1,351 research outputs found
Low-Cost Terrestrial Demonstration of Autonomous Satellite Proximity Operations
The lack of satellite servicing capabilities significantly impacts the development and operation of current orbital assets. With autonomous solutions under consideration for servicing, the purpose of this research is to build and validate a low-cost hardware platform to expedite the development of autonomous satellite proximity operations. This research aims to bridge the gap between simulation and existing higher fidelity hardware testing with an affordable alternative. An omnidirectional variant of the commercially available TurtleBot3 mobile robot is presented as a 3-DOF testbed that demonstrates a satellite servicing inspection scenario. Reference trajectories for the scenario are generated via optimal control using the commercial solver GPOPS-11, and results from simulation and hardware demonstration are presented. Recommendations are then given for using the platform as a rapid method for experimentally verifying various satellite control algorithms
Enhanced vision-based localization and control for navigation of non-holonomic omnidirectional mobile robots in GPS-denied environments
New Zealand’s economy relies on primary production to a great extent, where use of the technological
advances can have a significant impact on the productivity. Robotics and automation
can play a key role in increasing productivity in primary sector, leading to a boost in national
economy. This thesis investigates novel methodologies for design, control, and navigation
of a mobile robotic platform, aimed for field service applications, specifically in agricultural
environments such as orchards to automate the agricultural tasks.
The design process of this robotic platform as a non-holonomic omnidirectional mobile
robot, includes an innovative integrated application of CAD, CAM, CAE, and RP for development
and manufacturing of the platform. Robot Operating System (ROS) is employed for
the optimum embedded software system design and development to enable control, sensing,
and navigation of the platform.
3D modelling and simulation of the robotic system is performed through interfacing ROS
and Gazebo simulator, aiming for off-line programming, optimal control system design, and
system performance analysis. Gazebo simulator provides 3D simulation of the robotic system,
sensors, and control interfaces. It also enables simulation of the world environment, allowing
the simulated robot to operate in a modelled environment. The model based controller for kinematic
control of the non-holonomic omnidirectional platform is tested and validated through
experimental results obtained from the simulated and the physical robot.
The challenges of the kinematic model based controller including the mathematical and
kinematic singularities are discussed and the solution to enable an optimal kinematic model based controller is presented. The kinematic singularity associated with the non-holonomic
omnidirectional robots is solved using a novel fuzzy logic based approach. The proposed
approach is successfully validated and tested through the simulation and experimental results.
Development of a reliable localization system is aimed to enable navigation of the platform
in GPS-denied environments such as orchards. For this aim, stereo visual odometry (SVO) is
considered as the core of the non-GPS localization system. Challenges of SVO are introduced
and the SVO accumulative drift is considered as the main challenge to overcome. SVO drift is
identified in form of rotational and translational drift. Sensor fusion is employed to improve
the SVO rotational drift through the integration of IMU and SVO.
A novel machine learning approach is proposed to improve the SVO translational drift
using Neural-Fuzzy system and RBF neural network. The machine learning system is formulated
as a drift estimator for each image frame, then correction is applied at that frame to avoid
the accumulation of the drift over time. The experimental results and analyses are presented
to validate the effectiveness of the methodology in improving the SVO accuracy.
An enhanced SVO is aimed through combination of sensor fusion and machine learning
methods to improve the SVO rotational and translational drifts. Furthermore, to achieve a
robust non-GPS localization system for the platform, sensor fusion of the wheel odometry
and the enhanced SVO is performed to increase the accuracy of the overall system, as well as
the robustness of the non-GPS localization system. The experimental results and analyses are
conducted to support the methodology
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