scholarly journals Route planning methods for a modular warehouse system

2019 ◽  
Vol 10 (1) ◽  
pp. 17-25
Author(s):  
Elif G. Dayıoğlu ◽  
Kenan Karagül ◽  
Yusuf Şahin ◽  
Michael G. Kay
Keyword(s):  
Storage System ◽  
Route Planning ◽  
Robot Motion ◽  
Planning Problem ◽  
Robot Motion Planning ◽  
Data Sets ◽  
Heuristic Methods ◽  
Planning Methods ◽  
Multiple Unit ◽  
Simultaneous Movement

In this study, procedures are presented that can be used to determine the routes of the packages transported within a modular storage system. The problem is a variant of robot motion planning problem.  The structures of the procedures are developed in three steps for the simultaneous movement of multiple unit-sized packages in a modular warehouse.  The proposed heuristic methods consist of route planning, tagging, and main control components. In order to demonstrate the solution performance of the methods, various experiments were conducted with different data sets and the solution times and qualities of the proposed methods were compared with previous studies. It was found that the proposed methods provide better solutions when taking the number of steps and solution time into consideration.

scholarly journals CATEGORY AND TOPOLOGICAL COMPLEXITY OF THE CONFIGURATION SPACE

2019 ◽  
Vol 100 (3) ◽  
pp. 507-517
Author(s):  
CESAR A. IPANAQUE ZAPATA
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Rigid Bodies ◽  
Robot Motion ◽  
Planning Problem ◽  
Topological Complexity ◽  
Robot Motion Planning ◽  
Spatial Motion ◽  
Lusternik Schnirelmann ◽  
Homotopy Invariants

The Lusternik–Schnirelmann category cat and topological complexity TC are related homotopy invariants. The topological complexity TC has applications to the robot motion planning problem. We calculate the Lusternik–Schnirelmann category and topological complexity of the ordered configuration space of two distinct points in the product $G\times \mathbb{R}^{n}$ and apply the results to the planar and spatial motion of two rigid bodies in $\mathbb{R}^{2}$ and $\mathbb{R}^{3}$ respectively.

The Role of Motion Planning in Robotics

2015 ◽  
Vol 811 ◽  
pp. 311-317
Author(s):  
Ellips Masehian
Keyword(s):  
Motion Planning ◽  
Computer Science ◽  
Robotic Systems ◽  
Robot Motion ◽  
Planning Problem ◽  
Robot Motion Planning ◽  
Wide Range ◽  
Vast Literature ◽  
High Level

As robotic systems evolve and get more sophisticated, expectations of them to accomplish high-level tasks increase gradually and their motion planning becomes more complex and difficult. The motion planning problem has been studied for more than four decades from different aspects such that presently has a vast literature. This paper investigates different components of the robot motion planning (RMP) problem and presents a new comprehensive taxonomy for a wide range of RMP problems. The taxonomy is based on a survey of the literature on RMP problems and applications in robotics and computer science.

Extending RRT for Robot Motion Planning with SLAM

2012 ◽  
Vol 151 ◽  
pp. 493-497 ◽  
Author(s):  
Hai Zhu Pan ◽  
Jin Xue Zhang
Keyword(s):  
Genetic Algorithms ◽  
Mobile Robot ◽  
Motion Planning ◽  
Cell Decomposition ◽  
Potential Fields ◽  
Robot Motion ◽  
Planning Problem ◽  
Robot Motion Planning ◽  
The Past ◽  
Road Maps

In this paper,the motion planning problem for mobile robot is addressed. Motion planning (MP) has diversified over the past few decades to include many different approaches such as cell decomposition, road maps, potential fields, and genetic algorithms. Often the goal of motion planning is not just obstacle avoidance but optimization of certain parameters as well. A motion planning algorithms based on Rapidly-exploring random Tree(RRT) is present in the paper. Then the RRT algorithm has been extended which combines the SLAM algorithm.The Extend-RRT-SLAM has been simulated in MobileSim.Simulation results show Extend-RRT-SLAM to be very effective for robot motion planning.

Route planning of intelligent bridge cranes based on an improved artificial potential field method

2021 ◽  
pp. 1-8
Author(s):  
Zhengyan Chang ◽  
Zhengwei Zhang ◽  
Qiang Deng ◽  
Zheren Li
Keyword(s):  
Potential Field ◽  
Autonomous Navigation ◽  
Route Planning ◽  
Simple Algorithm ◽  
Field Method ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Operating Characteristics ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.

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