URBANO: A Tour-Guide Robot Learning to Make Better Speeches

—Thanks to the numerous attempts that are being made to develop autonomous robots, increasingly intelligent and cognitive skills are allowed. This paper proposes an automatic presentation generator for a robot guide, which is considered one more cognitive skill. The presentations are made up of groups of paragraphs. The selection of the best paragraphs is based on a semantic understanding of the characteristics of the paragraphs, on the restrictions defined for the presentation and by the quality criteria appropriate for a public presentation. This work is part of the ROBONAUTA project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create "awareness" in a robot guide. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. To achieve this goal, the system has to perform the optimized decision making, in different phases. The modeling of the quality index of the presentation is made using fuzzy logic and it represents the beliefs of the robot about what is good, bad, or indifferent about a presentation. This fuzzy system is used to select the most appropriate group of paragraphs for a presentation. The beliefs of the robot continue to evolving in order to coincide with the opinions of the public. It uses a genetic algorithm for the evolution of the rules. With this tool, the tour guide-robot shows the presentation, which satisfies the objectives and restrictions, and automatically it identifies the best paragraphs in order to find the most suitable set of contents for every public profil

Learning to Be a Good Tour-Guide Robot

Thanks to the numerous attempts that are being made to develop autonomous robots, increasingly intelligent and cognitive skills are allowed. This paper proposes an automatic presentation generator for a robot guide, which is considered one more cognitive skill. The presentations are made up of groups of paragraphs. The selection of the best paragraphs is based on a semantic understanding of the characteristics of the paragraphs, on the restrictions defined for the presentation and by the quality criteria appropriate for a public presentation. This work is part of the ROBONAUTA project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create "awareness" in a robot guide. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. To achieve this goal, the system has to perform the optimized decision making, in different phases. The modeling of the quality index of the presentation is made using fuzzy logic and it represents the beliefs of the robot about what is good, bad, or indifferent about a presentation. This fuzzy system is used to select the most appropriate group of paragraphs for a presentation. The beliefs of the robot continue to evolving in order to coincide with the opinions of the public. It uses a genetic algorithm for the evolution of the rules. With this tool, the tour guide-robot shows the presentation, which satisfies the objectives and restrictions, and automatically it identifies the best paragraphs in order to find the most suitable set of contents for every public profile

Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

Wavefront Propagation and Fuzzy Based Autonomous Navigation

Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments

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

Development of a Mobile Robot Local Navigation System Based on Fuzzy-Logic Control and Actual Virtual Target Switching

Robot local path planning in an unknown and changing environment with uncertainties is one of the most challenging problems in robotics which involves the integration of many different bodies of knowledge. This makes mobile robotics a challenge worldwide which for many years has been investigated by researchers. Therefore in this thesis, a new fuzzy logic control system is developed for reactive navigation of a behavior-based mobile robot. The motion of a Pioneer 3TM mobile robot was simulated to show the algorithm performance. The robot perceives its environment through an array of eight sonar range finders and self positioning-localization sensors. The robot environment consists of walls and dead end traps from any size and shape, as well as other stationary obstacles and it is assumed to be fully unknown. Robot behaviors consist of obstacle avoidance, target seeking, speed control, barrier following and local minimum avoidance. While the fuzzy logic body of the algorithm performs the main tasks of obstacle avoidance, target seeking, and speed adjustment, an actual-virtual target switch strategy integrated with the fuzzy logic algorithm enables the robot to show wall following behavior when needed. This combinational approach which uses a new kind of target shift, significantly results in resolving the problem of multiple minimum in local navigation which is an advantage beyond the pure fuzzy logic approach and the common virtual target switch techniques. In this work, multiple traps may have any type of shape or arrangement from barriers forming simple corners and U-shape dead ends to loops, maze, snail shape, and many others. Under the control of the algorithm, the mobile robot makes logical trajectories toward the target, finds best ways out of dead ends, avoids any types of obstacles, and adjusts its speed efficiently for better obstacle avoidance and according to power considerations and actual limits. From TRAINER Software and Colbert Program which were used in the simulation work, the system managed to solve all the problems in sample environments and the results were compared with results from other related methods to show the effectiveness and robustness of the proposed approach