Autonomous navigation and multi-sensorial real-time mocalization for a mobile robot

Doutoramento em Engenharia MecânicaO principio por detrás da proposta desta tese é a navegação de ambientes utilizando uma sequência de instruções condicionadas nas observações feitas pelo robô. Esta sequência é denominada como uma 'missão de navegação'. A interacção com um robô através de missões permitirá uma interface mais eficaz com humanos e a navegação de ambientes de maior escala e duma forma mais simplificada. No entanto, esta abordagem abre problemas novos no que diz respeito à forma como os dados sensoriais devem ser representados e utilizados. Neste trabalho representações binárias foram introduzidas para facilitar a integração dos dados multi-sensoriais, a dimensionalidade da qual foi reduzida através da utilização de Misturas de Distribuições de tipo Bernoulli. Foi também aplicada a técnica de cadeias de Markov ocultas (Hidden Markov Models), que contou com o desenvolvimento e a utilização dum modelo de cadeia de Markov original, esta que consegue explorar a informação contextual da sequência da missão. Uma aplicação que surgiu da aplicação do método de localização foi a criação de representações topologicas do ambiente sem ter que previamente recorrer à criação de mapas geométricos. Outras contribuições incluem a aplicação de métodos para a extracção de propriedades locais em imagens e o desenvolvimento de propriedades extraídas a partir de varrimentos dum medidor de distancia laser.This thesis evaluates the requisites for the specification of mobile robot 'Missions' for navigation within environments that are typically used by human beings. The principal idea behind the proposal of this thesis was to allow localization and navigation by providing a sequence of instructions, the execution of each instruction being conditional on the expected sensor data. This approach to navigation is expected to lead to new applications which will include the autonomous navigation of environments of very large scale. It is also expected to lead to a more intuitive interaction between mobile robots and humans. However, the concept of the navigation Mission opens up new problems namely in the way in which the sequence of instructions and the expected observations are to be represented. To solve this problem, binary features were used to integrate observations from multiple sensors, the dimensionality of which was reduced by modelling the binary data as a Finite Mixture Model comprised of Bernoulli distributions. Another original contribution was the modification of the Markov Chains used in Hidden Markov Models to enable the use of the sequential context in which the expected observations are specified in the navigation Mission. The localization method that was developed enabled the direct creation of a topological representation of an environment without recourse to an intermediate geometric map. Other contributions include developments that were made in the characterisation of images through the application of local features and of laser range scans through the creation of original features based on the scan contour and free-area properties

Pose-Tracking and Initialization of an Autonomous Mobile Robot Using Ultrasonics and Laser Scanner

本論文旨在探討分別應用兩種外部感測器：超音波及雷射掃描器，於自動導航車姿態追蹤與姿態初值化之方法學與實現技術。本文首先發展一種以FPGA為基礎的超音波姿態追蹤系統，透過超音波飛行時間(time of flight)測量法，並結合模糊適應增廣訊息濾波技術(fuzzy adaptive extended information filtering)來增進姿態估測的精確度與強健度。模糊適應增廣訊息濾波技術在於避免非線性增廣訊息濾波技術的發散問題。其次，本文提出一種以矩形模型及2D雷射掃描器為基礎的低複雜性和精確的姿態追蹤演算法，並結合增廣型卡爾曼濾波策略(extended Kalman filter)。最後，在環境模型已知的情況下，本文另提出一種以2D雷射掃描器為基礎的姿態初值化演算法，以便決定自動導航車在環境中的姿態。透過電腦模擬及實驗數據足以證明本論文所提之姿態追蹤與姿態初值化技術的可行性與有效性。This thesis develops methodologies and techniques for pose-tracking and pose initialization of an autonomous mobile robot (AMR) using two different external sensors: ultrasonics and laser scanner. First, a novel FPGA-based ultrasonic pose-tracking system by fusing the time-of-flight (TOF) readings together with the FAEIF algorithm is proposed to improve the accuracy and robustness of pose estimation for the AMR. The FAEIF-based sensor fusion approach is presented to circumvent the nonlinear filter divergence problems. Second, a low-complexity and accurate pose-tracking EKF algorithm is proposed based on rectangular model and a 2-D laser scanner. Finally, a pose initialization scheme based on a 2-D laser scanner is presented to determine the initial pose of the AMR given that the environmental model is known. Numerous simulations and experimental results are provided to verify the feasibility and effectiveness of the proposed pose-tracking and pose initialization methods.Chinese Abstract i English Abstract ii Acknowledgments iii Contents iv List of Figures viii List of Tables xii Nomenclature xiii Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Literature Review 3 1.3 Contributions of the Thesis 5 1.4 Organization of the Thesis 6 Chapter 2 Ultrasonic-Based Pose-Tracking 8 2.1 Introduction 8 2.2 Description of the Autonomous Mobile Robot and Control System 10 2.3 FPGA-based Ultrasonic Pose-Tracking System 12 2.3.1 Field Programmable Gate Array (FPGA) 13 2.3.2 Design Software 15 2.4 Physical Configuration and Mathematical Description of the FPGA-based Ultrasonic Pose-Tracking System 18 2.5 FAEIF-based Dynamic Pose Estimation Algorithm 22 2.5.1 Fuzzy Adaptive EIF (FAEIF) 25 2.5.2 FAEIF-based Pose Estimation Algorithm 28 2.6 Simulation, Experimental Results and Discussion 31 2.6.1 Computer Simulation 31 2.6.2 Dynamic Experiments 34 2.6.2.1 Static Pose Estimate 35 2.6.2.2 Dynamic Pose-Tracking 36 2.7 Concluding Remarks 39 Chapter 3 Laser-Based Pose-Tracking 40 3.1 Introduction 40 3.2 Laser Scanning System 41 3.2.1 Basics of Laser Measurement System 42 3.2.2 Hardware Setup 44 3.2.2.1 Required Components 44 3.2.2.2 Serial Interface for Data Exchange 46 3.2.2.3 Communication Setup and Software 46 3.3 Pose-Tracking Algorithm 48 3.3.1 Environmental Model 49 3.3.2 Measurement Model 50 3.3.3 EKF-based Pose-Tracking Algorithm 52 3.3.4 Line Extraction 54 3.3.4.1 Validation Gate 55 3.3.4.2 Range-Weighted Hough Transform 57 3.4 Simulations, Experimental Results and Discussion 60 3.4.1 Computer Simulations 60 3.4.2 Experiments 63 3.5 Concluding Remarks 67 Chapter 4 Pose Initialization 68 4.1 Introduction 68 4.2 Clustering and Line Segment Extraction 69 4.3 Pose Initialization 77 4.3.1 Static Pose Initialization 77 4.3.2 Dynamic Pose Initialization 80 4.4 Experimental Results and Discussion 81 4.4.1 Static Experiment 82 4.4.2 Dynamic Experiments 83 4.5 Concluding Remarks 88 Chapter 5 Conclusions and Future Work 89 5.1 Conclusions 89 5.2 Future Work 90 References 92 Appendix A 9