Terrain sensing and estimation for dynamic outdoor mobile robots

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (p. 120-125).In many applications, mobile robots are required to travel on outdoor terrain at high speed. Compared to traditional low-speed, laboratory-based robots, outdoor scenarios pose increased perception and mobility challenges which must be considered to achieve high performance. Additionally, high-speed driving produces dynamic robot-terrain interactions which are normally negligible in low speed driving. This thesis presents algorithms for estimating wheel slip and detecting robot immobilization on outdoor terrain, and for estimating traversed terrain profile and classifying terrain type. Both sets of algorithms utilize common onboard sensors. Two methods are presented for robot immobilization detection. The first method utilizes a dynamic vehicle model to estimate robot velocity and explicitly estimate longitudinal wheel slip. The vehicle model utilizes a novel simplified tire traction/braking force model in addition to estimating external resistive disturbance forces acting on the robot. The dynamic model is combined with sensor measurements in an extended Kalman filter framework. A preliminary algorithm for adapting the tire model parameters is presented. The second, model-free method takes a signal recognition-based approach to analyze inertial measurements to detect robot immobilization. Both approaches are experimentally validated on a robotic platform traveling on a variety of outdoor terrains. Two detector fusion techniques are proposed and experimentally validated which combine multiple detectors to increase detection speed and accuracy. An algorithm is presented to classify outdoor terrain for high-speed mobile robots using a suspension mounted accelerometer. The algorithm utilizes a dynamic vehicle model to estimate the terrain profile and classifies the terrain based on spatial frequency components of the estimated profile. The classification algorithm is validated using experimental results collected with a commercial automobile driving in real-world conditions.by Christopher Charles Ward.S.M

Slip Modelling, Estimation and Control of Omnidirectional Wheeled Mobile Robots with Powered Caster Wheels

Ph.DDOCTOR OF PHILOSOPH

타이어 모델을 사용한 자율 드리프트 주행 제어 설계 및 분석

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 이동준.본 논문에서는 Wheeled Mobile Robot(WMR)의자율드리프트 드라이빙 컨트롤러를 디자인 하고 분석하며, 이를 상용 프로그램인 CarSim을 사용한 시뮬레이션을 통하여 알고리즘을 검증 한다. 첫째로, WMR의 다이나믹스와 타이어 모델을 정의 하고, 이러한 모델로 인한 제약 사항에 대하여 논의한다. 다음으로, 사람의 관점에서 드리프트 드라이빙을 분석하고, 드리프트 드라이빙 제어기의 제어 목적을 정의한다. (차량의 방향과 요 각속도를 제어한다.) 드리프트 드라이빙 제어기는 고-레벨 제어, 목표 값을 찾기 위한 최적화 그리고 고-게인 제어로 구성된다. 다음으로, 제어하지 않는 속도에 대한 분석을 진행하였다. 마지막으로 제안한 알고리즘을 CarSim 시뮬 레이터를 이용하여 검증하였다. 정상 상태의 드리프트 시뮬레이션 결과와, 헤어핀 경로에 대한 드리프트 시뮬레이션 결과를 제시 한다.Control design and analysis of Wheeled Mobile Robot(WMR) autonomous drift-driving and the simulation experiment using the CarSim simulator are presented and the analysis of the controller proceeds. We first introduce WMR dynamics, tire model and problem formulation of the WMR. We then design drift-driving control using human strategy (control side slip angle and yaw rate). The drift-driving control consists of high-level control, optimization to find desired control input and high-gain control. We analyze the uncontrolled velocity dynamics and stability of the controller. The CarSim simulation results of drift-driving on steady-state equilibriums and the hairpin path with the desired yaw rate are provided.List of Figures - v List of Tables - vi Abbreviations - vii 1 Introduction - 1 1.1 Motivation and related works . . . . . . . . . . . . . . . . . . . . 1 1.2 Contribution of this work . . . . . . . . . . . . . . . . . . . . . . 3 2 System Modeling - 5 2.1 Model dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Tire model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Problemformulation . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Drift-Driving Control Design - 10 3.1 High-level control . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.3 High-gain control . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Analysis of Control - 17 4.1 Internal dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 Stability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5 Simulation Results - 25 5.1 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Steady-state drift-driving . . . . . . . . . . . . . . . . . . . . . . 27 5.3 Hairpin turn drift-driving . . . . . . . . . . . . . . . . . . . . . . 33 6 Conclusion and Future Work - 40 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Maste

Gain-scheduling LPV control for autonomous vehicles including friction force estimation and compensation mechanism

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This study presents a solution for the integrated longitudinal and lateral control problem of urban autonomousvehicles. It is based on a gain-scheduling linear parameter-varying (LPV) control approach combined with the use of anUnknown Input Observer (UIO) for estimating the vehicle states and friction force. Two gain-scheduling LPV controllers are usedin cascade configuration that use the kinematic and dynamic vehicle models and the friction and observed states provided bythe Unknown Input Observer (UIO). The LPV–UIO is designed in an optimal manner by solving a set of linear matrix inequalities(LMIs). On the other hand, the design of the kinematic and dynamic controllers lead to solve separately two LPV–LinearQuadratic Regulator problems formulated also in LMI form. The UIO allows to improve the control response in disturbanceaffected scenarios by estimating and compensating the friction force. The proposed scheme has been integrated with atrajectory generation module and tested in a simulated scenario. A comparative study is also presented considering the casesthat the friction force estimation is used or not to show its usefulnessPeer ReviewedPostprint (author's final draft

Autonomous robot systems and competitions: proceedings of the 12th International Conference

This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories, methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions. All accepted contributions are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic

TS-MPC for autonomous vehicles Including a TS-MHE-UIO estimator

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This approach is based on the use of a cascade control where the external loop solves the position control using a novel Takagi Sugeno-Model Predictive Control (TS-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a Takagi Sugeno-Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the kinematic and dynamic models of the vehicle. In addition, a novel Takagi-Sugeno estimator-Moving Horizon Estimator-Unknown Input Observer (TS-MHE-UIO) is presented. This method estimates the dynamic states of the vehicle optimally as well as the force of friction acting on the vehicle that is used to reduce the control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO techniques is that using the TS model formulation of the vehicle allows us to solve the nonlinear problem as if it were linear, reducing computation times by 10-20 times. To demonstrate the potential of the TS-MPC, we propose a comparison between three methods of solving the kinematic control problem: Using the nonlinear MPC formulation (NL-MPC) with compensated friction force, the TS-MPC approach with compensated friction force, and TS-MPC without compensated friction force.This work was supported by the Spanish Min-istry of Economy and Competitiveness (MINECO) and FEDER through theProjects SCAV (ref. DPI2017-88403-R) and HARCRICS (ref. DPI2014-58104-R). The corresponding author, Eugenio Alcalá, is supported under FI AGAURGrant (ref 2017 FI B00433).Peer ReviewedPostprint (author's final draft