushing simulator’s stainless steel threaded shafts. Besides, to analyse the brushing simulator’s threaded shaft life expectancy. The motor speed of Set 1 can be modified to 450 rpm, 480 rpm, 510 rpm, or 540 rpm. Meanwhile, speeds for set 2 are 550 rpm, 580 rpm, 610 rpm, and 640 rpm. In this study, Solidworks software was used to construct a brushing simulator model and obtain the result of stress distribution in a static simulation. The life expectancy of the aluminium and stainless-steel threaded shafts was determined from the design calculation method by using the simulation data. The threaded shaft life expectancy result showed that stainless steel is more durable than aluminium which is 3522 hours from set 1 compared to 728 hours at 640 rpm for the aluminium threaded shaft. Based on its material properties, the findings indicate that stainless steel is stronger than aluminium. Furthermore, the study shows that life expectancy at speeds below 550 rpm is higher than at speeds above 550 rpm. Hence, the life expectancy of a threaded shaft decreases as the speed increases

Adaptive Controller Algorithm for 2-DOF Humanoid Robot Arm

AbstractA computational model of human motor control for a nonlinear 2 degrees-of-freedom (DOF) robot arm to mimic humanlike behavior is developed and presented in this paper. The model is based on a simple mathematical model of a 2-segment compound pendulum which mimics the human upper arm and forearm. Using the Lagrangian and Euler-Lagrange equations, the 2-DOF dynamic equations were successfully derived and solved using Euler's method. Two types of controllers; a feedback Proportional-Derivative (PD) controller and a feedforward controller, were combined into the model. The algorithm exhibited learning of the necessary torque required in performing the desired Position Control via Specific Trajectory (PCST) rehabilitative task via feedback control and using it as the feedforward torque in subsequent trial motions. After 30 trials, the mean absolute error with respect to the desired motion of the upper arm, showed a decrease from 0.09533 to 0.005859, and the forearm motion from 0.3526 to 0.006138. This decrement trend in mean absolute errorwith increase in number of trials is consistent with the adaptive control strategy of the human arm known as the Feedback Error Learning (FEL) strategy

Strategy planning for collaborative humanoid soccer robots based on principle solution

The final publication is available at Springer via http://dx.doi.org/10.1007/s11740-012-0416-4[EN] Collaborative humanoid soccer robots are currently under the lime light in the rapidly advancing research area of multi-robot systems. With new functionalities of software and hardware, they are becoming more versatile, robust and agile in response to the changes in the environment under dynamic conditions. This work focuses on a new approach for strategy planning of humanoid soccer robot teams as in the RoboCup Standard Platform League. The key element of the approach is a holistic system model of the principle solution encompassing various strategies of a soccer robot team. The benefits of the model-based approach are twofold¿it enables intuitive behavioral specification of the humanoid soccer robots in line with the team strategies envisaged by the system developers, and it systematizes the realization of their collaborative behaviors based on the principle solution. The principle solution is modeled with the newly developed specification technique CONSENS for the conceptual design of mechatronic and self-optimizing systems.The specification technique CONSENS was developed in the course of the Collaborative Research Center 614 ‘‘Self-Optimizing Concepts and Structures in Mechanical Engineering’’ funded by the German Research Foundation (DFG) under grant number SFB 614. The first two authors are funded by the Ministry of Higher Education Malaysia under the grant number 600-RMI/ST/ FRGS 5/3/Fst (256/2010) and 600-RMI/ERGS 5/3 (23/2011).Low, CY.; Aziz, N.; Aldemir, M.; Dumitrescu, R.; Anacker, H.; Mellado Arteche, M. (2013). Strategy planning for collaborative humanoid soccer robots based on principle solution. Production Engineering. 7(1):23-34. https://doi.org/10.1007/s11740-012-0416-4S233471Asada M, Kitano H (1999) The RoboCup challenge. Rob Auton Syst 29:3–12Spaan MTJ, Groen FCA (2002) Team coordination through roles, positioning and coordinated procedures. RoboCupLau N, Lopes LS, Corrente G, Nelson F (2009) Multi-robot team coordination through roles, positionings and coordinated procedures. In: 2009 IEEE/RSJ international conference on intelligent robots and systems, October 11–15, St. Louis, USAIocchi L, Nardi D, Piaggo M, Sgorbissa A (2003) Distributed coordination in heterogeneous multi-robot systems. Auton Robots 15:155–168Almeida F, Lau N, Reis LP (2010) A survey on coordination methodologies for simulated robotic soccer teams, multi-agent logics, languages, and organisations federated workshops (MALLOW 2010). Lyon, FranceLückel J, Hestermeyer T, Liu-Henke X (2001) Generalization of the Cascade principle in view of structured form of mechatronic systems. In: IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2001), Villa Olmo, Como, ItalyInternational Council on Systems Engineering (INCOSE) (2007) Systems engineering vision 2020. Incose-TP-2004-004-02, SeptemberGausemeier J, Frank U, Donoth J, Kahl S (2009) Specification technique for the description of self-optimizing mechatronic systems. Res Eng Des 20(4):201–223Cyberbotics Ltd., Webots overview. 20 September 2012 at http://www.cyberbotics.com/overviewBirkhofer H (1980) Analyse und Synthese der FunktionenTechnischerProdukte. Dissertation, TechnischeUniversitätBraunschweigLanglotz G (2000) Ein Beitrag zur Funktionsstrukturentwicklung Innovativer Produkte. Dissertation, Institut fuerr Rechneranwendung in Planung und Konstruktion, Universitaet Karlsruhe, Shaker-Verlag, Band 2/2000, AachenPostel J (1980) User Datagram Protocol. RFC 760, USC/Information Sciences Institut

Architecting centralized coordination of soccer robots based on principle solution

This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 2015, available online:http://www.tandfonline.com/10.1080/01691864.2015.1017534Coordination strategy is a relevant topic in multi-robot systems, and robot soccer offers a suitable domain to conduct research in multi-robot coordination. Team strategy collects and uses environmental information to derive optimal team reactions, through cooperation among individual soccer robots. This paper presents a diagrammatic approach to architecting the coordination strategy of robot soccer teams by means of a principle solution. The proposed model focuses on robot soccer leagues that possess a central decision-making system, involving the dynamic selection of the roles and behaviors of the robot soccer players. The work sets out from the conceptual design phase, facilitating cross-domain development efforts, where different layers must be interconnected and coordinated to perform multiple tasks. The principle solution allows for intuitive design and the modeling of team strategies in a highly complex robot soccer environment with changing game conditions. Furthermore, such an approach enables systematic realization of collaborative behaviors among the teammates.This work was partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-01/02. Jose G. Guarnizo was supported by a scholarship from the Administrative Department of Science, Technology and Innovation COLCIENCIAS, Colombia.Guarnizo Marín, JG.; Mellado Arteche, M.; Low, CY.; Blanes Noguera, F. (2015). Architecting centralized coordination of soccer robots based on principle solution. Advanced Robotics. 29(15):989-1004. https://doi.org/10.1080/01691864.2015.1017534S98910042915Farinelli, A., Iocchi, L., & Nardi, D. (2004). Multirobot Systems: A Classification Focused on Coordination. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34(5), 2015-2028. doi:10.1109/tsmcb.2004.832155Tews, A., & Wyeth, G. (2000). MAPS: a system for multi-agent coordination. Advanced Robotics, 14(1), 37-50. doi:10.1163/156855300741429Stulp, F., Utz, H., Isik, M., & Mayer, G. (2010). Implicit Coordination with Shared Belief: A Heterogeneous Robot Soccer Team Case Study. Advanced Robotics, 24(7), 1017-1036. doi:10.1163/016918610x496964Guarnizo, J. G., Mellado, M., Low, C. Y., & Aziz, N. (2013). Strategy Model for Multi-Robot Coordination in Robotic Soccer. Applied Mechanics and Materials, 393, 592-597. doi:10.4028/www.scientific.net/amm.393.592Riley, P., & Veloso, M. (2002). Recognizing Probabilistic Opponent Movement Models. Lecture Notes in Computer Science, 453-458. doi:10.1007/3-540-45603-1_59Ros, R., Arcos, J. L., Lopez de Mantaras, R., & Veloso, M. (2009). A case-based approach for coordinated action selection in robot soccer. Artificial Intelligence, 173(9-10), 1014-1039. doi:10.1016/j.artint.2009.02.004Atkinson, J., & Rojas, D. (2009). On-the-fly generation of multi-robot team formation strategies based on game conditions. Expert Systems with Applications, 36(3), 6082-6090. doi:10.1016/j.eswa.2008.07.039Costelha, H., & Lima, P. (2012). Robot task plan representation by Petri nets: modelling, identification, analysis and execution. Autonomous Robots, 33(4), 337-360. doi:10.1007/s10514-012-9288-xAbreu, P. H., Silva, D. C., Almeida, F., & Mendes-Moreira, J. (2014). Improving a simulated soccer team’s performance through a Memory-Based Collaborative Filtering approach. Applied Soft Computing, 23, 180-193. doi:10.1016/j.asoc.2014.06.021Duan, Y., Liu, Q., & Xu, X. (2007). Application of reinforcement learning in robot soccer. Engineering Applications of Artificial Intelligence, 20(7), 936-950. doi:10.1016/j.engappai.2007.01.003Hwang, K.-S., Jiang, W.-C., Yu, H.-H., & Li, S.-Y. (2011). Cooperative Reinforcement Learning Based on Zero-Sum Games. Mobile Robots - Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training. doi:10.5772/26620Gausemeier, J., Dumitrescu, R., Kahl, S., & Nordsiek, D. (2011). Integrative development of product and production system for mechatronic products. Robotics and Computer-Integrated Manufacturing, 27(4), 772-778. doi:10.1016/j.rcim.2011.02.005Klančar, G., Zupančič, B., & Karba, R. (2007). Modelling and simulation of a group of mobile robots. Simulation Modelling Practice and Theory, 15(6), 647-658. doi:10.1016/j.simpat.2007.02.002Gausemeier, J., Frank, U., Donoth, J., & Kahl, S. (2009). Specification technique for the description of self-optimizing mechatronic systems. Research in Engineering Design, 20(4), 201-223. doi:10.1007/s00163-008-0058-

Structuring Framework for Early Validation of Product Ideas

The advancing digitalization leads to new challenges in the development of new product ideas. The systems, which need to be developed, are becoming increasingly complex. As complexity rises, so does the slowness of organizations. In order to remain innovative and be able to react quickly to changes in the market, validation approaches offer great potential. An essential focus is the early, customer-centric and continuous validation of development artefacts. This is the only way to cope with shorter development cycles, and develop products that address a real need. To plan a validation, engineers need to have an idea about the possibilities within a validation. This paper presents a structuring framework for early validation of product ideas, which contains the three tools of Validation Map, Building Block Cards and Validation Canvas. The objective is to enable easier planning of validation experiments