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.

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.

Assess Team Q-Learning Algorithm in a Purely Cooperative Multi-Robot Task

2007 ◽  
Author(s):  
Ying Wang ◽  
Clarence W. de Silva
Keyword(s):  
Stochastic Games ◽  
Autonomous Robots ◽  
Learning Algorithm ◽  
Single Agent ◽  
Dynamic Environment ◽  
Q Learning ◽  
Robot Systems ◽  
Simulation Results ◽  
Object Transportation ◽  
Multi Robot

Multi-robot systems have received more and more attentions in the robotics community in the past decade. The most important issue in this area is multi-robot coordination, which focuses on how to make multiple autonomous robots cooperate or compete with each other to complete a common task. Due to its complexity, the conventional planning-based or behavior-based approaches can not work well in multi-robot coordination, especially in a dynamic unknown environment. Therefore, machine learning is becoming a promising method to help robots work in an unknown dynamic environment and improve their performance increasingly. The Q-learning algorithm was selected by most of multi-robot researchers to accomplish the above objective because of its simplicity and low computational requirements. However, directly extending the single-agent Q-learning algorithm will violate its Markov assumption and result in a low convergence speed and failing to learn a good cooperative policy. In this paper, the team Q-learning algorithm, which was originally designed for the framework of Stochastic Games (SG), is proposed to make decisions for a multi-robot purely cooperative project: Multi-robot object transportation. Firstly, the basic idea of the framework of Stochastic Games and the team Q-learning algorithm are introduced. Next, the algorithm is extended to a multi-robot object transportation task, and the implementation details are presented. Some computer simulation results are presented to demonstrate that the team Q-learning algorithm works well to make decisions for the proposed multi-robot system. Finally, effects of some parameters of team Q-learning are assessed and some interesting conclusions are drawn. In particular, the simulation results show that training is helpful for improving the performance of multi-robot decision-making, but its effect is very limited. In addition, it is also pointed out that the team Q-learning will result in a huge learning space when the robot number is bigger than ten, which indicates that a new Q-learning algorithm integrating single-agent Q-learning and Team Q-learning is urgent to be developed for multi-robot systems.

Research on Cooperation Localization and Mapping for Hoisting Multi-Robot System

2012 ◽  
Vol 229-231 ◽  
pp. 2248-2252
Author(s):  
Lin Jun ◽  
Zi Bin ◽  
Wu Xia
Keyword(s):  
Mobile Robots ◽  
Grid Method ◽  
Sensor Technology ◽  
Robot System ◽  
Localization And Mapping ◽  
Practical Operation ◽  
Mapping System ◽  
Simulation Results ◽  
Multiple Robot ◽  
Multi Robot

According to the practical operation of the hoisting multi-mobile robots system (HMRS), the cooperation localization and mapping is studied. Firstly, an improved algorithm of cooperation localization solution of multiple robot system is proposed based on map gridding algorithm. In addition, by virtue of sensor technology, the grid method is designed, which has the ability of accurate, reliable localization and rapid local mapping of the HMRS. Finally, simulation results demonstrate that the localization and mapping system is feasible and efficient.

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.

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.

Design and Movement Analysis of Single Roller Omni Directional Wheeled Robot for Different Assembly Structures

2014 ◽  
Vol 592-594 ◽  
pp. 2324-2328 ◽  
Author(s):  
A. P. Mohanraj ◽  
A. Elango ◽  
D. Ragavendhiran ◽  
P.Vignesh Raja ◽  
K. Ashok
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Mobile Robotics ◽  
Movement Analysis ◽  
Rotary Motion ◽  
Triangular Structure ◽  
Synthetic Rubber ◽  
Wheeled Robot ◽  
Omni Wheel

This paper addresses the movement analysis of square structured and triangular structured Omni directional mobile robots and its combination in the form of octagonal structured mobile robot. The Omni wheel used in this research is having 8 rollers made up of synthetic rubber coated polypropylene rollers. An experiment was setup to analyse the movement of the square, triangular and octagonal structured robot in x-axis, y-axis and rotary motion. This experiment is an attempt of combining square structure and triangular structure robot in a single robot. Omni Directional mobile robot creates another step in the field of mobile robotics.

ADAPTIVE CONTROL OF MOBILE ROBOTS USING A NEURAL NETWORK

2001 ◽  
Vol 11 (03) ◽  
pp. 211-218 ◽  
Author(s):  
Celso de Sousa ◽  
Elder Moreira Hermerly
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Robot Navigation ◽  
Control Approach ◽  
On Line ◽  
Simulation Results ◽  
Previous Learning

A Neural Network - based control approach for mobile robot is proposed. The weight adaptation is made on-line, without previous learning. Several possible situations in robot navigation are considered, including uncertainties in the model and presence of disturbance. Weight adaptation laws are presented as well as simulation results.

scholarly journals Path tracking mobile robot using steppers

2019 ◽  
Vol 87 ◽  
pp. 01028
Author(s):  
Ankit Deo ◽  
Ayush Gupta ◽  
Himanshu Khemani ◽  
Rashmi Ranjan Das
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Control Algorithm ◽  
Path Tracking ◽  
3D Mapping ◽  
Precise Control ◽  
Dc Motors ◽  
Wheeled Robot ◽  
Stepper Motors

In control of mobile robots, precision plays a key role in path tracking. In this paper we have intended to use hybrid stepper motors for precise control of the two wheeled robot. A control algorithm was developed to control the robot along different trajectories. We have found that stepper motors are more accurate for path tracking than normal DC motors with wheel encoders and one can obtain the implicit coordinates of the robot in runtime more precisely. Getting the precise coordinates of the robot at runtime can be used in various SLAM and VSLAM techniques for more accurate 3D mapping of the environment.

Wheeled Robot Path Tracking Study Based on IMM Algorithm

2014 ◽  
Vol 1037 ◽  
pp. 228-231
Author(s):  
Li Cai ◽  
Jian Ping Jia
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Path Tracking ◽  
Wheeled Mobile Robot ◽  
Robot Design ◽  
Matlab Software ◽  
Wheeled Robot ◽  
Simulation Results ◽  
Robot Path

This paper proposes a wheeled robot design based on IMM algorithm , aiming at how to achieve the optimization of path planning. Obstacle detecting and avoidance method for mobile robot are implemented with the photoelectric sensors. Then IMM algorithm for path planning is introduced and the simulation results in the MATLAB software show that the method of introducing IMM into mobile robot is convenient. By this means, the path planning is well optimized in real-time way for wheeled mobile robot.

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