Unified Behavior Framework for Reactive Robot Control

Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: (1) simplifies development and testing; (2) promotes the reuse of code; (3) supports designs that scale easily into large hierarchies while restricting code complexity; and (4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. When a hybrid or three layer control architecture includes the unified behavior framework, a common interface is shared by all behaviors, leaving the higher order planning and sequencing elements free to interchange behaviors during execution to achieve high level goals and plans. The framework\u27s ability to compose structures from independent elements encourages experimentation and reuse while isolating the scope of troubleshooting to the behavior composition. The ability to use elemental components to build and evaluate behavior structures is demonstrated using the Robocode simulation environment. Additionally, the ability of a reactive controller to change its active behavior during execution is shown in a goal seeking robot implementation

Navigation and Control of Automated Guided Vehicle using Fuzzy Inference System and Neural Network Technique

Automatic motion planning and navigation is the primary task of an Automated Guided Vehicle (AGV) or mobile robot. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. Artificial Intelligence based decision making systems have become increasingly more successful as they are capable of handling large complex calculations and have a good performance under unpredictable and imprecise environments. This research focuses on developing Fuzzy Logic and Neural Network based implementations for the navigation of an AGV by using heading angle and obstacle distances as inputs to generate the velocity and steering angle as output. The Gaussian, Triangular and Trapezoidal membership functions for the Fuzzy Inference System and the Feed forward back propagation were developed, modelled and simulated on MATLAB. The reserach presents an evaluation of the four different decision making systems and a study has been conducted to compare their performances. The hardware control for an AGV should be robust and precise. For practical implementation a prototype, that functions via DC servo motors and a gear systems, was constructed and installed on a commercial vehicle

Autonomous navigation with deadlock detection and avoidance

This paper studies alternatives to solve the problem of autonomous mobile robots navigation in unknown indoor environments. The navigation system uses fuzzy logic to combine the information obtained from range sensors and the navigational data to plan the robot’s movements. The strategy is built upon five modules: i) target following, ii) obstacle avoidance, iii) possible path, iv) deadlock detection and v) wall following. Given a possible path and obstacles near the environment of the robot, the controller will modulate the output velocity in order to go to the target and avoid collisions. In case of dead lock situations, a method that enables the robot to detect, escape and reach the target is proposed. The performance and behavior of the proposed navigational system was evaluated through simulations in different conditions, where the effectiveness of the proposed method is demonstrated and compared with previous results.Sociedad Argentina de Informática e Investigación Operativ

Autonomous navigation with deadlock detection and avoidance

This paper studies alternatives to solve the problem of autonomous mobile robots navigation in unknown indoor environments. The navigation system uses fuzzy logic to combine the information obtained from range sensors and the navigational data to plan the robot’s movements. The strategy is built upon five modules: i) target following, ii) obstacle avoidance, iii) possible path, iv) deadlock detection and v) wall following. Given a possible path and obstacles near the environment of the robot, the controller will modulate the output velocity in order to go to the target and avoid collisions. In case of dead lock situations, a method that enables the robot to detect, escape and reach the target is proposed. The performance and behavior of the proposed navigational system was evaluated through simulations in different conditions, where the effectiveness of the proposed method is demonstrated and compared with previous results.Sociedad Argentina de Informática e Investigación Operativ

Behavior-based Fuzzy Control For A Mobile Robot With Non-holonomic Constraints

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2005Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2005Bu çalışmada robotik alanında yeni yaklaşımlar olan davranış temelli robotik ve bulanık mantık konuları gerçek zamanda mobil robot uygulamaları bakımından incelenmiş, dört ilerlemeli, dört yönelmeli bir mobil robot için Engelden Sakın , Hedefe Git , Duvarı İzle , Yola Teğet İlerle , Avare Gez davranışları oluşturulmuştur. Bu davranışların içinden Engelden Sakın , Hedefe Git ve Duvarı İzle davranışları için sonar sensör matematik modelleri oluşturulmuş ve bu davranışların yapısında bulanık mantık yaklaşımı kullanılmıştır. Mobil robot, kinetik ve dinamik olarak holonomik olmayan kısıtları kullanılarak modellenmiştir ve simülasyon sırasında mobil robotun pozisyonu, tekerlek ve robot yönelimleri, tekerlek ve robot hızları, tekerlek torkları gibi parametreler izlenebilmektedir. Davranışlar da, simülasyon ortamında kazanımları, bulanık mantık işleme yapıları, gerçek zaman uygulanabilirliği ve davranışların koordine edilmeleri bakımından incelenmiştir. Bu çalışma gerçek bir robotta yapılacak deneyler için temel teşkil etmektedir.In this study, the new approaches to the robotics subject, behavior-based robotics and fuzzy logic control are investigated for the real-time applications of mobile robots, Avoid Obstacle , Move to Goal , Wall Following , Head-on , Wander behaviors are built up for a four-wheel driven and four-wheel steered mobile robot. Sonar sensor mathematical models are formed for Avoid Obstacle , Move to Goal and Wall Following behaviors and fuzzy logic concepts are used in the structure of these behaviors. The mobile robot is modelled kinematically and dynamically considering the non-holonomic constraints. The posture and speed of the robot and the configurations, speeds and torques of the wheels can be obtained from the simulation. The behaviors are investigated regarding their gains, fuzzy inference structures, real-time applicabilities and thein coordination. This study constitutes basis for the experiments on a real mobile robot.Yüksek LisansM.Sc