Control of Outdoor Robots at Higher Speeds on Challenging Terrain

This thesis studies the motion control of wheeled mobile robots. Its focus is set on high speed control on challenging terrain. Additionally, it deals with the general problem of path following, as well as path planning and obstacle avoidance in difficult conditions. First, it proposes a heuristic longitudinal control for any wheeled mobile robot, and evaluates it on different kinematic configurations and in different conditions, including laboratory experiments and participation in a robotic competition. Being the focus of the thesis, high speed control on uneven terrain is thoroughly studied, and a novel control law is proposed, based on a new model representation of skid-steered vehicles, and comprising of nonlinear lateral and longitudinal control. The lateral control part is based on the Lyapunov theory, and the convergence of the vehicle to the geometric reference path is proven. The longitudinal control is designed for high speeds, taking actuator saturation and the vehicle properties into account. The complete solution is experimentally tested on two different vehicles on several different terrain types, reaching the speeds of ca. 6 m/s, and compared against two state-of-the-art algorithms. Furthermore, a novel path planning and obstacle avoidance system is proposed, together with an extension of the proposed high speed control, which builds up a navigation system capable of autonomous outdoor person following. This system is experimentally compared against two classical obstacle avoidance methods, and evaluated by following a human jogger in outdoor environments, with both static and dynamic obstacles. All the proposed methods, together with various different state-of-the-art control approaches, are unified into one framework. The proposed framework can be used to control any wheeled mobile robot, both indoors and outdoors, at low or high speeds, avoiding all the obstacles on the way. The entire work is released as open-source software

Design of an Autonomous Agriculture Robot for Real Time Weed Detection using CNN

Agriculture has always remained an integral part of the world. As the human population keeps on rising, the demand for food also increases, and so is the dependency on the agriculture industry. But in today's scenario, because of low yield, less rainfall, etc., a dearth of manpower is created in this agricultural sector, and people are moving to live in the cities, and villages are becoming more and more urbanized. On the other hand, the field of robotics has seen tremendous development in the past few years. The concepts like Deep Learning (DL), Artificial Intelligence (AI), and Machine Learning (ML) are being incorporated with robotics to create autonomous systems for various sectors like automotive, agriculture, assembly line management, etc. Deploying such autonomous systems in the agricultural sector help in many aspects like reducing manpower, better yield, and nutritional quality of crops. So, in this paper, the system design of an autonomous agricultural robot which primarily focuses on weed detection is described. A modified deep-learning model for the purpose of weed detection is also proposed. The primary objective of this robot is the detection of weed on a real-time basis without any human involvement, but it can also be extended to design robots in various other applications involved in farming like weed removal, plowing, harvesting, etc., in turn making the farming industry more efficient. Source code and other details can be found at https://github.com/Dhruv2012/Autonomous-Farm-RobotComment: Published at the AVES 2021 conference. Source code and other details can be found at https://github.com/Dhruv2012/Autonomous-Farm-Robo

High and low level control for an Unmanned ground vehicle.

Esta Investigación presenta el desarrollo de una metodología de control de alto y bajo nivel para robot móvil o vehículo terrestre no tripulados que opera en un entorno definido, la aplicación de métodos de control automático lineal y no lineal, junto con algoritmos de búsqueda y planificación, proporcionan la plataforma de autonomía

MODEL PREDICTIVE CONTROL OF SKID-STEERED MOBILE ROBOT WITH DEEP LEARNING SYSTEM DYNAMICS

This thesis project presents several model predictive control (MPC) strategies for control of skid-steered mobile robots (SSMRs) using two different combinations of software environment, optimization tool and machine learning framework. The control strategies are tested in WeBots simulator. Spatial-based path following MPC of SSMR with static obstacle avoidance is developed in MATLAB environment with ACADO optimization toolkit using spatial kinematic model of SSMR. It includes static obstacle and border avoidance strategy based on artificial potential fields. Simulations show that the controller is effective at driving SSMR on a track, while avoiding borders and obstacles. Several more MPCs are developed using Python environment, ACADOS optimisation framework, and Pytorch-Casadi integration framework. Two time-domain controllers are made in Python environment, one based on SSMR kinematic model and another based on data-driven state-space model using Pytorch- Casadi framework. Both are setup to reach a goal point in simulation experiment. Experiments show that both versions reliably reach a target point. Standard and data-driven versions of spatial path following MPC are developed. Standard is a reimplementation of MPC designed in MATLAB with modifications to cost function and border avoidance, without static obstacle avoidance. Data-driven path following MPC is an extension of standard variant with state-space model replaced with a hybrid of spatial kinematics and data-driven model. Simulation of both spatial controllers confirm their effectiveness in following reference path

Lunar Rover Motion Planning and Commands

Space exploration is moving forward and one of the topics currently being researched is mining. The objective of this thesis is to design and develop software for the auton- omous navigation of a wheeled rover that is being built for NASA’s Lunabotics Mining Competition. The motion control system is a crucial component of a planetary rover system and its implementation heavily depends on the chassis configuration. The configuration of the rover enables us to use three steering modes: Ackermann, Point- turn and Crab steering. The implementation takes advantages of all the modes and involves algorithms for path planning, path smoothing and path following. In addi- tion, the system offers a feature of automatic steering mode selection. The system can be tuned and controlled by the cross-platform application specifically developed for this purpose. The performance of the implemented system is analyzed by testing in a simulator with a realistic physics engine and 3D visualization capabilities. Our con- ducted tests confirm that the system is sufficient in the framework of the Lunabotics Mining Competition

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Feedforward model with cascading proportional derivative active force control for an articulated arm mobile manipulator

This thesis presents an approach for controlling a mobile manipulator (MM) using a two degree of freedom (DOF) controller which essentially comprises a cascading proportional-derivative (CPD) control and feedforward active force control (FAFC). MM possesses both features of mobile platform and industrial arm manipulator. This has greatly improved the performance of MM with increased workspace capacity and better operation dexterity. The added mobility advantage to a MM, however, has increased the complexity of the MM dynamic system. A robust controller that can deal with the added complexity of the MM dynamic system was therefore needed. The AFC which can be considered as one of the novelties in the research creates a torque feedback within the dynamic system to allow for the compensation of sudden disturbances in the dynamic system. AFC also allows faster computational performance by using a fixed value of the estimated inertia matrix (IN) of the system. A feedforward of the dynamic system was also implemented to complement the IN for a better trajectory tracking performance. A localisation technique using Kalman filter (KF) was also incorporated into the CPD-FAFC scheme to solve some MM navigation problems. A simulation and experimental studies were performed to validate the effectiveness of the MM controller. Simulation was performed using a co-simulation technique which combined the simultaneous execution of the MSC Adams and MATLAB/Simulink software. The experimental study was carried out using a custom built MM experimental rig (MMer) which was developed based on the mechatronic approach. A comparative studies between the proposed CPD-FAFC with other type of controllers was also performed to further strengthen the outcome of the system. The experimental results affirmed the effectiveness of the proposed AFC-based controller and were in good agreement with the simulation counterpart, thereby verifying and validating the proposed research concepts and models

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

Ph.DDOCTOR OF PHILOSOPH

Modeling and Control of the UGV Argo J5 with a Custom-Built Landing Platform

This thesis aims to develop a detailed dynamic model and implement several navigation controllers for path tracking and dynamic self-leveling of the Argo J5 Unmanned Ground Vehicle (UGV) with a custom-built landing platform. The overall model is derived by combining the Argo J5 driveline system with the wheelsterrain interaction (using terramechanics theory and mobile robot kinetics), while the landing platform model follows the Euler-Lagrange formulation. Different controllers are, then, derived, implemented to demonstrate: i.) self-leveling accuracy of the landing platform, ii.) trajectory tracking capabilities of the Argo J5 when moving in uneven terrains. The novelty of the Argo J5 model is the addition of a vertical load on each wheel through derivation of the shear stress depending on the point’s position in 3D space on each wheel. Static leveling of the landing platform within one degree of the horizon is evaluated by implementing Proportional Derivative (PD), Proportional Integral Derivative (PID), Linear Quadratic Regulator (LQR), feedback linearization, and Passivity Based Adaptive Controller (PBAC) techniques. A PD controller is used to evaluate the performance of the Argo J5 on different terrains. Further, for the Argo J5 - landing platform ensemble, PBAC and Neural Network Based Adaptive Controller (NNBAC) are derived and implemented to demonstrate dynamic self-leveling. The emphasis is on different controller implementation for complex real systems such as Argo J5 - Landing platform. Results, obtained via extensive simulation studies in a Matlab/Simulink environment that consider real system parameters and hardware limitations, contribute to understanding navigation performance in a variety of terrains with unknown properties and illustrate the Argo J5 velocity, wheel rolling resistance, wheel turning resistance and shear stress on different terrains