Design Concepts for a Hybrid Swimming and Walking Vehicle

AbstractThis paper describes the design and proposed control methods for a 6-legged swimming and walking robot that can be used in a variety of different transportation and equipment control applications above ground, under water and above water. Known as the TURTLE (Tele–operated Unmanned Robot for Telemetry and Legged Exploration), a prototype of this mobile robot is currently being designed and developed for experimental testing in the near future. It will be powered by rechargeable electric batteries (to be recharged by solar panels) and all of its actuators will be electric motors, each controlled and monitored by onboard microcontrollers supervised by an onboard master computer. The TURTLE will be fitted with several high-resolution digital cameras, 3D laser and sonar scanners, an IMU (Inertial Management Unit), electronic compass, GPS (satellite navigation) module, underwater sonar transceiver hardware and two or more types of long-distance wireless communications hardware. The first prototype of the TURTLE will focus on basic tasks such as remote video surveillance, 3D terrain surface scanning (above ground and underwater), basic swimming styles, basic walking styles, climbing over large rocks and walking over very rough ground and steep terrain. This paper describes the main objectives, basic performance specifications, functions and mechanical design solutions that have been developed so far for this project. It covers details of the various different swimming modes and feasible solutions for achieving the main design objectives

Microrobots for wafer scale microfactory: design fabrication integration and control.

Future assembly technologies will involve higher automation levels, in order to satisfy increased micro scale or nano scale precision requirements. Traditionally, assembly using a top-down robotic approach has been well-studied and applied to micro-electronics and MEMS industries, but less so in nanotechnology. With the bloom of nanotechnology ever since the 1990s, newly designed products with new materials, coatings and nanoparticles are gradually entering everyone’s life, while the industry has grown into a billion-dollar volume worldwide. Traditionally, nanotechnology products are assembled using bottom-up methods, such as self-assembly, rather than with top-down robotic assembly. This is due to considerations of volume handling of large quantities of components, and the high cost associated to top-down manipulation with the required precision. However, the bottom-up manufacturing methods have certain limitations, such as components need to have pre-define shapes and surface coatings, and the number of assembly components is limited to very few. For example, in the case of self-assembly of nano-cubes with origami design, post-assembly manipulation of cubes in large quantities and cost-efficiency is still challenging. In this thesis, we envision a new paradigm for nano scale assembly, realized with the help of a wafer-scale microfactory containing large numbers of MEMS microrobots. These robots will work together to enhance the throughput of the factory, while their cost will be reduced when compared to conventional nano positioners. To fulfill the microfactory vision, numerous challenges related to design, power, control and nanoscale task completion by these microrobots must be overcome. In this work, we study three types of microrobots for the microfactory: a world’s first laser-driven micrometer-size locomotor called ChevBot，a stationary millimeter-size robotic arm, called Solid Articulated Four Axes Microrobot (sAFAM), and a light-powered centimeter-size crawler microrobot called SolarPede. The ChevBot can perform autonomous navigation and positioning on a dry surface with the guidance of a laser beam. The sAFAM has been designed to perform nano positioning in four degrees of freedom, and nanoscale tasks such as indentation, and manipulation. And the SolarPede serves as a mobile workspace or transporter in the microfactory environment

Influence of complex environments on LiDAR-Based robot navigation

La navigation sécuritaire et efficace des robots mobiles repose grandement sur l’utilisation des capteurs embarqués. L’un des capteurs qui est de plus en plus utilisé pour cette tâche est le Light Detection And Ranging (LiDAR). Bien que les recherches récentes montrent une amélioration des performances de navigation basée sur les LiDARs, faire face à des environnements non structurés complexes ou des conditions météorologiques difficiles reste problématique. Dans ce mémoire, nous présentons une analyse de l’influence de telles conditions sur la navigation basée sur les LiDARs. Notre première contribution est d’évaluer comment les LiDARs sont affectés par les flocons de neige durant les tempêtes de neige. Pour ce faire, nous créons un nouvel ensemble de données en faisant l’acquisition de données durant six précipitations de neige. Une analyse statistique de ces ensembles de données, nous caractérisons la sensibilité de chaque capteur et montrons que les mesures de capteurs peuvent être modélisées de manière probabilistique. Nous montrons aussi que les précipitations de neige ont peu d’influence au-delà de 10 m. Notre seconde contribution est d’évaluer l’impact de structures tridimensionnelles complexes présentes en forêt sur les performances d’un algorithme de reconnaissance d’endroits. Nous avons acquis des données dans un environnement extérieur structuré et en forêt, ce qui permet d’évaluer l’influence de ces derniers sur les performances de reconnaissance d’endroits. Notre hypothèse est que, plus deux balayages laser sont proches l’un de l’autre, plus la croyance que ceux-ci proviennent du même endroit sera élevée, mais modulé par le niveau de complexité de l’environnement. Nos expériences confirment que la forêt, avec ses réseaux de branches compliqués et son feuillage, produit plus de données aberrantes et induit une chute plus rapide des performances de reconnaissance en fonction de la distance. Notre conclusion finale est que, les environnements complexes étudiés influencent négativement les performances de navigation basée sur les LiDARs, ce qui devrait être considéré pour développer des algorithmes de navigation robustes.To ensure safe and efficient navigation, mobile robots heavily rely on their ability to use on-board sensors. One such sensor, increasingly used for robot navigation, is the Light Detection And Ranging (LiDAR). Although recent research showed improvement in LiDAR-based navigation, dealing with complex unstructured environments or difficult weather conditions remains problematic. In this thesis, we present an analysis of the influence of such challenging conditions on LiDAR-based navigation. Our first contribution is to evaluate how LiDARs are affected by snowflakes during snowstorms. To this end, we create a novel dataset by acquiring data during six snowfalls using four sensors simultaneously. Based on statistical analysis of this dataset, we characterized the sensitivity of each device and showed that sensor measurements can be modelled in a probabilistic manner. We also showed that falling snow has little impact beyond a range of 10 m. Our second contribution is to evaluate the impact of complex of three-dimensional structures, present in forests, on the performance of a LiDAR-based place recognition algorithm. We acquired data in structured outdoor environment and in forest, which allowed evaluating the impact of the environment on the place recognition performance. Our hypothesis was that the closer two scans are acquired from each other, the higher the belief that the scans originate from the same place will be, but modulated by the level of complexity of the environments. Our experiments confirmed that forests, with their intricate network of branches and foliage, produce more outliers and induce recognition performance to decrease more quickly with distance when compared with structured outdoor environment. Our conclusion is that falling snow conditions and forest environments negatively impact LiDAR-based navigation performance, which should be considered to develop robust navigation algorithms

Visual Servoing For Robotic Positioning And Tracking Systems

Visual servoing is a robot control method in which camera sensors are used inside the control loop and visual feedback is introduced into the robot control loop to enhance the robot control performance in accomplishing tasks in unstructured environments. In general, visual servoing can be categorized into image-based visual servoing (IBVS), position-based visual servoing (PBVS), and hybrid approach. To improve the performance and robustness of visual servoing systems, the research on IBVS for robotic positioning and tracking systems mainly focuses on aspects of camera configuration, image features, pose estimation, and depth determination. In the first part of this research, two novel multiple camera configurations of visual servoing systems are proposed for robotic manufacturing systems for positioning large-scale workpieces. The main advantage of these two multiple camera configurations is that the depths of target objects or target features are constant or can be determined precisely by using computer vision. Hence the accuracy of the interaction matrix is guaranteed, and thus the positioning performances of visual servoing systems can be improved remarkably. The simulation results show that the proposed multiple camera configurations of visual servoing for large-scale manufacturing systems can satisfy the demand of high-precision positioning and assembly in the aerospace industry. In the second part of this research, two improved image features for planar central symmetrical-shaped objects are proposed based on image moment invariants, which can represent the pose of target objects with respect to camera frame. A visual servoing controller based on the proposed image moment features is designed and thus the control performance of the robotic tracking system is improved compared with the method based on the commonly used image moment features. Experimental results on a 6-DOF robot visual servoing system demonstrate the efficiency of the proposed method. Lastly, to address the challenge of choosing proper image features for planar objects to get maximal decoupled structure of the interaction matrix, the neural network (NN) is applied as the estimator of target object poses with respect to camera frame based on the image moment invariants. Compared with previous methods, this scheme avoids image interaction matrix singularity and image local minima in IBVS. Furthermore, the analytical form of depth computation is given by using classical geometrical primitives and image moment invariants. A visual servoing controller is designed and the tracking performance is enhanced for robotic tracking systems. Experimental results on a 6-DOF robot system are provided to illustrate the effectiveness of the proposed scheme

Affordable flexible hybrid manipulator for miniaturised product assembly

Miniaturised assembly systems are capable of assembling parts of a few millimetres in size with an accuracy of a few micrometres. Reducing the size and the cost of such a system while increasing its flexibility and accuracy is a challenging issue. The introduction of hybrid manipulation, also called coarse/fine manipulation, within an assembly system is the solution investigated in this thesis. A micro-motion stage (MMS) is designed to be used as the fine positioning mechanism of the hybrid assembly system. MMSs often integrate compliant micro-motion stages (CMMSs) to achieve higher performances than the conventional MMSs. CMMSs are mechanisms that transmit an output force and displacement through the deformation of their structure. Although widely studied, the design and modelling techniques of these mechanisms still need to be improved and simplified. Firstly, the linear modelling of CMMSs is evaluated and two polymer prototypes are fabricated and characterised. It is found that polymer based designs have a low fabrication cost but not suitable for construction of a micro-assembly system. A simplified nonlinear model is then derived and integrated within an analytical model, allowing for the full characterisation of the CMMS in terms of stiffness and range of motion. An aluminium CMMS is fabricated based on the optimisation results from the analytical model and is integrated within an MMS. The MMS is controlled using dual-range positioning to achieve a low-cost positioning accuracy better than 2µm within a workspace of 4.4×4.4mm2. Finally, a hybrid manipulator is designed to assemble mobile-phone cameras and sensors automatically. A conventional robot manipulator is used to pick and place the parts in coarse mode while the aluminium CMMS based MMS is used for fine alignment of the parts. A high-resolution vision system is used to locate the parts on the substrate and to measure the relative position of the manipulator above MMS using a calibration grid with square patterns. The overall placement accuracy of the assembly system is ±24µm at 3σ and can reach 2µm, for a total cost of less than £50k, thus demonstrating the suitability of hybrid manipulation for desktop-size miniaturised assembly systems. The precision of the existing system could be significantly improved by making the manipulator stiffer (i.e. preloaded bearings…) and adjustable to compensate for misalignment. Further improvement could also be made on the calibration of the vision system. The system could be either scaled up or down using the same architecture while adapting the controllers to the scale.Engineering and Physical Sciences Research Council (EPSRC

Pjezorobotų trajektorijų valdymas nanopalydovų stabilizavimui

Rapid industrial advancement requires novel ideas, new scientific approaches and effective technologies that would ensure quality and precision. Application of piezoelectric actuators in robotics opens many possibilities to create systems with extreme precision and control. A very important step in the development of autonomous robots is the formation of motion trajectories. Classical interpolation methods used for formation of the trajectories are suitable only when robots have wheels, legs or other parts for motion transmission. Piezorobots that are analyzed in this dissertation have no additional components that create motion, only contact points with the static plane. Therefore, traditional motion formation methods are not suitable and a problem arises how to define motion trajectory of such device. The aim of this work is to create a trajectory control algorithm of multi-degrees-of-freedom piezorobot used for nanosatellite stabilization. In order to achieve the objective, the following tasks had to be solved: to analyze constructions of precise piezorobots, their operating principles and motion formation methods; to analyze stabilization problems of satellites and application of multi-degrees-of-freedom piezorobots for nanosatellite stabilization; to create piezorobots’ motion formation algorithms according to electrode excitation schemes, to perform an experimental research; to determine quantitative characteristics of the constructed piezorobots and their motion trajectories. The introduction describes the importance and novelty of this thesis, goals of this work, its practical value and defended statements. The first chapter analyses the principals of ultrasonic devices, gives a thorough review of constructions of ultrasonic devices with multi-degrees-of-freedom. The second chapter provides a review of satellite stabilization principles and how multi-degrees-of-freedom piezorobots can be applied for nanosatellite stabilization. Motion formation methods for ultrasonic devices with multi-degrees-of-freedom are presented. The third chapter presents the detailed analysis of different piezorobots. In the fourth chapter experimental results are provided. Trajectory planning of piezorobot is shown, results are compared to numerical calculations performed in the third chapter. The conclusions about applicability of piezorobots’ motion formation algorithms according to electrode excitation schemes are given. Seven articles focusing on the subject of the dissertation have been published, two presentations on the subject have been presented in conferences at international level. The research for the dissertation has been funded by the Lithuanian State Science and Studies Foundation: European Regional Development Fund, Project No. DOTSUT-234 and Research Council of Lithuania, Project No. MIP-084/2015.Dissertatio

DEVELOPMENT OF A NOVEL Z-AXIS PRECISION POSITIONING STAGE WITH MILLIMETER TRAVEL RANGE BASED ON A LINEAR PIEZOELECTRIC MOTOR

Piezoelectric-based positioners are incorporated into stereotaxic devices for microsurgery, scanning tunneling microscopes for the manipulation of atomic and molecular-scale structures, nanomanipulator systems for cell microinjection and machine tools for semiconductor-based manufacturing. Although several precision positioning systems have been developed for planar motion, most are not suitable to provide long travel range with large load capacity in vertical axis because of their weights, size, design and embedded actuators. This thesis develops a novel positioner which is being developed specifically for vertical axis motion based on a piezoworm arrangement in flexure frames. An improved estimation of the stiffness for Normally Clamped (NC) clamp is presented. Analytical calculations and finite element analysis are used to optimize the design of the lifting platform as well as the piezoworm actuator to provide maximum thrust force while maintaining a compact size. To make a stage frame more compact, the actuator is integrated into the stage body. The complementary clamps and the amplified piezoelectric actuators based extenders are designed such that no power is needed to maintain a fixed vertical position, holding the payload against the force of gravity. The design is extended to a piezoworm stage prototype and validated through several tests. Experiments on the prototype stage show that it is capable of a speed of 5.4 mm/s, a force capacity of 8 N and can travel over 16 mm

A framework of using customized LIDAR to localize robot for nuclear reactor inspections

While remote inspection of industrial structures, such as nuclear reactors, using robotic crawlers currently presents significant advantages in terms of safety, accuracy and cost, other challenges emerge due to poor context-awareness and positional accuracy. This results in a lack of visibility for path planning and difficulty in precise localization of NDE (Non-Destructive Evaluation) inspection data. LIDAR (Light Detection and Ranging) are one form of sensors that estimate distances at various angles to map the surrounding environment using optical techniques. Existing commercial LIDARs offer a long range of measurement, allowing mapping of the surroundings. However, such sensors often have centimeter accuracy and a minimum scan range, resulting in a blind area and are generally unsuitable for compact spaces and areas with high density of neighboring objects. This paper presents a framework for using a customized 2D laser scanner, an IMU (Inertial Measurement Unit) and a data fusion approach for localization inside high-density volumes such as nuclear reactors. The laser scanner offers precise measurements with submillimeter accuracy for items located in the short range. The IMU calculates the robot attitude angles, which are critical for inclination angle corrections. The facilities are often made of metallic materials with highly reflective surfaces, which remains problematic for the laser scanner. A mock-up nuclear dome, of realistic material construction, was utilized to benchmark the performance of this framework. The distance and orientation error observed were below 2 mm and 1°, respectively. The framework will be further processed to produce a close-range environment mapping