Preliminary laboratory test on navigation accuracy of an autonomous robot for measuring air quality in livestock buildings

Air quality in many poultry buildings is less than desirable. However, the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult. To counter this, the development of an autonomous robot that could collect key environmental data continuously in livestock buildings was initiated. This research presents a specific part of the larger study that focused on the preliminary laboratory test for evaluating the navigation precision of the robot being developed under the different ground surface conditions and different localization algorithm according internal sensors. The construction of the robot was such that each wheel of the robot was driven by an independent DC motor with four odometers fixed on each motor. The inertial measurement unit (IMU) was rigidly fixed on the robot vehicle platform. The research focused on using the internal sensors to calculate the robot position (x, y, θ) through three different methods. The first method relied only on odometer dead reckoning (ODR), the second method was the combination of odometer and gyroscope data dead reckoning (OGDR) and the last method was based on Kalman filter data fusion algorithm (KFDF). A series of tests were completed to generate the robot’s trajectory and analyse the localisation accuracy. These tests were conducted on different types of surfaces and path profiles. The results proved that the ODR calculation of the position of the robot is inaccurate due to the cumulative errors and the large deviation of the heading angle estimate. However, improved use of the gyroscope data of the IMU sensor improved the accuracy of the robot heading angle estimate. The KFDF calculation resulted in a better heading angle estimate than the ODR or OGDR calculations. The ground type was also found to be an influencing factor of localisation errors

Unmanned Ground Vehicles for Smart Farms

Forecasts of world population increases in the coming decades demand new production processes that are more efficient, safer, and less destructive to the environment. Industries are working to fulfill this mission by developing the smart factory concept. The agriculture world should follow industry leadership and develop approaches to implement the smart farm concept. One of the most vital elements that must be configured to meet the requirements of the new smart farms is the unmanned ground vehicles (UGV). Thus, this chapter focuses on the characteristics that the UGVs must have to function efficiently in this type of future farm. Two main approaches are discussed: automating conventional vehicles and developing specifically designed mobile platforms. The latter includes both wheeled and wheel-legged robots and an analysis of their adaptability to terrain and crops

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

Quantitative Analysis of Non-Linear Probabilistic State Estimation Filters for Deployment on Dynamic Unmanned Systems

The work conducted in this thesis is a part of an EU Horizon 2020 research initiative project known as DigiArt. This part of the DigiArt project presents and explores the design, formulation and implementation of probabilistically orientated state estimation algorithms with focus towards unmanned system positioning and three-dimensional (3D) mapping. State estimation algorithms are considered an influential aspect of any dynamic system with autonomous capabilities. Possessing the ability to predictively estimate future conditions enables effective decision making and anticipating any possible changes in the environment. Initial experimental procedures utilised a wireless ultra-wide band (UWB) based communication network. This system functioned through statically situated beacon nodes used to localise a dynamically operating node. The simultaneous deployment of this UWB network, an unmanned system and a Robotic Total Station (RTS) with active and remote tracking features enabled the characterisation of the range measurement errors associated with the UWB network. These range error metrics were then integrated into an Range based Extended Kalman Filter (R-EKF) state estimation algorithm with active outlier identification to outperform the native approach used by the UWB system for two-dimensional (2D) pose estimation.The study was then expanded to focus on state estimation in 3D, where a Six Degreeof-Freedom EKF (6DOF-EKF) was designed using Light Detection and Ranging (LiDAR) as its primary observation source. A two step method was proposed which extracted information between consecutive LiDAR scans. Firstly, motion estimation concerning Cartesian states x, y and the unmanned system’s heading (ψ) was achieved through a 2D feature matching process. Secondly, the extraction and alignment of ground planes from the LiDAR scan enabled motion extraction for Cartesian position z and attitude angles roll (θ) and pitch (φ). Results showed that the ground plane alignment failed when two scans were at 10.5◦ offset. Therefore, to overcome this limitation an Error State Kalman Filter (ES-KF) was formulated and deployed as a sub-system within the 6DOF-EKF. This enabled the successful tracking of roll, pitch and the calculation of z. The 6DOF-EKF was seen to outperform the R-EKF and the native UWB approach, as it was much more stable, produced less noise in its position estimations and provided 3D pose estimation

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

Implementation of nonlinear model predictive control on all terrain mobile robot

The objective of this thesis is to control a mobile robot with nonholonomic constraints to achieve two control objectives: point stabilization and trajectory tracking. This research adopts Nonlinear Model Predictive Control (NMPC) to achieve these control objectives. The mobile robot platform used in the research is Seekur Jr., which is a skid-steering all terrain mobile robot with nonholonomic constraints. In this study NMPC is developed and tested for both indoor and outdoor navigation. To address the indoor localization issues, two methods have been adopted. In the former approach for indoor localization, a map of the environment is generated using a laser range finder. This map, along with laser range finder, is used to determine the pose (position and orientation) of the mobile robot in the environment. In the second approach, OptiTrack motion capture system has been used, which gives the position data of the mobile robot in the environment and orientation is evaluated through this. For outdoor navigation, Global Positioning System (GPS) is used to obtain the localization. The implementation of NMPC involves solving a dynamic optimization control problem, which makes the evaluation of control command time consuming. Therefore, it is difficult to implement NMPC for mobile robots in real-time applications. To address this issue, an open source toolkit solving Optimal Control Problem (OCP) has been used to implement fast NMPC routine, which provides real-time applicability of the control strategy. Obstacle avoidance feature is also added to the controller to avoid static obstacles in the trajectory of the mobile robot. The proposed control strategy is evaluated on a number of simulations and experimental studies. The results validate the real-time applicability of the proposed approach in indoor and outdoor navigation

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

Design and Real Time Control of a Versatile Scansorial Robot

This thesis presents investigations into the development of a versatile scansorial mobile robot and real-time realisation of a control system for different configurations of the robot namely climbing mode, walking mode and steering mode. The mobile robot comprises of a hybrid leg and wheel mechanism with innovative design that enables it to interchange its configuration to perform the specific tasks of pole climbing in climbing mode, walking and step climbing in walking mode, and skid steering and inclined slope climbing in steering mode. The motivation of this research is due to the surrounding environment which is not always structured for exploration or navigation missions, and thus poses significant difficulty for the robot to manoeuvre and accomplish the intended task. Hence, the development of versatile scansorial robot with a flexible and interchangeable configuration can provide a broad range of applications and locomotion system and to achieve the mission objective successfully. The robot design consists of four arms/legs with wheel attached at each end-effector and has two link manipulation capability. In climbing mode, the arms are configured as grippers to grip the pole and wheels accelerate to ascend or descend. The climbing angle is monitored to retain the level of the robot while climbing. However, in walking mode, the arms are configured as legs and the wheels are disabled. By implementing a periodic walking gait, the robot is capable of performing stable walking and step climbing. In steering mode, the arms are configured as suspension and the wheels are used for manoeuvring. In this mode, the skid steering system is used to enable the robot perform the turn. The versatile scansorial robot’s configurations and locomotion capabilities are assessed experimentally in real time implementation using the physical prototype. The experiments provided demonstrate the versatility of the robot and successfully fulfill the aims and objectives of the research