Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand

In the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role Assignmen

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

Mobile robot controller using novel hybrid system

Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduce that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration

Review of Sustainable Irrigation Technological Practices in Agriculture

The paper focuses on the increasing demand for water and its impact on irrigated agriculture, emphasizing the importance of effective water management. It reviews the use of soil moisture sensors, IoT, big data analytics, and machine learning in agriculture, particularly in the context of Indian agriculture. The study explores the potential of IoT technologies, such as sensors, drones, and machine learning algorithms, to optimize water usage, minimize waste, and enhance crop yields. The role of big data analytics in sustainable water irrigation management and decision support systems is highlighted. The integration of IoT and sensory systems in smart agriculture is discussed, addressing both the challenges and benefits of implementing sensory-based irrigation systems. Additionally, the paper describes an automated irrigation system developed to optimize water use for crops, utilizing a distributed wireless network of sensors and a web application. The system, powered by photovoltaic panels, demonstrated significant water savings of up to 90% compared to traditional irrigation methods in a sage crop field. The system's energy autonomy and cost-effectiveness suggest its potential utility in water-limited and geographically isolated areas

Fuzzy Logic Method to Control Evenly Distributed and Stable Waterbath Temperature with Four Heaters

Water baths are commonly used in scientific fields to incubate samples at specific temperatures. The temperature of the water bath must be controlled precisely, because even the slightest temperature variation can affect the results of the experiment. It can handle imprecise, uncertain and incomplete information, making it suitable for temperature control in water baths. This research aims to determine the distribution and stability of fuzzy logic control to control the temperature of a water bath with four heaters. Even heat distribution from the four heaters will ensure consistent water bath temperature throughout the bath. This research uses an Arduino microcontroller to process the temperature sensor output from the DS18B20, then the processed temperature value will be displayed on the TFT LCD. The independent variable in this research is the temperature value, while the dependent variable is the DS18B20 temperature sensor. The largest error value from the module measurements is at a temperature setting of 30 ºC on the 2nd temperature sensor with an error value of 1.43%. Meanwhile, the smallest error value is found at a temperature setting of 35 ºC on the 1st and 4th temperature sensors with an error value of 0.01%. Data collection used a digital thermometer comparison tool with 10 repetitions as a temperature sensor reference tool. The results obtained using this sensor are more stable and have a high accuracy value. The results of the research show that the temperature difference between points 1 to 4 when viewed from the error percentage is very small, or it can be said that the temperature distribution is even. The conclusion from these results is that the module has a stable temperature value and the error value is low and is still within the tolerance limit. permitted is ±5%

Toward enhancement of deep learning techniques using fuzzy logic: a survey

Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed