MULTI-ROBOT COVERAGE WITH DYNAMIC COVERAGE INFORMATION COMPRESSION

This work considers the problem of coverage of an initially unknown environment by a set of autonomous robots. A crucial aspect in multi-robot coverage involves robots sharing information about the regions they have already covered at certain intervals, so that multiple robots can avoid repeated coverage of the same area. However, sharing the coverage information between robots imposes considerable communication and computation overhead on each robot, which increases the robots’ battery usage and overall coverage time. To address this problem, we explore a novel coverage technique where robots use an information compression algorithm before sharing their coverage maps with each other. Specifically, we use a polygonal approximation algorithm to represent any arbitrary region covered by a robot as a polygon with a fixed, small number of vertices. At certain intervals, each robot then sends this small set of vertices to other robots in its communication range as its covered area, and each receiving robot records this information in a local map of covered regions so that it can avoid repeat coverage. The coverage information in the map is then utilized by a technique called spanning tree coverage (STC) by each robot to perform area coverage. We have verified the performance of our algorithm on simulated Coroware Corobot robots within the Webots robot simulator with different sizes of environments and different types of obstacles in the environments, while modelling sensor noise from the robots’ sensors. Our results show that using the polygonal compression technique is an effective way to considerably reduce data transfer between robots in a multi-robot team without sacrificing the performance and efficiency gains that communication provides to such a system

Galaxy Zoo Supernovae

This paper presents the first results from a new citizen science project: Galaxy Zoo Supernovae. This proof of concept project uses members of the public to identify supernova candidates from the latest generation of wide-field imaging transient surveys. We describe the Galaxy Zoo Supernovae operations and scoring model, and demonstrate the effectiveness of this novel method using imaging data and transients from the Palomar Transient Factory (PTF). We examine the results collected over the period April-July 2010, during which nearly 14,000 supernova candidates from PTF were classified by more than 2,500 individuals within a few hours of data collection. We compare the transients selected by the citizen scientists to those identified by experienced PTF scanners, and find the agreement to be remarkable - Galaxy Zoo Supernovae performs comparably to the PTF scanners, and identified as transients 93% of the ~130 spectroscopically confirmed SNe that PTF located during the trial period (with no false positive identifications). Further analysis shows that only a small fraction of the lowest signal-to-noise SN detections (r > 19.5) are given low scores: Galaxy Zoo Supernovae correctly identifies all SNe with > 8{\sigma} detections in the PTF imaging data. The Galaxy Zoo Supernovae project has direct applicability to future transient searches such as the Large Synoptic Survey Telescope, by both rapidly identifying candidate transient events, and via the training and improvement of existing machine classifier algorithms.Comment: 13 pages, 10 figures, accepted MNRA

Visual Odometry in Challenging Environments: An Urban Underground Railway Scenario Case

Localization is one of the most critical tasks for an autonomous vehicle, as position information is required to understand its surroundings and move accordingly. Visual Odometry (VO) has shown promising results in the last years. However, VO algorithms are usually evaluated in outdoor street scenarios and do not consider underground railway scenarios, with low lighting conditions in tunnels and significant lighting changes between tunnels and railway platforms. Besides, there is a lack of GPS, and it is not easy to access such infrastructures. This research proposes a method to create a ground truth of images and poses in underground railway scenarios. Second, the EnlightenGAN algorithm is proposed to face challenging lighting conditions, which can be coupled with any state-of-the-art VO techniques. Finally, the obtained ground truth and the EnlightenGAN have been tested in a real scenario. Two different VO approaches have been used: ORB-SLAM2 and DF-VO. The results show that the EnlightenGAN enhancement improves the performance of both approaches

The WEAVE fibre positioner: calibration, commissioning, and first-light

This thesis details the final assembly and calibration procedures for the WEAVE fibre positioner, from October 2019 to first-light in September 2022. The positioner was successfully mounted to the William Herschel Telescope (WHT) in May 2022. I present the first-light results of WEAVE for each of its three observing modes and discuss whether the positioner has met its design expectations. Chapter one describes the evolution of astronomical spectroscopy and the scientific motivations behind the construction of WEAVE. This is followed by the instrument design specification and sub-systems required to meet them. Chapter two will focus on the positioner in detail and include the evolution of our quality control methods. An account of the calibration procedures, in both the lab environment and on the elevation testing rig, is given in chapter three. The test rig revealed flexure behaviour in the positioner's structure, which was modified to correct for this. The calibration work was repeated with improved techniques, to predict changes in the positioner's metrology with elevation. This chapter then presents the results of the fibre movement testing, including the current placement timings To maximise the scientific output from WEAVE, I applied a series of adjustments to a subset of target fields, identifying methods that will improve the survey output using the fibre assignment program, Configure, and highlight potential selection effects. Chapter five presents the first-light results and the steps taken to achieve this. It required extensive measurements of the optical distortion map across the FoV, which were carefully mapped using astrometry of the night-sky across our focal plane. I then compare these results with the observational requirements from WEAVE's conception, to define its overall success. Chapter 6 provides an overview of the work completed in this thesis and describes potential upgrades to the instrument in the future

MAGNETIC FIELD-ASSISTED MANUFACTURING OF FUNCTIONAL POLYMERIC COMPOSITES

Magnetic polymeric composites have recently found their way into creating functional products that respond to an external stimulus while accomplishing complex tasks. A common manufacturing method for these materials is magnetic field-assisted manufacturing, where an external field is used during or after creating the product to induce local or global particle patterns or magnetization profiles. As one of the processes paired with magnetic field, additive manufacturing (AM) has shown great potential in developing complex structures with local control over material properties. In this work, using magneto-responsive reinforcing fibers and different polymeric matrices, we explore the potential of this process in creating functional polymeric composites and find applications for the resulting products.First, a design, manufacturing, and characterization framework for creating functional magnetic composites is developed. The potential of pairing magnetic field with AM in creating functionally graded polymeric composites with controlled local fiber orientation and concentration is studied. Composite materials fabricated and characterized to understand the relationships between process parameters, particle distribution, and local mechanical properties. Two simple characterization frameworks are developed to correlate the particle distribution and local properties of manufactured polymeric composites, and replace more expensive and complex methods.Next, two photocurable resin systems for the magnetic field-assisted AM are proposed and verified. Due to the molecular and capillary forces in bottom-up vat photopolymerization, it is challenging to control the particle distribution during the manufacturing process. Here we use surface treatment of magnetic particles to create temporary bonding between the particles and resin systems. Using this method enables a better control over particle distribution during the manufacturing process and prevents the particle aggregation. It is also proved that treated particles in resin systems can ensure more predictable curing behavior and better mechanical properties in the final photocured polymeric composites.Finally, we explore the potential magnetic field-assisted manufacturing in introducing desired magnetization profiles into polymeric composites. In this section, magnetoelastic structures are fabricated and studied to understand their behavior under dynamic magnetic fields. Using the proposed design for the magnetoelastic composites, untethered multimodal control over soft magnetic robots is achieved

Body swarm interface (BOSI) : controlling robotic swarms using human bio-signals

Traditionally robots are controlled using devices like joysticks, keyboards, mice and other similar human computer interface (HCI) devices. Although this approach is effective and practical for some cases, it is restrictive only to healthy individuals without disabilities, and it also requires the user to master the device before its usage. It becomes complicated and non-intuitive when multiple robots need to be controlled simultaneously with these traditional devices, as in the case of Human Swarm Interfaces (HSI). This work presents a novel concept of using human bio-signals to control swarms of robots. With this concept there are two major advantages: Firstly, it gives amputees and people with certain disabilities the ability to control robotic swarms, which has previously not been possible. Secondly, it also gives the user a more intuitive interface to control swarms of robots by using gestures, thoughts, and eye movement. We measure different bio-signals from the human body including Electroencephalography (EEG), Electromyography (EMG), Electrooculography (EOG), using off the shelf products. After minimal signal processing, we then decode the intended control action using machine learning techniques like Hidden Markov Models (HMM) and K-Nearest Neighbors (K-NN). We employ formation controllers based on distance and displacement to control the shape and motion of the robotic swarm. Comparison for ground truth for thoughts and gesture classifications are done, and the resulting pipelines are evaluated with both simulations and hardware experiments with swarms of ground robots and aerial vehicles

Computational Techniques for Analysis of Genetic Network Dynamics

In this paper we propose modeling and analysis techniques for genetic networks that provide biologists with insight into the dynamics of such systems. Central to our modeling approach is the framework of hybrid systems and our analysis tools are derived from formal analysis of such systems. Given a set of states characterizing a property of biological interest P, we present the Multi-Affine Rectangular Partition (MARP) algorithm for the construction of a set of infeasible states I that will never reach P and the Rapidly Exploring Random Forest of Trees (RRFT) algorithm for the construction of a set of feasible states F that will reach P. These techniques are scalable to high dimensions and can incorporate uncertainty (partial knowledge of kinetic parameters and state uncertainty). We apply these methods to understand the genetic interactions involved in the phenomenon of luminescence production in the marine bacterium V. fischeri

Gathering of Mobile Robots in Anonymous Trees

Gathering problem of mobile robots is a class of graph problem that has a lot of relevance in everyday life. The problem requires a set of mobile robots, initially located at different nodes of a graph, to gather at the same location in the graph, which is not decided before. This report considers the gathering problem of mobile robots in anonymous trees. The robots considered here are identical, do not communicate directly with other robots and also, all the robots execute the same algorithm to achieve gathering. Robots are assumed to have minimal capabilities with respect to the memory associated with them as well as their visibility capability. In this report, three models have been proposed for solving gathering problem under three different scenarios. Possible solutions in each of these models have been described. The current work that has already happened and the future work that can be done in each model have also been mentioned