Autonomous navigation of a wheeled mobile robot in farm settings

This research is mainly about autonomously navigation of an agricultural wheeled mobile robot in an unstructured outdoor setting. This project has four distinct phases defined as: (i) Navigation and control of a wheeled mobile robot for a point-to-point motion. (ii) Navigation and control of a wheeled mobile robot in following a given path (path following problem). (iii) Navigation and control of a mobile robot, keeping a constant proximity distance with the given paths or plant rows (proximity-following). (iv) Navigation of the mobile robot in rut following in farm fields. A rut is a long deep track formed by the repeated passage of wheeled vehicles in soft terrains such as mud, sand, and snow. To develop reliable navigation approaches to fulfill each part of this project, three main steps are accomplished: literature review, modeling and computer simulation of wheeled mobile robots, and actual experimental tests in outdoor settings. First, point-to-point motion planning of a mobile robot is studied; a fuzzy-logic based (FLB) approach is proposed for real-time autonomous path planning of the robot in unstructured environment. Simulation and experimental evaluations shows that FLB approach is able to cope with different dynamic and unforeseen situations by tuning a safety margin. Comparison of FLB results with vector field histogram (VFH) and preference-based fuzzy (PBF) approaches, reveals that FLB approach produces shorter and smoother paths toward the goal in almost all of the test cases examined. Then, a novel human-inspired method (HIM) is introduced. HIM is inspired by human behavior in navigation from one point to a specified goal point. A human-like reasoning ability about the situations to reach a predefined goal point while avoiding any static, moving and unforeseen obstacles are given to the robot by HIM. Comparison of HIM results with FLB suggests that HIM is more efficient and effective than FLB. Afterward, navigation strategies are built up for path following, rut following, and proximity-following control of a wheeled mobile robot in outdoor (farm) settings and off-road terrains. The proposed system is composed of different modules which are: sensor data analysis, obstacle detection, obstacle avoidance, goal seeking, and path tracking. The capabilities of the proposed navigation strategies are evaluated in variety of field experiments; the results show that the proposed approach is able to detect and follow rows of bushes robustly. This action is used for spraying plant rows in farm field. Finally, obstacle detection and obstacle avoidance modules are developed in navigation system. These modules enables the robot to detect holes or ground depressions (negative obstacles), that are inherent parts of farm settings, and also over ground level obstacles (positive obstacles) in real-time at a safe distance from the robot. Experimental tests are carried out on two mobile robots (PowerBot and Grizzly) in outdoor and real farm fields. Grizzly utilizes a 3D-laser range-finder to detect objects and perceive the environment, and a RTK-DGPS unit for localization. PowerBot uses sonar sensors and a laser range-finder for obstacle detection. The experiments demonstrate the capability of the proposed technique in successfully detecting and avoiding different types of obstacles both positive and negative in variety of scenarios

Characterizing Energy Usage of a Commercially Available Ground Robot: Method and Results

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106934/1/rob21507.pd

Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems

In this paper, we consider the closed-loop control problem of nonlinear robotic systems in the presence of probabilistic uncertainties and disturbances. More precisely, we design a state feedback controller that minimizes deviations of the states of the system from the nominal state trajectories due to uncertainties and disturbances. Existing approaches to address the control problem of probabilistic systems are limited to particular classes of uncertainties and systems such as Gaussian uncertainties and processes and linearized systems. We present an approach that deals with nonlinear dynamics models and arbitrary known probabilistic uncertainties. We formulate the controller design problem as an optimization problem in terms of statistics of the probability distributions including moments and characteristic functions. In particular, in the provided optimization problem, we use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems. In order to reduce the tracking deviations, we minimize the uncertainty of the probabilistic states around the nominal trajectory by minimizing the trace and the determinant of the covariance matrix of the probabilistic states. To obtain the state feedback gains, we solve deterministic optimization problems in terms of moments, characteristic functions, and state feedback gains using off-the-shelf interior-point optimization solvers. To illustrate the performance of the proposed method, we compare our method with existing probabilistic control methods.Comment: International Conference on Intelligent Robots and Systems (IROS), 202

3LP: a linear 3D-walking model including torso and swing dynamics

In this paper, we present a new model of biped locomotion which is composed of three linear pendulums (one per leg and one for the whole upper body) to describe stance, swing and torso dynamics. In addition to double support, this model has different actuation possibilities in the swing hip and stance ankle which could be widely used to produce different walking gaits. Without the need for numerical time-integration, closed-form solutions help finding periodic gaits which could be simply scaled in certain dimensions to modulate the motion online. Thanks to linearity properties, the proposed model can provide a computationally fast platform for model predictive controllers to predict the future and consider meaningful inequality constraints to ensure feasibility of the motion. Such property is coming from describing dynamics with joint torques directly and therefore, reflecting hardware limitations more precisely, even in the very abstract high level template space. The proposed model produces human-like torque and ground reaction force profiles and thus, compared to point-mass models, it is more promising for precise control of humanoid robots. Despite being linear and lacking many other features of human walking like CoM excursion, knee flexion and ground clearance, we show that the proposed model can predict one of the main optimality trends in human walking, i.e. nonlinear speed-frequency relationship. In this paper, we mainly focus on describing the model and its capabilities, comparing it with human data and calculating optimal human gait variables. Setting up control problems and advanced biomechanical analysis still remain for future works.Comment: Journal paper under revie

Real-time vehicle measurements using digital image correlation

The tyre-road interface is one of the most important research topics in the field of vehicle dynamics. This is largely due to all the vehicle excitation forces (besides aerodynamic forces) being generated at this interface. There are many parameters which govern the generation of tyre forces, of which the side-slip angle is of utmost importance. Vehicle side-slip angle can be used as a measure of vehicle stability. Stability control schemes require side-slip angle and typically estimate this parameter instead of using a direct measurement. The relationship between tyre lateral force and tyre side-slip allows the lateral force generated by the tyre to be determined from the tyre side-slip angle. Therefore, real-time measurement of side-slip angle is important in tyre research and vehicle stability. Solutions exist to measure the side-slip angle, however, do not perform well at low speeds or over rough terrain and are prohibitively expensive. In terramechanics, tyre soil deformation in the form of rut depth is a widely researched topic as it can be used as a measure of the vehicle’s ability to traverse the terrain, estimate soil characteristics and for vehicle environmental impact studies. Currently, these measurements are labour intensive and are typically conducted by hand. Other solutions exist however they are developed for road use and are prohibitively expensive. The research field would, therefore, benefit largely from online rut depth measurements. Digital Image Correlation is the mathematical process of tracking changes in digital images. The development of robust algorithms and ease of implementation has allowed many fields to be adapt this non-contact based, optical technique for application-specific measurements. Previous studies (Botha, 2015) have proved DIC to be a viable candidate for measuring the side-slip angle and rut depth that overcome current measuring hurdles. However, the analysis was conducted in post-processing from pre-recorded footage due to the large computational expense of the image processing. This opens the opportunity to adapt and optimise these techniques to achieve real-time processing speeds required for these camera-based sensors. This study builds on Botha (2015) with a real-time implementation which allows for online measurements to be made using inexpensive, off-the-shelf cameras with dedicated software. This will eventually provide systems such as ABS, stability control schemes and semi-active suspension with real time vehicle side-slip angle and rut depth with a cost-effective camera-based sensor. The aim of the present study is to develop and test two systems that can measure the side-slip angle and rut depth in real-time. The side-slip angle is measured using a single camera pointing down on the terrain and digital image correlation. It is shown to measure accurately and in real-time. The sensor is tested on a flat surface using a rig that allows for validation. The rut depth is measured using multiple cameras pointing at the terrain and digital image correlation to create a 3D map of the terrain. Three methods for determining the rut depth from the 3D map is investigated, with varying degree of accuracy and processing speed.Dissertation (MEng)--University of Pretoria, 2017.Mechanical and Aeronautical EngineeringMEngUnrestricte

Controlled density transport using Perron Frobenius generators

We consider the problem of the transport of a density of states from an initial state distribution to a desired final state distribution through a dynamical system with actuation. In particular, we consider the case where the control signal is a function of time, but not space; that is, the same actuation is applied at every point in the state space. This is motivated by several problems in fluid mechanics, such as mixing and manipulation of a collection of particles by a global control input such as a uniform magnetic field, as well as by more general control problems where a density function describes an uncertainty distribution or a distribution of agents in a multi-agent system. We formulate this problem using the generators of the Perron-Frobenius operator associated with the drift and control vector fields of the system. By considering finite-dimensional approximations of these operators, the density transport problem can be expressed as a control problem for a bilinear system in a high-dimensional, lifted state. With this system, we frame the density control problem as a problem of driving moments of the density function to the moments of a desired density function, where the moments of the density can be expressed as an output which is linear in the lifted state. This output tracking problem for the lifted bilinear system is then solved using differential dynamic programming, an iterative trajectory optimization scheme.Comment: 8 pages, 9 figures, accepted to CDC 202

Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

Innovations in Road, Railway and Airfield Bearing Capacity – Volume 2 comprises the second part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field