Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Secure indoor navigation and operation of mobile robots

In future work environments, robots will navigate and work side by side to humans. This raises big challenges related to the safety of these robots. In this Dissertation, three tasks have been realized: 1) implementing a localization and navigation system based on StarGazer sensor and Kalman filter; 2) realizing a human-robot interaction system using Kinect sensor and BPNN and SVM models to define the gestures and 3) a new collision avoidance system is realized. The system works on generating the collision-free paths based on the interaction between the human and the robot.In zukünftigen Arbeitsumgebungen werden Roboter navigieren nebeneinander an Menschen. Das wirft Herausforderungen im Zusammenhang mit der Sicherheit dieser Roboter auf. In dieser Dissertation drei Aufgaben realisiert: 1. Implementierung eines Lokalisierungs und Navigationssystem basierend auf Kalman Filter: 2. Realisierung eines Mensch-Roboter-Interaktionssystem mit Kinect und AI zur Definition der Gesten und 3. ein neues Kollisionsvermeidungssystem wird realisiert. Das System arbeitet an der Erzeugung der kollisionsfreien Pfade, die auf der Wechselwirkung zwischen dem Menschen und dem Roboter basieren

Navegação autónoma para robôs de serviço em ambientes interiores usando faróis

Nowadays robots are becoming more present in our daily life performing a variety of on-demand services. In order to perform autonomous tasks the robot should be aware of its environment. To achieve this goal, there are three main problems to solve: mapping, localisation and navigation. During this work, we developed an autonomous welcome robot for the Institute of Electronics and Informatics Engineering of Aveiro ( IEETA ) with the capacity to receive requests from a visitor and guide him to the requested destination. At the end of this task, the robot should return autonomously to its docking station. To accomplish this goal we studied algorithms related to the three referred problems. As an example, a laser-based solution is used for the Simultaneous Localisation and Mapping procedure ( Gmapping ), the adaptive Monte Carlo localisation approach (AMCL) for the robot moving in 2-D and A* as a method for path planning. Improvements have been made regarding the use of these algorithms including in the environment an active localisation system based on the use of ultrasound beacons. The end result is an autonomous agent capable of mapping the building, self-localise in the resulting map and moving from current position to a specified target. It is also capable of path recalculation and minimal real-time collision avoidance while navigating.Hoje em dia, os robôs estão cada vez mais presentes no nosso quotidiano, fornecendo uma variedade de serviços e realizando as mais diversas tarefas, algumas delas de forma completamente autónoma. Para que o robô execute tarefas autónomas deve estar ciente do ambiente que o rodeia e conhecer a sua posição no mesmo. Para atingir esse objetivo, existem três problemas principais a serem resolvidos: mapeamento, localização e navegação. Durante este trabalho desenvolvemos um robô autónomo de boas-vindas para o Instituto de Engenharia Eletrónica e Informática de Aveiro com a capacidade de receber ordens de um visitante e guiá-lo até ao destino solicitado. No final desta tarefa, o robô retorna autonomamente ao seu local de partida, onde retoma a tarefa de carregamento. Para atingir este objetivo estudámos algoritmos relacionados com os três problemas referidos. Como exemplo, o algoritmo GMapping baseado em laser scans é usado para o processo de Mapeamento e Localização Simultânea, a abordagem adaptativa de localização de Monte Carlo é usada para que o robô que se mova no espaço e o algoritmo A* é aplicado para planeamento de um caminho. Foram feitas diversas melhorias em relação ao uso desses algoritmos, incluindo no ambiente um sistema de localização ativa baseado no uso de beacons ultra-som. O resultado final é um agente autónomo capaz de mapear o edifício, localizarse no mapa resultante e mover-se da posição atual para um destino especificado. Também é capaz de recalcular o caminho e evitar colisões mínimas em tempo real durante a navegação.Mestrado em Engenharia de Computadores e Telemátic

Design and Development of an Automated Mobile Manipulator for Industrial Applications

This thesis presents the modeling, control and coordination of an automated mobile manipulator. A mobile manipulator in this investigation consists of a robotic manipulator and a mobile platform resulting in a hybrid mechanism that includes a mobile platform for locomotion and a manipulator arm for manipulation. The structural complexity of a mobile manipulator is the main challenging issue because it includes several problems like adapting a manipulator and a redundancy mobile platform at non-holonomic constraints. The objective of the thesis is to fabricate an automated mobile manipulator and develop control algorithms that effectively coordinate the arm manipulation and mobility of mobile platform. The research work starts with deriving the motion equations of mobile manipulators. The derivation introduced here makes use of motion equations of robot manipulators and mobile platforms separately, and then integrated them as one entity. The kinematic analysis is performed in two ways namely forward & inverse kinematics. The motion analysis is performed for various WMPs such as, Omnidirectional WMP, Differential three WMP, Three wheeled omni-steer WMP, Tricycle WMP and Two steer WMP. From the obtained motion analysis results, Differential three WMP is chosen as the mobile platform for the developed mobile manipulator. Later motion analysis is carried out for 4-axis articulated arm. Danvit-Hartenberg representation is implemented to perform forward kinematic analysis. Because of this representation, one can easily understand the kinematic equation for a robotic arm. From the obtained arm equation, Inverse kinematic model for the 4-axis robotic manipulator is developed. Motion planning of an intelligent mobile robot is one of the most vital issues in the field of robotics, which includes the generation of optimal collision free trajectories within its work space and finally reaches its target position. For solving this problem, two evolutionary algorithms namely Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) are introduced to move the mobile platform in intelligent manner. The developed algorithms are effective in avoiding obstacles, trap situations and generating optimal paths within its unknown environments. Once the robot reaches its goal (within the work space of the manipulator), the manipulator will generate its trajectories according to task assigned by the user. Simulation analyses are performed using MATLAB-2010 in order to validate the feasibility of the developed methodologies in various unknown environments. Additionally, experiments are carried out on an automated mobile manipulator. ATmega16 Microcontrollers are used to enable the entire robot system movement in desired trajectories by means of robot interface application program. The control program is developed in robot software (Keil) to control the mobile manipulator servomotors via a serial connection through a personal computer. To support the proposed control algorithms both simulation and experimental results are presented. Moreover, validation of the developed methodologies has been made with the ER-400 mobile platform

Autonomous Driving Segway Robots

In this thesis, an autonomous driving robot has been proposed and built based on a two-wheel Segway self-balancing scooter. Sensors including LiDAR, camera, encoder, and IMU were implemented together with digital servos as actuators. The robot was tested simultaneously with the functionality features including obstacle avoidance based on fuzzy logic and 2D grid map, data fusion based on co-calibration, 2D simultaneously localization and mapping (SLAM) and path planning under different scenarios both indoor and outdoor. As a result, the robot initially has the ability of self-exploration with avoiding obstacles and constructing 2D grid map simultaneously. A simulation of the robot with same functionalities except data fusion has also been tested and performed based on robot operating system (ROS) and Gazebo as the simple comparison of the robot in real world.MSEElectrical Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/167349/1/Jiaming Liu - Final Thesis.pd

Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions

Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal. ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%. After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine. Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee. We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers. Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true. We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal

Analysis and Development of Computational Intelligence based Navigational Controllers for Multiple Mobile Robots

Navigational path planning problems of the mobile robots have received considerable attention over the past few decades. The navigation problem of mobile robots are consisting of following three aspects i.e. locomotion, path planning and map building. Based on these three aspects path planning algorithm for a mobile robot is formulated, which is capable of finding an optimal collision free path from the start point to the target point in a given environment. The main objective of the dissertation is to investigate the advanced methodologies for both single and multiple mobile robots navigation in highly cluttered environments using computational intelligence approach. Firstly, three different standalone computational intelligence approaches based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Cuckoo Search (CS) algorithm and Invasive Weed Optimization (IWO) are presented to address the problem of path planning in unknown environments. Next two different hybrid approaches are developed using CS-ANFIS and IWO-ANFIS to solve the mobile robot navigation problems. The performance of each intelligent navigational controller is demonstrated through simulation results using MATLAB. Experimental results are conducted in the laboratory, using real mobile robots to validate the versatility and effectiveness of the proposed navigation techniques. Comparison studies show, that there are good agreement between them. During the analysis of results, it is noticed that CS-ANFIS and IWO-ANFIS hybrid navigational controllers perform better compared to other discussed navigational controllers. The results obtained from the proposed navigation techniques are validated by comparison with the results from other intelligent techniques such as Fuzzy logic, Neural Network, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and other hybrid algorithms. By investigating the results, finally it is concluded that the proposed navigational methodologies are efficient and robust in the sense, that they can be effectively implemented to solve the path optimization problems of mobile robot in any complex environment