Path Planning and Control of Mobile Robots Using Modified Tabu Search Algorithm in Complex Environment

2020 ◽  
Author(s):  
Saroj Kumar ◽  
Manoj Kumar Muni ◽  
Krishna Kant Pandey ◽  
Animesh Chhotray ◽  
Dayal R. Parhi
Keyword(s):  
Tabu Search ◽  
Path Planning ◽  
Mobile Robots ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Complex Environment ◽  
Planning And Control ◽  
And Control

scholarly journals Energy Modeling and Power Measurement for Three-Wheeled Omnidirectional Mobile Robots for Path Planning

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 843 ◽  
Author(s):  
Linfei Hou ◽  
Liang Zhang ◽  
Jongwon Kim
Keyword(s):  
Path Planning ◽  
Power Consumption ◽  
Mobile Robots ◽  
High Mobility ◽  
Intelligent Manufacturing ◽  
Power Measurement ◽  
Research Focus ◽  
Planning And Control ◽  
Perfect Symmetry ◽  
And Control

Due to their high mobility, mobile robots (MR) are widely used in intelligent manufacturing. Due to the perfect symmetry of the MR of the three-wheeled moving chassis, it can move quickly in a crowded and complex factory environment. Because it is powered by a lithium battery, in order to improve its energy efficiency, we need to ensure that its power consumption is reduced as much as possible in order to avoid frequent battery replacement. The power consumption of MRs has also become an important research focus for researchers. Therefore, a power consumption modeling of the omnidirectional mobility of the three-wheeled omnidirectional mobile robot (TOMR) is proposed in this paper. When TOMR advances heading at different angles, the speed of each wheel changes dramatically. So, the power consumption of robots will also be greatly changed. In this paper, the energy and power consumption of the robot heading in different directions is analyzed and modeled by formulas. This research can be valuable for path planning and control design.

scholarly journals Multi-mobile robots and multi-trips feeding scheduling problem in smart manufacturing system: An improved hybrid genetic algorithm

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986812
Author(s):  
Feng Yao ◽  
Yan-Jie Song ◽  
Zhong-Shan Zhang ◽  
Li-Ning Xing ◽  
Xin Ma ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Mobile Robots ◽  
Production Process ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Smart Manufacturing ◽  
Tabu Search Algorithm ◽  
Scheduling Problem ◽  
Production Lines

Modern manufacturing systems require timely and efficient production tasks. Any mistakes can have serious consequences which effect the production process obviously. The supply of goods is the beginning of the production process, ensuring that production can proceed normally. Using mobile robots for transportation and supply of production lines can achieve automatic manufacturing. We studied the use of multiple mobile robots to supply multiple production lines. Robots need to return to warehouse when no goods exist. This problem is called a multi-mobile robots and multi-trips feeding scheduling problem. We constructed a mathematical model describing multi-mobile robots and multi-trips feeding scheduling problem, and the objective function is to minimize the transportation cost and waiting cost. To solve this problem, we proposed an improved hybrid genetic algorithm, where a strategy of mixing improved genetic algorithm and tabu search algorithm is adopted to find robots with reasonable routes. Combining genetic algorithm with tabu search algorithm can improve the route planning effect and find a lower cost solution. In the experimental part, it is verified that the proposed algorithm could effectively find reasonable ways for robots to provide services. We also put forward suggestions for the scenarios of using robots in actual production.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

Networks ◽  
2021 ◽  
Vol 77 (2) ◽  
pp. 322-340 ◽  
Author(s):  
Richard S. Barr ◽  
Fred Glover ◽  
Toby Huskinson ◽  
Gary Kochenberger
Keyword(s):  
Tabu Search ◽  
Extreme Point ◽  
Search Algorithm ◽  
Fixed Charge ◽  
Tabu Search Algorithm ◽  
Network Problems
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