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

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

Ph.DDOCTOR OF PHILOSOPH

Support polygon in the hybrid legged-wheeled CENTAURO robot: modelling and control

Search for the robot capable to perform well in the real-world has sparked an interest in the hybrid locomotion systems. The hybrid legged-wheeled robots combine the advantages of the standard legged and wheeled platforms by switching between the quick and efficient wheeled motion on the flat grounds and the more versatile legged mobility on the unstructured terrains. With the locomotion flexibility offered by the hybrid mobility and appropriate control tools, these systems have high potential to excel in practical applications adapting effectively to real-world during locomanipuation operations. In contrary to their standard well-studied counterparts, kinematics of this newer type of robotic platforms has not been fully understood yet. This gap may lead to unexpected results when the standard locomotion methods are applied to hybrid legged-wheeled robots. To better understand mobility of the hybrid legged-wheeled robots, the model that describes the support polygon of a general hybrid legged-wheeled robot as a function of the wheel angular velocities without assumptions on the robot kinematics or wheel camber angle is proposed and analysed in this thesis. Based on the analysis of the developed support polygon model, a robust omnidirectional driving scheme has been designed. A continuous wheel motion is resolved through the Inverse Kinematics (IK) scheme, which generates robot motion compliant with the Non-Sliding Pure-Rolling (NSPR) condition. A higher-level scheme resolving a steering motion to comply with the non-holonomic constraint and to tackle the structural singularity is proposed. To improve the robot performance in presence to the unpredicted circumstances, the IK scheme has been enhanced with the introduction of a new reactive support polygon adaptation task. To this end, a novel quadratic programming task has been designed to push the system Support Polygon Vertices (SPVs) away from the robot Centre of Mass (CoM), while respecting the leg workspace limits. The proposed task has been expressed through the developed SPV model to account for the hardware limits. The omnidirectional driving and reactive control schemes have been verified in the simulation and hardware experiments. To that end, the simulator for the CENTAURO robot that models the actuation dynamics and the software framework for the locomotion research have been developed

Stabilization of Mobile Manipulators

The focus of this work is to generate a method of stabilization in a system generated through the marriage of a mobile robot and a manipulator. While the stability of a rigid manipulator is a solved problem, upon the introduction of flexibilities into the manipulator base structure there is the simultaneous introduction of an unmodeled, induced, oscillatory disturbance to the manipulator system from the mobile base suspension and mounting. Under normal circumstances, the disturbance can be modeled through experimentation and then a form of vibration suppression control can be employed to damp the induced oscillations in the base. This approach is satisfactory for disturbances that are measured, however the hardware necessary to measure the induced oscillations in the manipulator base is generally not included in mobile manipulation systems. Because of this lack of sensing hardware it becomes difficult to directly compensate for the induced disturbances in the system. Rather than developing a direct method for compensation, efforts are made to find postures of the manipulator where the flexibilities of the system are passive. In these postures the manipulator behaves as if it is on a rigid base, this allows the use of higher feedback gains and simpler control architectures.Ph.D

Vision-based methods for state estimation and control of robotic systems with application to mobile and surgical robots

For autonomous systems that need to perceive the surrounding environment for the accomplishment of a given task, vision is a highly informative exteroceptive sensory source. When gathering information from the available sensors, in fact, the richness of visual data allows to provide a complete description of the environment, collecting geometrical and semantic information (e.g., object pose, distances, shapes, colors, lights). The huge amount of collected data allows to consider both methods exploiting the totality of the data (dense approaches), or a reduced set obtained from feature extraction procedures (sparse approaches). This manuscript presents dense and sparse vision-based methods for control and sensing of robotic systems. First, a safe navigation scheme for mobile robots, moving in unknown environments populated by obstacles, is presented. For this task, dense visual information is used to perceive the environment (i.e., detect ground plane and obstacles) and, in combination with other sensory sources, provide an estimation of the robot motion with a linear observer. On the other hand, sparse visual data are extrapolated in terms of geometric primitives, in order to implement a visual servoing control scheme satisfying proper navigation behaviours. This controller relies on visual estimated information and is designed in order to guarantee safety during navigation. In addition, redundant structures are taken into account to re-arrange the internal configuration of the robot and reduce its encumbrance when the workspace is highly cluttered. Vision-based estimation methods are relevant also in other contexts. In the field of surgical robotics, having reliable data about unmeasurable quantities is of great importance and critical at the same time. In this manuscript, we present a Kalman-based observer to estimate the 3D pose of a suturing needle held by a surgical manipulator for robot-assisted suturing. The method exploits images acquired by the endoscope of the robot platform to extrapolate relevant geometrical information and get projected measurements of the tool pose. This method has also been validated with a novel simulator designed for the da Vinci robotic platform, with the purpose to ease interfacing and employment in ideal conditions for testing and validation. The Kalman-based observers mentioned above are classical passive estimators, whose system inputs used to produce the proper estimation are theoretically arbitrary. This does not provide any possibility to actively adapt input trajectories in order to optimize specific requirements on the performance of the estimation. For this purpose, active estimation paradigm is introduced and some related strategies are presented. More specifically, a novel active sensing algorithm employing visual dense information is described for a typical Structure-from-Motion (SfM) problem. The algorithm generates an optimal estimation of a scene observed by a moving camera, while minimizing the maximum uncertainty of the estimation. This approach can be applied to any robotic platforms and has been validated with a manipulator arm equipped with a monocular camera

Rapid Orbital Motion Emulator (ROME): Kinematics Modeling and Control

Space missions design requires already tested and trusted control algorithms for spacecraft motion. Rapidly testing control algorithms at a low cost is essential. A novel robotic system that emulates orbital motion in a laboratory environment is presented. The system is composed of a six degree of freedom robotic manipulator fixed on top of an omnidirectional ground vehicle accompanied with onboard computer and sensors. The integrated mobile manipulator is used as a testbed to emulate and realize orbital motion and control algorithms. The kinematic relations of the ground vehicle, robotic manipulator and the coupled kinematics are derived. The system is used to emulate an orbit trajectory. The system is scalable and capable of emulating servicing missions, satellite rendezvous and chaser follower problems

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