Neural Network Controller Design for a Mobile Robot Navigation; a Case Study

Mobile robot are widely applied in various aspect of human  life. The main issue of this type of robot is how to navigate safely to reach the goal or finish the assigned task  when applied autonomously in dynamic and uncertain environment. The  ap- plication of artificial intelligence, namely neural   network,  can provide a ”brain” for the robot to navigate safely in completing the assigned task. By applying neural network, the complexity of mobile robot control can be  reduced by choosing the right model of the system, either   from mathematical modeling or directly taken from the input of sensory data  information. In this study, we compare the presented methods of previous  researches that applies neural network to mobile robot navigation. The comparison  is started  by considering  the right  mathematical model for the robot, getting the Jacobian  matrix  for online training, and giving the achieved input model to  the designed neural network layers in order to get the estimated position of the robot. From this literature study, it  is concluded that the consideration of both kinematics and dynamics modeling  of the robot will result in better performance since the exact parameters of the system are known

Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning

Slip and skid compensation is crucial for mobile robots' navigation in outdoor environments and uneven terrains. In addition to the general slipping and skidding hazards for mobile robots in outdoor environments, slip and skid cause uncertainty for the trajectory tracking system and put the validity of stability analysis at risk. Despite research in this field, having a real-world feasible online slip and skid compensation is still challenging due to the complexity of wheel-terrain interaction in outdoor environments. This paper presents a novel trajectory tracking technique with real-world feasible online slip and skid compensation at the vehicle-level for skid-steering mobile robots in outdoor environments. The sliding mode control technique is utilized to design a robust trajectory tracking system to be able to consider the parameter uncertainty of this type of robot. Two previously developed deep learning models [1], [2] are integrated into the control feedback loop to estimate the robot's slipping and undesired skidding and feed the compensator in a real-time manner. The main advantages of the proposed technique are (1) considering two slip-related parameters rather than the conventional three slip parameters at the wheel-level, and (2) having an online real-world feasible slip and skid compensator to be able to reduce the tracking errors in unforeseen environments. The experimental results show that the proposed controller with the slip and skid compensator improves the performance of the trajectory tracking system by more than 27%

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

Ph.DDOCTOR OF PHILOSOPH

Wheeled Mobile Robots: State of the Art Overview and Kinematic Comparison Among Three Omnidirectional Locomotion Strategies

In the last decades, mobile robotics has become a very interesting research topic in the feld of robotics, mainly because of population ageing and the recent pandemic emergency caused by Covid-19. Against this context, the paper presents an overview on wheeled mobile robot (WMR), which have a central role in nowadays scenario. In particular, the paper describes the most commonly adopted locomotion strategies, perception systems, control architectures and navigation approaches. After having analyzed the state of the art, this paper focuses on the kinematics of three omnidirectional platforms: a four mecanum wheels robot (4WD), a three omni wheel platform (3WD) and a two swerve-drive system (2SWD). Through a dimensionless approach, these three platforms are compared to understand how their mobility is afected by the wheel speed limitations that are present in every practical application. This original comparison has not been already presented by the literature and it can be used to improve our understanding of the kinematics of these mobile robots and to guide the selection of the most appropriate locomotion system according to the specifc application

Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles

Trajectory tracking is an essential capability of robotics operation in industrial automation. In this article, an artificial neural controller is proposed to tackle trajectory-tracking problem of an autonomous ground vehicle (AGV). The controller is implemented based on fractional order proportional integral derivative (FOPID) control that was already designed in an earlier work. A non-holonomic model type of AGV is analysed and presented. The model includes the kinematic, dynamic characteristics and the actuation system of the VGA. The artificial neural controller consists of two artificial neural networks (ANNs) that are designed to control the inputs of the AGV. In order to train the two artificial neural networks, Levenberg-Marquardt (LM) algorithm was used to obtain the parameters of the ANNs. The validation of the proposed controller has been verified through a given reference trajectory. The obtained results show a considerable improvement in term of minimising trajectory tracking error over the FOPID controller

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

Control of Flexible Manipulator Robots Based on Dynamic Confined Space of Velocities: Dynamic Programming Approach

Linear Parameter Varying models-based Model Predictive Control (LPV-MPC) has stood out in manipulator robots because it presents well-rejection to dynamic uncertainties in flexible joints. However, it has become too weak when the MPC's optimization problem does not include kinematic constraints-based conditions. This paper uses dynamic confined space of velocities (DCSV) to include these conditions as a recursive polytopic constraint, guaranteeing optimal dependency on a simplex scheduling parameter. To this end, the local frame's velocities and torque/force preload of joints (related to violation of kinematic constraints) are associated with different time scale dynamics such that DCSV correlates them as a polytope. So, a classical LPV-MPC will be updated using a dynamic programming approach according to the DCSV-based polytope. As a result, one lemma about DCSV-based recursive polytope and a five-step procedure for two decoupled close-loop schemes with different time scales compose the LPV-MPC proposed method. Numerical validation shows that even for relevant flexibility situations, trajectory tracking performance is improved by tuning finite horizons and optimization problem constraints regarding DCSV's behavior