Behavior Based Robot Navigation: Resolving Behavior Conflicts Using Fuzzy Inference System

Volume 2 ◽  
2004 ◽  
Author(s):  
S. Parasuraman ◽  
V. Ganapathy ◽  
Bijan Shirinzadeh
Keyword(s):  
Control Systems ◽  
Robot Control ◽  
Fuzzy Inference ◽  
Robot Navigation ◽  
Sensory Information ◽  
System Control ◽  
Inference System ◽  
Control Decision ◽  
Behavior Based ◽  
Perception Action

Conflict resolution is the control decision process, which should be taken as a result of the firing among several fuzzy behavior rules. In the Behavior-based Robot Navigation System, control of a robot is shared between a set of perception-action units, called behaviors selection. In other words, the behavior selection is the way that an agent selects the most appropriate or the most relevant next action to take at a particular moment, when facing a particular problem. Based on selective sensory information, each behavior produces immediate reaction to control the robot with respect to a particular objective, i.e., a narrow aspect of the robot’s overall task such as obstacle avoidance or goal seek. Behaviors with different and possibly incommensurable objectives may produce conflicting actions that are seemingly irreconcilable. The main issue in the design of behavior based robot control systems is the formulation of effective mechanism to coordinate the behavior’s activities without any behavior conflicts during navigation. This paper presents the techniques to design the behaviors and resolve the behaviors conflicts, which are based on the Situation Context of Applicability (SCA) of the environments.

scholarly journals Selected Aircraft Throttle Controller With Support Of Fuzzy Expert Inference System

2014 ◽  
Vol 30 (1) ◽  
pp. 87-97
Author(s):  
Józef Żurek ◽  
Norbert Grzesik ◽  
Jakub Kurpas
Keyword(s):  
Fuzzy Logic ◽  
Performance Optimization ◽  
Fuzzy Inference ◽  
Aircraft Engine ◽  
Control Surface ◽  
Project Work ◽  
System Control ◽  
Range Analysis ◽  
Inference System ◽  
Engine Maintenance

Abstract The paper describes Zlin 143Lsi aircraft engine work parameters control support method – hourly fuel flow as a main factor under consideration. The method concerns project of aircraft throttle control support system with use of fuzzy logic (fuzzy inference). The primary purpose of the system is aircraft performance optimization, reducing flight cost at the same time and support proper aircraft engine maintenance. Matlab Software and Fuzzy Logic Toolbox were used in the project. Work of the system is presented with use of twenty test samples, five of them are presented graphically. In addition, system control surface, included in the paper, supports system all work range analysis.

scholarly journals A Fuzzy Inference System for Management Control Tools

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2145
Author(s):  
Carolina Nicolas ◽  
Javiera Müller ◽  
Francisco-Javier Arroyo-Cañada
Keyword(s):  
Decision Making ◽  
Organizational Performance ◽  
Control Systems ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Small And Medium Enterprises ◽  
Belief Systems ◽  
Management Control ◽  
Inference System ◽  
Medium Enterprises

Despite the importance of the role of small and medium enterprises (SMEs) in developing and growing economies, little is known regarding the use of management control tools in them. In management control in SMEs, a holistic system needs to be modeled to enable a careful study of how each lever (belief systems, boundary systems, interactive control systems, and diagnostic control systems) affects the organizational performance of SMEs. In this article, a fuzzy logic approach is proposed for the decision-making system in management control in small and medium enterprises. C. Mamdani fuzzy inference system (MFIS) was applied as a decision-making technique to explore the influence of the use of management control tools on the organizational performance of SMEs. Perceptions data analysis is obtained through empirical research.

scholarly journals PERANCANGAN BEHAVIOR-BASED ROBOT DENGAN ALGORITMA FUZZY Q-LEARNING (FQL) PADA SISTEM NAVIGASI ROBOT OTONOM BERODA DALAM MEDAN YANG TIDAK TERSTRUKTUR

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Made Santo Gitakarma
Keyword(s):  
Reinforcement Learning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Q Learning ◽  
Behavior Based

Pada banyak aplikasi robotika, seperti sistem navigasi robot mandiri atau robot otonom yang bergerak dengan mandiri pada lingkungan tidak terstruktur, sangat sulit atau tidak mungkin memperoleh model matematik yang tepat dari interaksi robot dengan lingkungannya. Untuk itu diperlukan pendekatan sistem kendali robot yang dikenal dengan sistem kendali Behavior-Based Robot (BBR). Pada pendekatan ini, sistem diuraikan menjadi beberapa modul yang masing-masingnya bertanggung jawab untuk melakukan satu perilaku (behavior). Salah satu metode pembelajaran yang paling cocok untuk aplikasi robot adalah Reinforcement Learning (RL), dengan jenis algoritma Q-learning. Kombinasi Q-learning dengan Fuzzy Inference System (FIS) dikenal dengan nama Fuzzy Q-Learning (FQL). Berdasarkan percobaan yang dilakukan sebanyak 3 kali pada robot beroda dapat disimpulkan bahwa waktu rata-rata robot kembali ke Homebase yaitu 1 menit 10 detik. Sedangkan waktu rata-rata robot dalam mematikan api lilin adalah 2 detik. Sehingga dapat dikatakan robot yang dibuat mempunyai kinerja yang cukup baik.

Optimalisasi Lampu Lalu Lintas dengan Fuzzy Logic

2013 ◽  
Vol 5 (2) ◽  
pp. 58-62 ◽  
Author(s):  
Adhitya Yoga Yudanto ◽  
Marvin Apriyadi ◽  
Kevin Sanjaya
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Inference ◽  
System Control ◽  
Light Control ◽  
Traffic Light ◽  
Inference System ◽  
Traffic Lights ◽  
The Road ◽  
Traffic Light Control

The traffic lights problem is already commonly found in large cities. The traffic lights are supposed to control the flow of the road, but sometimes causes a congestion. This happens because the distribution of the time are all the same for all lines, without seeing the condition of the density of each lane. There’s one effort that can be done to overcome this problem, is to create a traffic light control system. With this system, the congestion that occurs around the traffic lights can be reduced. This system is using fuzzy logic. Fuzzy logic is one of computer science that studies about the value of truth that worth a lot. For example, a air conditioning system control subway Sendai in Japan. As for making a traffic light control system, the author using Fuzzy Inference System (FIS) that already exist in the application of MATLAB R2013a with Mamdani method. Index Terms —fuzzy logic, traffic lights, MATLAB.

Wind Turbine Pitch Angle Control Based on ANFIS

2013 ◽  
Vol 291-294 ◽  
pp. 507-512
Author(s):  
Jun Xiao ◽  
Jiang Xiao ◽  
Wei Chen
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Simulation Analysis ◽  
Dynamic Performance ◽  
System Control ◽  
Inference System ◽  
Pitch Control ◽  
Neural Fuzzy

Wind turbine pitch control need a higher demand due to the random changes in wind speed, this paper proposes using adaptive neural fuzzy inference system to control wind turbine pitch, and constructs a mathematical model of the wind turbine. Consider the error between the measured and the actual value of generator speed as the input to the controller. Take a simulation analysis to the adaptive neural fuzzy inference system controller under random wind speed. The simulation results show that the adaptive neural fuzzy inference system control strategy has good robustness and dynamic performance, to improve wind turbine pitch control is feasible and effective.

scholarly journals AN APPROACH DESIGNING AUTONOMOUS ROBOT NAVIGATION SYSTEM BASED ON BEHAVIOR COORDINATION

2017 ◽  
Vol 24 (4) ◽  
pp. 353-367
Author(s):  
Long Thanh Ngo ◽  
Long The Pham ◽  
Phuong Hoang Nguyen
Keyword(s):  
Navigation System ◽  
Fuzzy Inference ◽  
Robot Navigation ◽  
Autonomous Robot ◽  
Inference System ◽  
Unstructured Environment ◽  
Individual Task ◽  
Simulation Results ◽  
Autonomous Robot Navigation ◽  
Wall Following

Robot navigation using fuzzy behavior is suited in unknown and unstructured environment in which each behavior have an individual task. This paper deals with an approach designing autonomous robot navigation system based on fuzzy behaviors including collision avoidance, wall-following, go-to-target. The proposed hierarchy of fuzzy behaviors is used to fuse the command in which each behavior is a fuzzy inference system and its outputs are fuzzy sets. Its inputs are information fused from sensors using fuzzy directional relationship. The simulation results with some statistics show that the system works correctly. 

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