• Journal of Computing Science and Engineering
• /
• v.2 no.4
• /
• pp.321-338
• /
• 2008

This paper presents a Modified Multiple Depth First Search algorithm for the exploration of the indoor environments occupied with obstacles in random distribution. The proposed algorithm was designed and implemented to employ one or a team of Khepera II mini robots for the exploration process. In case of multi-robots, the BlueCore2 External Bluetooth module was used to establish wireless networks with one master robot and one up to three slaves. Messages are sent and received via the module's Universal Asynchronous Receiver/Transmitter (UART) interface. Real exploration experiments were performed using locally developed teleworkbench with various autonomy features. In addition, computer simulation tool was also developed to simulate the exploration experiments with one master robot and one up to ten slaves. Computer simulations were in good agreement with the real experiments for the considered cases of one to one up to three networks. Results of the MMDFS for single robot exhibited 46% reduction in the needed number of steps for exploring environments with obstacles in comparison with other algorithms, namely the Ants algorithm and the original MDFS algorithm. This reduction reaches 71% whenever exploring open areas. Finally, results performed using multi-robots exhibited more reduction in the needed number of exploration steps.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.3 no.2
• /
• pp.119-125
• /
• 2008

This paper presents the polygon-based Q-leaning and Cascade Support Vector Machine algorithm for object search with multiple robots. We organized an experimental environment with ten mobile robots, twenty five obstacles, and an object, and then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and Cascade SVM to enhance the fusion model with DBAM and ABAM process.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.918-925
• /
• 2017

This paper presents a path planning problem for multi-robot system in the environment with dynamic obstacles. In order to guide the robots move along a collision-free path efficiently and reach the goal position quickly, a navigation method based on fuzzy logic controllers has been developed by using proximity sensors. There are two kinds of fuzzy controllers developed in this work, one is used for obstacle avoidance and the other is used for orientation to the target. Both static and dynamic obstacles are included in the environment and the dynamic obstacles are defined with no type of restriction of direction and velocity. Here, the environment is unknown for all the robots and the robots should detect the surrounding information only by the sensors installed on their bodies. The simulation results show that the proposed method has a positive effectiveness for the path planning problem.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.5
• /
• pp.570-576
• /
• 2007

This paper presents a hexagon-based Q-leaning algorithm to find a hidden targer object with multiple robots. An experimental environment was designed with five small mobile robots, obstacles, and a target object. Robots went in search of a target object while navigating in a hallway where obstacles were strategically placed. This experiment employed two control algorithms: an area-based action making (ABAM) process to determine the next action of the robots and hexagon-based Q-learning to enhance the area-based action making process.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.3
• /
• pp.220-224
• /
• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1909-1914
• /
• 2004

Passive velocity field control (PVFC) was previously developed for fully mechanical systems, in which the motion task was specified behaviorally in terms of a velocity field, and the closed-loop was passive with respect to a supply rate given by the environment input. However the PVFC was only applied to a single manipulator, the proposed control law was derived geometrically, and the geometric and robustness properties of the closed-loop system were also analyzed. In this paper, we propose a method to apply a decentralized control algorithm to cooperative 3-wheeled mobile robots whose subsystem is under nonholonomic constraints and which convey a common rigid object in a horizontal plain. Moreover it is shown that multiple robot systems ensure stability and the velocities of augmented systems convergence to a scaled multiple of each desired velocity field for cooperative mobile robot systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2461-2466
• /
• 2003

This paper presents a simple sensor fusion algorithm using neural network for navigation of mobile robots with obstacle avoidance and trap recovery. The multiple sensors input sensor data to the input layer of neural network activating the input nodes. The multiple sensors used include optical encoders, ultrasonic sensors, infrared sensors, a magnetic compass sensor, and GPS sensors. The proposed sensor fusion algorithm is combined with the VFH(Vector Field Histogram) algorithm for obstacle avoidance and AGPM(Adaptive Goal Perturbation Method) which sets adaptive virtual goals to escape trap situations. The experiment results show that the proposed low-level fusion algorithm is effective for real-time navigation of mobile robot.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.37.2-37
• /
• 2002

1. Introduction 2. Problem Formulation 3. Avoidability Measure And Relative Distance 4. Artificial Potential Field in Terms of Relative Distance 5. Simulation Results 6. Conclusions 7. Reference

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.4
• /
• pp.498-504
• /
• 2005

A fuzzy logic for collision avoidance of underwater robots is proposed in this paper. The VFF(Virtual Force Field) method, which is widely used in the field of mobile robots, is modified for application to the autonomous navigation of underwater robots. This Modified Virtual Force Field(MVFF) method using the fuzzy logic can be used in either track keeping or obstacle avoidance. Fuzzy logics are devised to handle various situations which can be faced during autonomous navigation of underwater robots. The obstacle avoidance algorithm has the ability to handle multiple static obstacles. Results of simulation show that the proposed method can be efficiently applied to obstacle avoidance of the underwater robots.

• Proceedings of the KIEE Conference
• /
• 2004.07d
• /
• pp.2411-2413
• /
• 2004

In this paer, we propose a method to apply a decentralized control algorithm for passive velocity field control using virtual flywheel system to cooperative mobile robots. The considered system convey a common rigid object in a horizontal plain. The effectiveness of proposed control algorithm is examined by numerical simulation for cooperative tasks.