Goal-seeking Behavior-based Mobile Robot Using Particle Swarm Fuzzy Controller

Behavior-based control architecture has successfully demonstrated their competence in mobile robot development. Fuzzy logic system characteristics are suitable to address the behavior design problems. However, there are difficulties encountered when setting fuzzy parameters manually. Therefore, most of the works in the field generate certain interest for the study of fuzzy systems with added learning capabilities. This paper presents the development of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A goal-seeking behaviors based on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations and experiments with MagellanPro mobile robot have been performed to analyze the performance of the algorithm.  The promising results have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment

WALL-FOLLOWING BEHAVIOR-BASED MOBILE ROBOT USING PARTICLE SWARM FUZZY CONTROLLER

Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment

Intelligent wheeled mobile robot navigation

The paper deals with the wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Sun SPOT technology and gives the fuzzy velocity control of a mobile robot motion in an unknown environment with obstacles. When the vehicle is moving towards the target and the sensors detect an obstacle, an avoiding strategy and velocity control are necessary. We proposed the wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Sun SPOT technology and a fuzzy reactive navigation strategy of collision-free motion and velocity control in an unknown environment with obstacles. The simulation results show the effectiveness and the validity of the obstacle avoidance behavior in an unknown environment. The proposed method have been implemented on the miniature mobile robot Khepera® that is equipped with sensors

Navigasi Berbasis Behavior dan Fuzzy Logic pada Simulasi Robot Bergerak Otonom

Mobile robot is the robotic mechanism that is able to moved automatically. The movement of the robot automatically require a navigation system. Navigation is a method for determining the robot motion. In this study, using a method developed robot navigation behavior with fuzzy logic. The behavior of the robot is divided into several modules, such as walking, avoid obstacles, to follow walls, corridors and conditions of u-shape. In this research designed mobile robot simulation in a visual programming. Robot is equipped with seven distance sensor and divided into several groups to test the behavior that is designed, so that the behavior of the robot generate speed and steering control. Based on experiments that have been conducted shows that mobile robot simulation can run smooth on many conditions. This proves that the implementation of the formation of behavior and fuzzy logic techniques on the robot working well.Keywords : behavior, fuzzy logic, mobile robot]Abstrak—Mobile robot merupakan mekanisme robot yang mampu bergerak otomatis. Pergerakan robot secara otomatis memerlukan suatu sistem navigasi. Navigasi adalah metode untuk menentukan gerak robot. Pada penelitian ini navigasi robot dikembangkan menggunakan metode behavior (perilaku) dengan logika fuzzy. Perilaku robot dibagi menjadi beberapa modul, seperti berjalan, menghindari halangan, mengikuti dinding, koridor maupun kondisi u-shape. Pada penelitian ini dirancang simulasi mobile robot di dalam pemrograman visual. Robot dilengkapi dengan tujuh sensor jarak dan dibagi menjadi beberapa kelompok untuk menguji perilaku yang dirancang, sehingga perilaku robot menghasilkan pengaturan kecepatan dan steering. Berdasarkan percobaan yang telah dilakukan menunjukkan bahwa simulasi mobile robot dapat berjalan mulus (smooth) pada berbagai kondisi. Hal ini membuktikan bahwa implementasi pembentukan behavior dan teknik logika fuzzy pada robot bekerja dengan baik.    Kata Kunci : behavior, logika fuzzy, mobile robo

Fuzzy based obstacle avoidance system for autonomous mobile robot

The goal of this research was to develop a fuzzy obstacle avoidance system for an autonomous mobile robot using IR detection sensors. This paper presents implemented control architecture for behavior-based mobile robot. The mobile robot is able to interact with an unknown environment using a reactive strategy determined by sensory information. Current research in robotics aims to build autonomous and intelligent robots, which can plan its motion in a dynamic environment. Autonomous mobile robots are increasingly used in well structured environment such as warehouses, offices and industries. Fuzzy behavior able to make inferences is well suited for mobile robot navigation because of the uncertainty of the environment. A rule-based fuzzy controller with reactive behavior was implemented and tested on a two wheels mobile robot equipped with infrared sensors to perform collision-free navigation. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for small wheeled mobile robots

Development of a Mobile Robot Local Navigation System Based on Fuzzy-Logic Control and Actual Virtual Target Switching

Robot local path planning in an unknown and changing environment with uncertainties is one of the most challenging problems in robotics which involves the integration of many different bodies of knowledge. This makes mobile robotics a challenge worldwide which for many years has been investigated by researchers. Therefore in this thesis, a new fuzzy logic control system is developed for reactive navigation of a behavior-based mobile robot. The motion of a Pioneer 3TM mobile robot was simulated to show the algorithm performance. The robot perceives its environment through an array of eight sonar range finders and self positioning-localization sensors. The robot environment consists of walls and dead end traps from any size and shape, as well as other stationary obstacles and it is assumed to be fully unknown. Robot behaviors consist of obstacle avoidance, target seeking, speed control, barrier following and local minimum avoidance. While the fuzzy logic body of the algorithm performs the main tasks of obstacle avoidance, target seeking, and speed adjustment, an actual-virtual target switch strategy integrated with the fuzzy logic algorithm enables the robot to show wall following behavior when needed. This combinational approach which uses a new kind of target shift, significantly results in resolving the problem of multiple minimum in local navigation which is an advantage beyond the pure fuzzy logic approach and the common virtual target switch techniques. In this work, multiple traps may have any type of shape or arrangement from barriers forming simple corners and U-shape dead ends to loops, maze, snail shape, and many others. Under the control of the algorithm, the mobile robot makes logical trajectories toward the target, finds best ways out of dead ends, avoids any types of obstacles, and adjusts its speed efficiently for better obstacle avoidance and according to power considerations and actual limits. From TRAINER Software and Colbert Program which were used in the simulation work, the system managed to solve all the problems in sample environments and the results were compared with results from other related methods to show the effectiveness and robustness of the proposed approach

A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-03413-3_26In this work is presented a general architecture for a multi physical agent network system based on the coordination and the behaviour management. The system is organised in a hierarchical structure where are distinguished the individual agent actions and the collective ones linked to the whole agent network. Individual actions are also organised in a hybrid layered system that take advantages from reactive and deliberative control. Sensing system is involved as well in the behaviour architecture improving the information acquisition performance.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-02, under coordinated project High Integrity Partitioned Embedded Systems (Hi-PartES): TIN2011-28567-C03-03, and under the collaborative research project supported by the European Union MultiPARTES Project: FP7-ICT 287702. 2011-14.Muñoz Alcobendas, M.; Munera Sánchez, E.; Blanes Noguera, F.; Simó Ten, JE. (2013). A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control. En ROBOT2013: First Iberian Robotics Conference: Advances in Robotics, Vol. 1. Springer. 363-380. https://doi.org/10.1007/978-3-319-03413-3_26S363380Aragues, R.: Consistent data association in multi-robot systems with limited communications. Robotics: Science and Systems, 97–104 (2010)Aragues, R., Cortes, J., Sagues, C.: Distributed consensus on robot networks for dynamically merging feature-based maps. IEEE Transactions on Robotics (2012)Arkin, R.C.: Motor schema based mobile robot navigation. The International Journal of Robotics Research 8(4), 92–112 (1989)Asama, H., Habib, M.K., Endo, I., Ozaki, K., Matsumoto, A., Ishida, Y.: Functional distribution among multiple mobile robots in an autonomous and decentralized robot system. In: Proceedings of the 1991 IEEE International Conference on Robotics and Automation. IEEE (1991)Benet, G., Blanes, F., Martínez, M., Simó, J.: A multisensor robot distributed architecture. In: IFAC Conference INCOM 1998 (1998)Brooks, R.: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2(1), 14–23 (1986)Canas, J.M., Matellán, V.: Dynamic schema hierarchies for an autonomous robot. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527, pp. 903–912. Springer, Heidelberg (2002)Choset, H., Nagatani, K.: Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Transactions on Robotics and Automation 17(2), 125–137 (2001)Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte carlo localization: Efficient position estimation for mobile robots. American Association for Artificial Intelligence, 343–349 (1999)Hu, J., Xie, L., Xu, J.: Vision-based multi-agent cooperative target search. In: Control Automation Robotics & Vision (ICARCV), pp. 895–900 (2012)Huq, R., Mann, G.K.I., Gosine, R.G.: Behavior-modulation technique in mobile robotics using fuzzy discrete event system. IEEE Transactions on Robotics 22(5), 903–916 (2006)Jayasiri, A., Mann, G., Gosine, R.G.: Mobile robot behavior coordination using supervisory control of fuzzy discrete event systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 690–695 (2009)Jayasiri, A., Mann, G.K.I., Gosine, R.G.: Behavior coordination of mobile robotics using supervisory control of fuzzy discrete event systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(5), 1224–1238 (2011)Koenig, N., Howard, A.: Gazebo-3d multiple robot simulator with dynamics. Technical report (2006)Lin, F., Ying, H.: Modeling and control of fuzzy discrete event systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 32(4), 408–415 (2002)Madden, J.D.: Multi-robot system based on model of wolf hunting behavior to emulate wolf and elk interactions. In: 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO, pp. 1043–1050 (2010)Mataric, M.J.: Interaction and intelligent behavior. Technical report, DTIC Document (1994)Olivera, V.M., Molina, J.M., Sommaruga, L., et al.: Fuzzy cooperation of autonomous robots. In: Fourth International System on Intelligent Robotics Systems, Lisboa, Portugal (1996)McGann, C., Py, F., Rajan, K., Thomas, H., Henthorn, R., McEwen, R.: A deliberative architecture for auv control. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 1049–1054. IEEE (2008)Munera, E., Muñoz, M., Simó, J., Blanes, F.: Humanoid Robot Self-Location In SPL League. In: Comité Español de Automática (CEA), XXXIII Jornadas de Automatica, 797–804 (2012)Proetzsch, M., Luksch, T., Berns, K.: Development of complex robotic systems using the behavior-based control architecture iB2C. Robotics and Autonomous Systems 58(1), 46–67 (2010)Qiu, D.: Supervisory control of fuzzy discrete event systems: a formal approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35(1), 72–88 (2005)Aladebaran Robotics. NAO Software Documentation 1.12. Technical report (2012)St-Pierre, M., Gingras, D.: Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system. In: 2004 IEEE Intelligent Vehicles Symposium, pp. 831–835 (2004)Stoytchev, A., Arkin, R.C.: Combining deliberation, reactivity, and motivation in the context of a behavior-based robot architecture. In: Proceedings of the 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 290–295 (2001)Nicolau, V., Muñoz, M., Simó, J.: KertrolBot Platform. SiDiReLi: Distributed System with Limited Resources. Technical report, Institute of Control Systems and Industrial Computing - Polytechnic University of Valencia, Valencia, Spain (2011

Behavior-based navigation of mobile robot in unknown environments using fuzzy logic and multi-objective optimization

© 2017 SERSC. This study proposes behavior-based navigation architecture, named BBFM, to deal with the problem of navigating the mobile robot in unknown environments in the presence of obstacles and local minimum regions. In the architecture, the complex navigation task is split into principal sub-tasks or behaviors. Each behavior is implemented by a fuzzy controller and executed independently to deal with a specific problem of navigation. The fuzzy controller is modified to contain only the fuzzification and inference procedures so that its output is a membership function representing the behavior's objective. The membership functions of all controllers are then used as the objective functions for a multi-objective optimization process to coordinate all behaviors. The result of this process is an overall control signal, which is Pareto-optimal, used to control the robot. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behaviorbased architectures in term of accuracy, smoothness, traveled distance, and time response

Behavior-Based Fuzzy Control for Mobile Robot Navigation

A new behavior-based fuzzy control method for mobile robot navigation is presented. It is based on behavioral architecture which can deal with uncertainties in unknown environments and has the ability to accommodate different behaviors. Basic behaviors are controlled by specific fuzzy logic controllers, respectively. The proposed approach qualifies for driving a robot to reach a target while avoiding obstacles in the environment. Simulation and experiments are performed to verify the correctness and feasibility of the proposed method