Artificial Intelligence for Extended Software Robots, Applications, Algorithms, and Simulators

2020 ◽  
pp. 71-92
Author(s):  
Gayathri Rajendran ◽  
Uma Vijayasundaram
Keyword(s):  
Artificial Intelligence ◽  
Path Planning ◽  
Domain Knowledge ◽  
Robot Navigation ◽  
Machine Learning Algorithms ◽  
Computational Time ◽  
Advanced Technique ◽  
Navigation Data ◽  
Planning Algorithms

Robotics has become a rapidly emerging branch of science, addressing the needs of humankind by way of advanced technique, like artificial intelligence (AI). This chapter gives detailed explanation about the background knowledge required in implementing the software robots. This chapter has an in-depth explanation about different types of software robots with respect to different applications. This chapter would also highlight some of the important contributions made in this field. Path planning algorithms are required for performing robot navigation efficiently. This chapter discusses several robot path planning algorithms which help in utilizing the domain knowledge, avoiding the possible obstacles, and successfully accomplishing the tasks in lesser computational time. This chapter would also provide a case study on robot navigation data and explain the significant of machine learning algorithms in decision making. This chapter would also discuss some of the potential simulators used in implementing software robots.

Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

2018 ◽  
Vol 06 (02) ◽  
pp. 95-118 ◽  
Author(s):  
Mohammadreza Radmanesh ◽  
Manish Kumar ◽  
Paul H. Guentert ◽  
Mohammad Sarim
Keyword(s):  
Path Planning ◽  
Comparative Study ◽  
Operating Conditions ◽  
Computational Time ◽  
Safe Operation ◽  
Sense And Avoid ◽  
Planning Algorithms ◽  
And Control ◽  
Global And Local ◽  
Further Development

Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.

scholarly journals Time-efficient path planning using two virtual robots

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988674
Author(s):  
Jonghoek Kim
Keyword(s):  
Path Planning ◽  
Finite Time ◽  
Free Space ◽  
Path Length ◽  
Optimal Path ◽  
Three Dimensional ◽  
Computational Time ◽  
Topological Map ◽  
Planning Algorithms ◽  
Safe Path

This article introduces time-efficient path planning algorithms handling both path length and safety within a reasonable computational time. The path is planned considering the robot’s size so that as the robot traverses the constructed path, it doesn’t collide with an obstacle boundary. This article introduces two virtual robots deploying virtual nodes which discretize the obstacle-free space into a topological map. Using the topological map, the planner generates a safe and near-optimal path within a reasonable computational time. It is proved that our planner finds a safe path to the goal in finite time. Using MATLAB simulations, we verify the effectiveness of our path planning algorithms by comparing it with the rapidly-exploring random tree (RRT)-star algorithm in three-dimensional environments.

scholarly journals Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

2019 ◽  
Vol 28 (01) ◽  
pp. 052-054
Author(s):  
Gretchen Jackson ◽  
Jianying Hu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
English Language ◽  
Machine Learning Algorithms ◽  
Original Research ◽  
Domain Experts ◽  
Ovid Medline ◽  
Significant Research ◽  
Health Applications

Objective: To summarize significant research contributions to the field of artificial intelligence (AI) in health in 2018. Methods: Ovid MEDLINE® and Web of Science® databases were searched to identify original research articles that were published in the English language during 2018 and presented advances in the science of AI applied in health. Queries employed Medical Subject Heading (MeSH®) terms and keywords representing AI methodologies and limited results to health applications. Section editors selected 15 best paper candidates that underwent peer review by internationally renowned domain experts. Final best papers were selected by the editorial board of the 2018 International Medical Informatics Association (IMIA) Yearbook. Results: Database searches returned 1,480 unique publications. Best papers employed innovative AI techniques that incorporated domain knowledge or explored approaches to support distributed or federated learning. All top-ranked papers incorporated novel approaches to advance the science of AI in health and included rigorous evaluations of their methodologies. Conclusions: Performance of state-of-the-art AI machine learning algorithms can be enhanced by approaches that employ a multidisciplinary biomedical informatics pipeline to incorporate domain knowledge and can overcome challenges such as sparse, missing, or inconsistent data. Innovative training heuristics and encryption techniques may support distributed learning with preservation of privacy.

Artificial Intelligence powered Internet of Things and smart public service

2019 ◽  
Vol 38 (1) ◽  
pp. 165-179 ◽  
Author(s):  
Ying Ma ◽  
Kang Ping ◽  
Chen Wu ◽  
Long Chen ◽  
Hui Shi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Public Service ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Content Type ◽  
Academic Fields ◽  
The Internet Of Things

Purpose The Internet of Things (IoT) has attracted a lot of attention in both industrial and academic fields for recent years. Artificial intelligence (AI) has developed rapidly in recent years as well. AI naturally combines with the Internet of Things in various ways, enabling big data applications, machine learning algorithms, deep learning, knowledge discovery, neural networks and other technologies. The purpose of this paper is to provide state of the art in AI powered IoT and study smart public services in China. Design/methodology/approach This paper reviewed the articles published on AI powered IoT from 2009 to 2018. Case study as a research method has been chosen. Findings The AI powered IoT has been found in the areas of smart cities, healthcare, intelligent manufacturing and so on. First, this study summarizes recent research on AI powered IoT systematically; and second, this study identifies key research topics related to the field and real-world applications. Originality/value This research is of importance and significance to both industrial and academic fields researchers who need to understand the current and future development of intelligence in IoT. To the best of authors’ knowledge, this is the first study to review the literature on AI powered IoT from 2009 to 2018. This is also the first literature review on AI powered IoT with a case study of smart public service in China.

Studi Performansi Algoritma Perencanaan Jalur diantara PRM, RRT, RRT* dan Informed-RRT

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Nelci Dessy Rumlaklak
Keyword(s):  
Path Planning ◽  
Optimality Criteria ◽  
Computational Time ◽  
Comparative Performance ◽  
Performance Review ◽  
Probabilistic Roadmap ◽  
Simulation Based ◽  
Planning Algorithms ◽  
Working Parameters ◽  
Conclusion Section

This paper will discuss a comparative performance review of several path planning algorithms. This study compares five well-known path planning algorithms, namely the Probabilistic Roadmap (PRM), Rapidly-exploring Random Tree (RRT), RRT* and Informed-RRT* algorithm. Testing is done through simulation based experiments using python. The test was conducted using several existing benchmark cases, namely narrow, maze, trap and clutter environment. The optimality criteria compared are path costs, computational time and the total number of nodes in the tree needed. The results of this study will provide information to readers about which algorithm is most suitable for use in user applications where there are several working parameters to be optimized. The findings have been summarized in the conclusion section. Keywords ­: Motion planning, PRM, RRT, RRT*, Informed-RRT*

A Hybrid Fireworks Algorithm to Navigation and Mapping

2020 ◽  
pp. 213-232
Author(s):  
Tingjun Lei ◽  
Chaomin Luo ◽  
John E. Ball ◽  
Zhuming Bi
Keyword(s):  
Artificial Intelligence ◽  
Path Planning ◽  
Mobile Robots ◽  
Robot Navigation ◽  
Optimal Path ◽  
Autonomous Robot ◽  
Mobile Robot Navigation ◽  
Fireworks Algorithm ◽  
Starting Point ◽  
Autonomous Robot Navigation

In recent years, computer technology and artificial intelligence have developed rapidly, and research in the field of mobile robots has continued to deepen with development of artificial intelligence. Path planning is an essential content of mobile robot navigation of computing a collision-free path between a starting point and a goal. It is necessary for mobile robots to move and maneuver in different kinds of environment with objects and obstacles. The main goal of path planning is to find the optimal path between the starting point and the target position in the minimal possible time. A new firework algorithm (FWA) integrated with a graph theory, Dijkstra's algorithm developed for autonomous robot navigation, is proposed in this chapter. The firework algorithm is improved by a local search procedure that a LIDAR-based local navigator algorithm is implemented for local navigation and obstacle avoidance. The grid map is utilized for real-time intelligent robot mapping and navigation. In this chapter, both simulation and comparison studies of an autonomous robot navigation demonstrate that the proposed model is capable of planning more reasonable and shorter, collision-free paths in non-stationary and unstructured environments compared with other approaches.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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