Development of the Searching Algorithm with Complexity Environment for Mobile Robots

2013 ◽  
Vol 284-287 ◽  
pp. 1826-1830
Author(s):  
Yung Chin Lin ◽  
Kuo Lan Su ◽  
Chih Hung Chang
Keyword(s):  
User Interface ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Initial Point ◽  
Motion Path ◽  
Target Point ◽  
Motion Platform ◽  
Motion Paths ◽  
Simulation Results ◽  
Flood Fill

The article programs the shortest path searching problems of the mobile robot in the complexity unknown environment, and uses the mobile robot to present the movement scenario from the start point to the target point in a collision-free space. The complexity environment contains variety obstacles, such as road, tree, river, gravel, grass, highway and unknown obstacle. We set the relative dangerous grade for variety obstacles. The mobile robot searches the target point to locate the positions of unknown obstacles, and avoids these obstacles moving in the motion platform. We develop the user interface to help users filling out the positions of the mobile robot and the obstacles on the supervised computer, such the initial point of the mobile robot, the start point and the target point. The supervised computer programs the motion paths of the mobile robot according to A* searching algorithm, flood-fill algorithm and 2-op exchange algorithm The simulation results present the proposed algorithms that program the shortest motion paths from the initial point approach to the target point for the mobile robot. The supervised computer controls the mobile robot that follows the programmed motion path moving to the target point in the complexity environment via wireless radio frequency (RF) interface.

Development of the Escaping Programming System for Fire Environment

2013 ◽  
Vol 300-301 ◽  
pp. 389-392
Author(s):  
Kuo Lan Su ◽  
Bo Yi Li ◽  
Jian Da Fong
Keyword(s):  
User Interface ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Programming System ◽  
Détection Signal ◽  
Intelligent Building ◽  
Fire Environment ◽  
Wireless Rf Interface ◽  
Motion Paths ◽  
Detection Signal

We present the path planning techniques of the fire escaping system using multiple mobile robots for intelligent building. The controller of the mobile robot is MCS-51 microchip, and acquires the detection signal from flame sensor through I/O pins, and receives the command from the supervised compute via wireless RF interface. The mobile robot transmits ID code, detection signal, location and orientation of the mobile robots to the supervised computer via wireless RF interface. We proposed A* searching algorithm to program escaping motion paths to guard peoples moving to the safety area using mobile robots, and develop user interface on the supervised computer for the fire escaping system. In the experimental results, the supervised computer locates the positions of fire sources by mobile robots, and programs the escaping paths on the user interface, and transmits the motion command to the mobile robots. The mobile robot guards peoples leaving the fire sources.

Greedy Algorithm Based Multiple Target Searching for Mobile Robots

2010 ◽  
Vol 44-47 ◽  
pp. 1335-1339
Author(s):  
J. Hung Guo ◽  
Kuo Lan Su
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Greedy Algorithm ◽  
Shortest Path ◽  
Heuristic Algorithm ◽  
Free Space ◽  
The Other ◽  
Motion Path ◽  
Target Point ◽  
Multiple Target

The article mainly researches path planning and task allocation problems of multiple mobile robots using A* searching algorithm and greedy algorithm, and solve the shortest path problems such that the robots can move from the start point to reach the multiple target points in a collision-free space, and uses 2-opt exchange heuristic algorithm to improve the shortest path. In this manner, the mobile moves to the final target point through the other points, and construct the motion path using A* searching algorithm and greedy algorithm. The supervised computer control the mobile robot feedback to the start point from the final target point through the other points, and programs a shortest path using 2-opt exchange heuristic algorithm. We develop the user interface to program the motion path of mobile robots via wireless RF interface. It can displays the motion path of the mobile robot on real-time. The simulated results presents that the proposed method can finds the shortest motion path for mobile robots moving to multiple target points from the start point in a collision-free space. Finally, we implement the experiment scenario on the grid platform using the module-based mobile robot.

Motion Planning of Multiple Pattern Formation for Mobile Robots

2013 ◽  
Vol 284-287 ◽  
pp. 1877-1882 ◽  
Author(s):  
J. Hung Guo ◽  
Yung Chin Lin ◽  
Kuo Lan Su ◽  
Bo Yi Li
Keyword(s):  
Pattern Formation ◽  
Mobile Robots ◽  
Motion Platform ◽  
Robot System ◽  
The Status ◽  
Wireless Rf Interface ◽  
Minimum Displacement ◽  
Motion Paths ◽  
Pattern Formations ◽  
Flood Fill

The article designs the multiple pattern formation controls of the multi-robot system according to two arms’ gesture of the player, and uses flood fill searching algorithm and A* searching algorithm to program the motion paths. The inertia module detects two arms’ gesture of the player. We use the inertia module to be embedded in the two arms, and use mobile robots to present the movement scenario of pattern formation exchange on the grid based motion platform. We have been developed some pattern formations applying in the war game, such as rectangle pattern formation, long snake pattern formation, L pattern formation, sword pattern formation, cone pattern formation and so on. We develop the user interface for variety pattern formation exchange according to the minimum displacement on the supervised computer. The mobile robot receives the command from the supervised compute, and transmits the status of environment to the supervised computer via wireless RF interface. Players can use variety arms’ gesture to control the multiple mobile robots to executed pattern formation exchange. In the experimental results, the supervised computer can decides the arm gesture using fusion algorithms. Mobile robots can receive the pattern formation command from the supervised computer, and change the original pattern formation to the assigned pattern formation on the motion platform, and avoid other mobile robots.

Enhance A* Searching Algorithm Applying in Multiple Robot System

2013 ◽  
Vol 479-480 ◽  
pp. 773-777 ◽  
Author(s):  
Kuo Lan Su ◽  
Bo Yi Li ◽  
Cheng Yun Chung
Keyword(s):  
User Interface ◽  
Mobile Robots ◽  
Simulation Method ◽  
Robot System ◽  
Collision Problem ◽  
Chess Game ◽  
Evaluation Algorithm ◽  
Wireless Rf Interface ◽  
Motion Paths ◽  
Multiple Robot

The article programs the shortest motion paths of the multiple mobile robots to be applied in the Chinese chess game, and presents the movement scenario of the chess using mobile robots on the grid based chessboard platform. Users play the chess game using the mouse to obey the evaluation algorithm on the user interface. The user interface programs the motion paths that are the shortest displacement using enhance A* searching algorithm and solves the collision problem of the programmed motion paths for the assigned chesses to and reprogram the new motion paths using enhance A* searching algorithm, too. The supervised computer controls mobile robots according to the programmed motion paths of the assigned chess moving on the platform via wireless RF interface. In the experimental results, we use simulation method to search the motion paths of the assigned chesses on the user interface, and implement the simulation results on the chessboard platform using mobile robots. Mobile robots move on the platform according to the programmed motion paths from the start points to the target points and avoid the collision points.

Virtual Reality based Mobile Robot Navigation in Greenhouse Environment

2017 ◽  
Vol 8 (2) ◽  
pp. 854-859
Author(s):  
M. Saiful Azimi ◽  
Z. A. Shukri ◽  
M. Zaharuddin
Keyword(s):  
Virtual Reality ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Virtual Environment ◽  
Robot Navigation ◽  
Tracking Error ◽  
Mobile Robot Navigation ◽  
Probabilistic Roadmap ◽  
Time Distance ◽  
Simulation Results

The difficulties of transporting heavy mobile robots limit robotic experiments in agriculture. Virtual reality however, offers an alternative to conduct experiments in agriculture. This paper presents an application of virtual reality in a robot navigational experiment using SolidWorks and simulated into MATLAB. Trajectories were initiated using Probabilistic Roadmap and compared based on travel time, distance and tracking error, and the efficiency was calculated. The simulation results showed that the proposed method was able to conduct the navigational experiment inside the virtual environment. U-turn trajectory was chosen as the best trajectory for crop inspection with 82.7% efficiency.

Image Recognition Method Applying in Formation Control of Mobile Robots

2012 ◽  
Vol 190-191 ◽  
pp. 693-698
Author(s):  
Y.C. Lin ◽  
C.H. Chen ◽  
K.L. Su ◽  
J.H. Guo
Keyword(s):  
Pattern Formation ◽  
Mobile Robots ◽  
Image Recognition ◽  
Formation Control ◽  
Recognition System ◽  
Motion Platform ◽  
Bar Code ◽  
Motion Paths ◽  
Pattern Formations ◽  
2D Bar Code

The article develops multi-pattern formation exchange using A* searching algorithm, and programs the shortest motion paths for mobile robots. The system contains an image recognition system, a motion platform, some wireless RF modules and five mobile robots. We use Otsu algorithm to recognize the variety 2D bar code to classify variety pattern, and control five mobile robots to execute formation exchange, and present the movement scenario on the motion platform. We have been developed some pattern formations according to game applications, such as hook pattern formation, T pattern formation, L pattern formation, rectangle pattern formation, sward pattern formation and so on, and develop the user interface of the multi-robot system to program motion paths for variety pattern formation exchange on the supervised computer. The supervised computer programs pattern formation exchange according to the image recognition results, and controls mobile robots moving on the motion platform via wireless RF interface. In the experimental results, mobile robots can receive the pattern formation command from the supervised computer, and change the original pattern formation to the assigned pattern formation on the motion platform, and avoid other mobile robots on real-time.

Decentralized Multi-Robot Teams Using Wheeled Manipulation for Object Transportation

2012 ◽  
Author(s):  
Tyson L. Ringold ◽  
Raymond J. Cipra
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Dynamic Simulation ◽  
Carrying Capacity ◽  
Arbitrary Number ◽  
Object Interaction ◽  
Wheeled Robot ◽  
Simulation Results ◽  
Object Transportation ◽  
Multi Robot

Object transportation is an especially suitable task for cooperative mobile robots where the carrying capacity of an individual robot is naturally limited. In this work, a unique wheeled robot is presented that, when used in homogeneous teams, is able to lift and carry objects which may be significantly larger than the robot itself. A key feature of the presented robot is that it is devoid of articulated manipulation mechanisms, but instead relies on its drive wheels for object interaction. After a brief introduction to the mechanics of this mobile robot, a behavior-based lifting and carrying strategy is developed that allows the robot to cooperatively raise an object from the ground, transition into a carrying role, and then transport the object across cluttered, unstructured terrain. The strategy is inherently decentralized, allowing an arbitrary number of robots to participate in the transportation task. Dynamic simulation results are then presented, showing the effectiveness of the strategy.

scholarly journals Motion Planning of Multiple Mobile Robots Using Virtual Impedance

1996 ◽  
Vol 8 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Tamio Arai ◽  
◽  
Jun Ota
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Lower Layer ◽  
Local Equilibrium ◽  
Current Position ◽  
Cooperative Motion ◽  
Robot Systems ◽  
Simulation Results ◽  
Virtual Impedance

This paper proposes a planning method for multiple mobile robot systems. It has two characteristics: (1) Each robot plans a path on its own, without any supervisor; (2) The concept of cooperative motion can be implemented. A two-layered hierarchy is defined for a scheme of individual path planning. The higher layer generates a trajectory from the current position to a goal. The lower layer called“Virtual Impedance Metho” makes a real-time plan to follow the generated trajectory while avoiding obstacles and avoiding or cooperating with other robots. This layer is composed of four modules called, “watchdog”, “deadlock solver”,“blockade solver”, and “pilot”. The local equilibrium is detected by the watchdog and cancelled by the deadlock solver or the blockade solver. Simulation results indicate the effectiveness of the proposed method.

scholarly journals An algorithm for formation control of mobile robots

2013 ◽  
Vol 10 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Aleksandar Cosic ◽  
Marko Susic ◽  
Stevica Graovac ◽  
Dusko Katic
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Formation Control ◽  
Control Structure ◽  
Kinematic Model ◽  
The Real ◽  
Avoidance Control ◽  
Simulation Results ◽  
High Level

Solution of the formation guidance in structured static environments is presented in this paper. It is assumed that high level planner is available, which generates collision free trajectory for the leader robot. Leader robot is forced to track generated trajectory, while followers? trajectories are generated based on the trajectory realized by the real leader. Real environments contain large number of static obstacles, which can be arbitrarily positioned. Hence, formation switching becomes necessary in cases when followers can collide with obstacles. In order to ensure trajectory tracking, as well as object avoidance, control structure with several controllers of different roles (trajectory tracking, obstacle avoiding, vehicle avoiding and combined controller) has been adopted. Kinematic model of differentially driven two-wheeled mobile robot is assumed. Simulation results show the efficiency of the proposed approach.

Motion Planning of the Mobile Robot Using Laser Detection System

2013 ◽  
Vol 418 ◽  
pp. 48-51
Author(s):  
Jr Hung Guo ◽  
Bo Yi Li ◽  
Shih Ping Lin ◽  
Kuo Lan Su
Keyword(s):  
Mobile Robot ◽  
Detection System ◽  
Target Position ◽  
Motion Path ◽  
Positioning System ◽  
Laser Range Finder ◽  
Range Finder ◽  
Laser Range ◽  
Laser Detection ◽  
Motion Paths

The paper programs motion paths using laser detection system for the mobile robot. The laser detection system contains a laser range finder and a laser positioning system. The mobile robot is constructed using aluminum frame, and has the shape of cylinder and its diameter, height and weight is 40 cm, 80cm and 40kg respectively. In the driver system and avoidance obstacle driver system, we use NI motion control card and MAXON drivers to control two DC servomotors, and detect obstacle using a laser range finder and sixteen reflective IR sensors. The mobile robot locates the position of the detected obstacles using a laser positioning system. The mobile robot can program the motion path using A* searching algorithm and avoids the detected obstacles to follow the programmed trajectory. We develop the user interface to display the positions of the detected obstacles. Finally, we implement the experimental results using the proposed method. The mobile robot moves to the target position from the start position autonomously. The mobile robot detects environment status using the laser detection system and avoids the detected obstacles to finish the assigned tasks.

Sign in / Sign up

Export Citation Format

Share Document