Game-Theoretic Strategic Coordination and Navigation of Multiple Wheeled Robots

2021 ◽  
Vol 21 (4) ◽  
pp. 1-15
Author(s):  
Buddhadeb Pradhan ◽  
Nirmal Baran Hui ◽  
Diptendu Sinha Roy ◽  
Gautam Srivastava ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Robot Navigation ◽  
Heuristic Method ◽  
Performance Comparison ◽  
Multiple Robots ◽  
Coordination Strategy ◽  
Strategic Coordination ◽  
Comparison Results ◽  
Game Theoretic ◽  
Multi Robot

Multiple robots negotiating in a dynamic workspace may lead to collisions. To avoid such issues, multi-robot navigation and coordination becomes necessary but is computationally very challenging, particularly when there are many robots. This article addresses the problem of multi-robot navigation where individual robots require coordination. Although a few such attempts for modeling multi-robot coordination and navigation have been studied, this work proposes a game-theoretic coordination strategy, also referred to as strategic coordination. We make use of a genetic algorithm tuned fuzzy logic–based motion planner. The proposed strategic coordination strategy has been pitted against a basic potential field-based motion planner, also referred to as the heuristic method, for performance comparison. Results are compared through computer simulation with 8 to 17 robots at different rounds. From the obtained results, it was observed that the proposed coordination scheme’s efficacy is strong for a larger number of robots. In addition, the proposed strategic coordination scheme with the genetic-fuzzy-based motion planner was found to outperform other combinations as far as the quality of solutions and time to reach the goal positions. The computational complexity of different methods has also been compared and presented.

scholarly journals The influence of the map merging order on the resulting global map in multi-robot mapping

2012 ◽  
Vol 13 (1) ◽  
pp. 22-28
Author(s):  
Ilze Andersone
Keyword(s):  
Autonomous Robot ◽  
Multiple Robots ◽  
Important Prerequisite ◽  
Robot Teams ◽  
Mapping Process ◽  
As If ◽  
Multi Robot ◽  
Map Merging

An important prerequisite for creation of an autonomous robot is the ability to create the map of the environment. While the use of robot teams becomes more and more widely used, the issue of robot coordination becomes one of the central questions to be addressed. If multiple robots are used for the exploration of the environment, their collected information has to be fused into one general global map. This problem is called map merging. In the case, when more than two robots map the environment, it is possible that the order of map merging can influence the quality of the result - the global map. However, most researches in the map merging field address the problem as if the recommended order of map merging were known. The goal of this paper is to prove that the merging order can greatly influence the resulting global map and discuss the consequences this knowledge makes in the mapping process.

scholarly journals Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control

2017 ◽  
Author(s):  
Chao Huang ◽  
Xin Chen ◽  
Yifan Zhang ◽  
Shengchao Qin ◽  
Yifeng Zeng ◽  
...  
Keyword(s):  
Predictive Control ◽  
Robot Navigation ◽  
Reference Trajectory ◽  
Control Parameters ◽  
Multiple Robots ◽  
High Complexity ◽  
Navigation Problem ◽  
A New Technique ◽  
Theoretical Results ◽  
Multi Robot

Multi-robot navigation control in the absence of reference trajectory is rather challenging as it is expected to ensure stability and feasibility while still offer fast computation on control decisions. The intrinsic high complexity of switched linear dynamical robots makes the problem even more challenging. In this paper, we propose a novel HMPC based method to address the navigation problem of multiple robots with switched linear dynamics. We develop a new technique to compute the reachable sets of switched linear systems and use them to enable the parallel computation of control parameters. We present theoretical results on stability, feasibility and complexity of the proposed approach, and demonstrate its empirical advance in performance against other approaches.

scholarly journals Comparison of Indonesian Population Information System Service Performace Based On Information System Functional Scorecard Theory

2021 ◽  
Vol 6 (3) ◽  
pp. 297-307
Author(s):  
Najla Alia Farah ◽  
Admaja Dwi Herlambang ◽  
Aditya Rachmadi
Keyword(s):  
Information System ◽  
Information Systems ◽  
Performance Comparison ◽  
Civil Registration ◽  
System Service ◽  
Optimal Service ◽  
Comparison Results ◽  
Population Information ◽  
Work Functions

The Balikpapan Population and Civil Registration Office has implemented service automation by using an information system to support the population and civil registration administration services in Balikpapan, Indonesia. The information system implemented is called The Population Administration Information System (SIAK). The office has previously implemented an information system similar to SIAK, namely the Population Administration Organizing System (SPAK). One of the office aims is to improve the quality of SIAK. SIAK quality is improved in order to achieve optimal service. As a step of achieving vision and mission, implementing work functions and optimizing the utilization of information systems, the comparison of information system performance between information systems that have been used with information systems that have previously been used. The guidelines used in assessing the performance of both systems are Information System Functional Scorecard (ISFS) theory. The data collected by questionnaires then conducted Paired-T Test so that the results of the two systems were obtained differences and can be compared to know which system is superior. SIAK is an information system that is declared superior to SPAK. Furthermore, the comparison results become a reference in providing recommendations for SIAK performance improvement based on the performance comparison results with SPAK.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals A CDT-Based Heuristic Zone Design Approach for Economic Census Investigators

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Changixu Cheng ◽  
Xiaomei Song ◽  
Jing Yang ◽  
Xiatian Hu ◽  
Shi Shen ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Real World ◽  
Heuristic Method ◽  
Real World Application ◽  
Economic Census ◽  
Speed Up ◽  
Growth Approach ◽  
Special Zone ◽  
Required Quality

This paper addresses a special zone design problem for economic census investigators that is motivated by a real-world application. This paper presented a heuristic multikernel growth approach via Constrained Delaunay Triangulation (CDT). This approach not only solved the barriers problem but also dealt with the polygon data in zoning procedure. In addition, it uses a new heuristic method to speed up the zoning process greatly on the premise of the required quality of zoning. At last, two special instances for economic census were performed, highlighting the performance of this approach.

scholarly journals A Heuristics-Based Parthenogenetic Algorithm for the VRP with Potential Demands and Time Windows

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Chenghua Shi ◽  
Tonglei Li ◽  
Yu Bai ◽  
Fei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Computation Time ◽  
Routing Problem ◽  
Split Delivery ◽  
Soft Time Windows ◽  
Comparison Results ◽  
Potential Demand ◽  
The Cost

We present the vehicle routing problem with potential demands and time windows (VRP-PDTW), which is a variation of the classical VRP. A homogenous fleet of vehicles originated in a central depot serves customers with soft time windows and deliveries from/to their locations, and split delivery is considered. Also, besides the initial demand in the order contract, the potential demand caused by conformity consuming behavior is also integrated and modeled in our problem. The objective of minimizing the cost traveled by the vehicles and penalized cost due to violating time windows is then constructed. We propose a heuristics-based parthenogenetic algorithm (HPGA) for successfully solving optimal solutions to the problem, in which heuristics is introduced to generate the initial solution. Computational experiments are reported for instances and the proposed algorithm is compared with genetic algorithm (GA) and heuristics-based genetic algorithm (HGA) from the literature. The comparison results show that our algorithm is quite competitive by considering the quality of solutions and computation time.

A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

1995 ◽  
Author(s):  
Stephen S. Altus ◽  
Ilan M. Kroo ◽  
Peter J. Gage
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Heuristic Method ◽  
Merit Function ◽  
Multidisciplinary Design ◽  
Design Problems ◽  
Design Studies ◽  
Complex Design ◽  
Complex Engineering

Abstract Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

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