Fuzzy State Machine Research Articles

Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition.

This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous for humans, provide ample room for improvement through collaboration with robots. However, the integration of humans in-the-loop raises open challenges due to the uncertainty that comes with the ambiguous nature of human behaviour. Such uncertainty makes it difficult to represent high-level human behaviour based on low-level sensory input data. The proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) approach addresses this challenge by fuzzifying ambiguous sensory data and developing a combined activity recognition and sequence modelling system using state machines and the LSTM deep learning method. The evaluation process compares the traditional LSTM approach with raw sensory data inputs, a Fuzzy-LSTM approach with fuzzified inputs, and the proposed FS-LSTM approach. The results show that the use of fuzzified inputs significantly improves accuracy compared to traditional LSTM, and, while the fuzzy state machine approach provides similar results than the fuzzy one, it offers the added benefits of ensuring feasible transitions between activities with improved computational efficiency.

Using artificial intelligence assistant technology to develop animation games on IoT

This research proposes an XNA animation game system with AI technology for action animation games in mobile devices, based on an object-oriented modular concept. The animation game function with AI technology is encapsulated into independent objects, through the combination of objects to build repetition. It adds AI technology to the finite state machine, fuzzy state machine and neural network and attempts to combine the traditional rule-base system and learning adaptation system to increase the learning ability of traditional AI roles. The main contributions are compared with traditional methods and the AI animation game system is shown to have more reusability, design flexibility and expansibility of its AI system through the object composition approach. It adds AI technology to combine the traditional rule-base system and learning adaptation system to increase the learning ability of traditional AI roles. Therefore, AI animation game producers can accelerate their processes of developing animation games and reducing costs.

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.

Energy management and optimization of PEMFC/battery mobile robot based on hybrid rule strategy and AMPSO

Due to the flexibility of seam tracking robot in special environment, independent power supply with different characteristics is adopted. Power allocation of hybrid power system composed of proton exchange membrane fuel cell and lithium battery is mainly researched. According to the requirements of economy and safety, hybrid rule fuzzy state machine control is designed to realize the optimal operation and fast response of power supply system. On the basis of energy allocation, further reduction of hydrogen consumption and power fluctuation is considered, which improves the power supply scheme. The sensitivity analysis method is used to screen the optimization variables, which reduces the complexity of the strategy; in addition, an adaptive mutation particle swarm optimization is studied to optimize the strategy, which combines the mutation idea, adaptively changes the weight and learning factor, and realizes the minimization of power fluctuation and equivalent hydrogen consumption. The results show that the stability of fuel cell and the rationality of lithium battery charging and discharging are greatly improved, and the fuel economy and service life of hybrid power system are improved.

Education Game Indonesian Old Museum Explorer using Fuzzy State Machine

Indonesia has a diverse culture from the form of creation, work, and intention, but over time Indonesian culture is eroded by the sophistication of the times. Using entertainment media such as games will be able to provide knowledge about culture through the game and can develop morale, using 3D interactive design of an object such as historical heritage will look close to real and with the Artificial Intelligence (AI) used in the opponent will make the game that is played becomes fun or not monotonous. This study uses the Fuzzy State Machine (FuSM) method which aims to shape character behaviour like human like behaviour. The results that can be generated from FuSM are behaviour that changes according to the conditions and rules that have been determined. This is where the AI that is formed will not always be the same in the same situation, all will be different depending on the conditions and rules that exist.

Educational Game Exploring the Old Museum Using Fuzzy State Machine

Indonesia has a diverse culture from the form of creation, work, and intention, but over time Indonesian culture is eroded by the sophistication of the times. Using entertainment media such as games will be able to provide knowledge about culture through the game and can develop morale, using 3D interactive design of an object such as historical heritage will look close to real and with the Aritificial Intelligence (AI) used in the opponent will make the game that is played becomes fun or not monotonous. This study uses the Fuzzy State Machine (FuSM) method which aims to shape character behavior like human like behavour. The results that can be generated from FuSM are behavior that changes according to the conditions and rules that have been determined. This is where the AI ​​that is formed will not always be the same in the same situation, all will be different depending on the conditions and rules that exist.

Fuzzy State Machine Energy Management Strategy for Hybrid Electric UAVs with PV/Fuel Cell/Battery Power System

Energy management strategies are the key technology for hybrid electric UAVs. This paper proposes a fuzzy state machine (FSM) energy management strategy with an online potential to control the power flow for the hybrid electric UAV which includes the photovoltaic, fuel cell, and battery power sources. The FSM strategy couples the fuzzy logical strategy with a state machine strategy where the fuzzy logical strategy controls the power split between a fuel cell and a battery and the state machine deals with the power flow of photovoltaics and battery. To evaluate the FSM strategy, a simulation platform integrating the hybrid power system model and UAV model is developed with a Matlab/Simulink tool. An existed online thermostat control strategy for the same type of UAV is employed to compare with the proposed strategy based on the developed platform. The energy management process and the state of each power source are analyzed under a given mission scenario. The comparison of the two strategies about the power and energy contribution rates of each power source, the battery state of charge, and the hydrogen consumption is presented. The results indicate that the FSM strategy can satisfy the demand power effectively during the mission and performs better than the thermostat control strategy on power distribution and fuel consumption.

Methods of functional-logic simulation of radiation-induced failures of electronic systems based on the fuzzy state machine model

The methods of functional-logic simulation of radiation-induced failures of electronic systems based on the Brauer fuzzy state machine model are presented. Recommendations for methods of radiationinduced failure prediction in the electronic component base are given. Theoretical and experimental results for the radiation fault tolerance of 1617 large-scale integrated circuits based on complementary metal–oxide–semiconductor structures are given as an example.

A fuzzy ontology for semantic modelling and recognition of human behaviour

We propose a fuzzy ontology for human activity representation, which allows us to model and reason about vague, incomplete, and uncertain knowledge. Some relevant subdomains found to be missing in previous proposed ontologies for this domain were modelled as well. The resulting fuzzy OWL 2 ontology is able to model uncertain knowledge and represent temporal relationships between activities using an underlying fuzzy state machine representation. We provide a proof of concept of the approach in work scenarios such as the office domain, and also make experiments to emphasize the benefits of our approach with respect to crisp ontologies. As a result, we demonstrate that the inclusion of fuzzy concepts and relations in the ontology provide benefits during the recognition process with respect to crisp approaches.

Design methodologies for the control of an unmanned sailing robot

Abstract The design of a sailing robot controller is addressed. Based on a simulation model, and validated by experiments with a real sailing robot, three controllers are compared: the classical fuzzy controller, the state machine controller and the fuzzy state machine controller. The fuzzy controller shows poor results because the robot is, like any sailboat, subject to dramatic change in its internal state (tacking, jibe, etc.) The “natural” approach is to describe the transitions between states as a classical state machine with transitions. We improve the concept with fuzzy transition, obtaining the same results in term of behavior but using effectiveness of fuzzy design to transpose human expertise in fuzzy rules. Although these controllers show good results in a wide range of wind force, we conclude that adaptive control is needed.

LEARNING OF FUZZY AUTOMATA

In this study, we revisit the well-known notion of fuzzy state machines and discuss their development through learning. The systematic development of fuzzy state machines has not been pursued as intensively as it could have been expected from the breadth of the possible usage of them as various modelling platforms. We concentrate on the generalization of the well known architectures exploited in Boolean system synthesis, namely Moore and Mealy machines and show how these can be implemented in terms of generic functional modules such as fuzzy JK flip-flops and fuzzy logic neurons (AND and OR neurons) organized in the form of logic processors. It is shown that the design of the fuzzy state machines can be accomplished through their learning. The detailed learning algorithm is presented and illustrated with a series of numeric examples. The study reveals an interesting option of constructing digital systems through learning: the original problem is solved in the setting of fuzzy state machines and afterwards "binarised" into the two-valued format realized via the standard digital hardware.

Fuzzy state machines to recognize totally unconstructed handwritten strokes

An automatic off-line character recognition system for totally unconstrained handwritten strokes is presented. A stroke representation is developed and described using five types of feature. Fuzzy state machines are defined to work as recognizers of strokes. An algorithm to obtain a deterministic fuzzy state machine from a stroke representation, that is capable of recognizing that stroke and its variants is presented. An algorithm is developed to merge two fuzzy state machines into one machine. The use of fuzzy machines to recognize strokes is clarified through a recognition algorithm. The learning algorithm is a complex of the previous algorithms. A set of 20 stroke classes was used in the learning and recognition stages. The system was trained on 5890 unnormalized strokes written by five writers. The learning stage produced a fuzzy state machine of 2705 states and 8640 arcs. A total of 6865 unnormalized strokes, written freely by five writers other than the writers of the learning stage, was used in testing. The recognition, rejection and error rates were 94.8%, 1.2% and 4.0%, respectively. The system can be more developed to deal with cursive handwriting.