A data grid strategy for non-prehensile object transport by a multi-robot system

The field of cooperative object transport within swarm and multi-robot systems (MRS) is an interesting area of research because exploring the dynamics of how multiple robots collaborate to transport objects presents fascinating opportunities to advance the capabilities of robotic teams. In this thesis we propose a control framework for non-prehensile object transport using a multi-robot system. While an object can be unmanageable for a single robot to push and transport, we demonstrate via simulations that a team of cooperative robots can be used to transport such an object. The proposed control strategy is divided into two phases: caging and cooperative transport. In the first phase, the robots start from arbitrary positions and then approach the object to be transported, forming a cage around it. The second phase consists of cooperatively transporting the object ensuring that it remains caged during transport. In the proposed strategy, the robots take a decentralized approach, wherein each robot operates autonomously while maintaining indirect communication with other robots. This is achieved by utilizing distributed data structures, such as distributed locks, sets, and maps, offered by the concept of an in-memory data grid (IMDG). By leveraging these distributed data structures, the robots can effectively share their respective states with one another. The key advantage of employing distributed data structures within our strategy is that it eliminates the need to develop a new application-specific protocol for interrobot communication. Instead, we can take advantage of the existing functionality provided by the data grid, which offers a convenient mechanism for exchanging information between robots. To our knowledge, the utilization of an IMDG in the domain of MRS is a novel concept. This thesis introduces a proposed design for a coordinated motion control strategy specifically aimed at object transport. The strategy leverages the capabilities of an IMDG, and presents it as a promising framework to simplify the communication process among the robots involved, leading to improved coordination and optimized object transport operations. We showcase the results of our research using a realistic simulator that effectively illustrates the viability of our method. This demonstration helps validate the effectiveness and potential of our methodology in various environments

Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08, 2023 : programme

In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: – Precision engineering and measurement technology Nanofabrication – Industry 4.0 and digitalisation in mechanical engineering – Mechatronics, biomechatronics and mechanism technology – Systems engineering – Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Efficient Belief Propagation for Perception and Manipulation in Clutter

Autonomous service robots are required to perform tasks in common human indoor environments. To achieve goals associated with these tasks, the robot should continually perceive, reason its environment, and plan to manipulate objects, which we term as goal-directed manipulation. Perception remains the most challenging aspect of all stages, as common indoor environments typically pose problems in recognizing objects under inherent occlusions with physical interactions among themselves. Despite recent progress in the field of robot perception, accommodating perceptual uncertainty due to partial observations remains challenging and needs to be addressed to achieve the desired autonomy. In this dissertation, we address the problem of perception under uncertainty for robot manipulation in cluttered environments using generative inference methods. Specifically, we aim to enable robots to perceive partially observable environments by maintaining an approximate probability distribution as a belief over possible scene hypotheses. This belief representation captures uncertainty resulting from inter-object occlusions and physical interactions, which are inherently present in clutterred indoor environments. The research efforts presented in this thesis are towards developing appropriate state representations and inference techniques to generate and maintain such belief over contextually plausible scene states. We focus on providing the following features to generative inference while addressing the challenges due to occlusions: 1) generating and maintaining plausible scene hypotheses, 2) reducing the inference search space that typically grows exponentially with respect to the number of objects in a scene, 3) preserving scene hypotheses over continual observations. To generate and maintain plausible scene hypotheses, we propose physics informed scene estimation methods that combine a Newtonian physics engine within a particle based generative inference framework. The proposed variants of our method with and without a Monte Carlo step showed promising results on generating and maintaining plausible hypotheses under complete occlusions. We show that estimating such scenarios would not be possible by the commonly adopted 3D registration methods without the notion of a physical context that our method provides. To scale up the context informed inference to accommodate a larger number of objects, we describe a factorization of scene state into object and object-parts to perform collaborative particle-based inference. This resulted in the Pull Message Passing for Nonparametric Belief Propagation (PMPNBP) algorithm that caters to the demands of the high-dimensional multimodal nature of cluttered scenes while being computationally tractable. We demonstrate that PMPNBP is orders of magnitude faster than the state-of-the-art Nonparametric Belief Propagation method. Additionally, we show that PMPNBP successfully estimates poses of articulated objects under various simulated occlusion scenarios. To extend our PMPNBP algorithm for tracking object states over continuous observations, we explore ways to propose and preserve hypotheses effectively over time. This resulted in an augmentation-selection method, where hypotheses are drawn from various proposals followed by the selection of a subset using PMPNBP that explained the current state of the objects. We discuss and analyze our augmentation-selection method with its counterparts in belief propagation literature. Furthermore, we develop an inference pipeline for pose estimation and tracking of articulated objects in clutter. In this pipeline, the message passing module with the augmentation-selection method is informed by segmentation heatmaps from a trained neural network. In our experiments, we show that our proposed pipeline can effectively maintain belief and track articulated objects over a sequence of observations under occlusion.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163159/1/kdesingh_1.pd

Creating resonance in emptiness with visual theatre : how the metaphorical potential of puppets, objects and images in theatre can be used to explore the constructed nature of reality and the complexity of the self

Includes the performance script of the play.Includes bibliographical references (leaves 88-93).The aim of this explication is to set my practical theatre research, and the production Dolos in particular, in a theoretical framework and performance historical context. Since the central theme of Dolos is the construction of reality and the consequent attachment to aspects of the self, my study draws on the ideas proposed by Phenomenology and Madhyamaka Buddhist philosophy.According to these two philosophical systems, the concept of reality is subjective and relative. This leads me to question the functioning of meaning-making in artistic practice. Metaphor is explored as a vehicle for the meaning-making process and for the creation of resonant experience in theatre and performance. The production style of Dolos is one that I have defined as Visual Theatre, a theatre of puppets, objects, visual and theatrical images. Visual Theatre is examined in the context of theatre as an artistic medium; it is then contextualised in terms of its 20th development through the Century; and definitions are offered of the major elements at play within Visual Theatre. A series of interviews conducted with five creator/ directors from four South African companies working in the general terrain of Visual Theatre is used to contextualise current practice in South Africa and to locate my own work. The interviews are used to establish trends of thought around the object/puppet and its relationship in theatre to constructed reality. The views of these practitioners on their own creative process as well as my observations about their practical work are used as examples throughout

Large space structures and systems in the space station era: A bibliography with indexes (supplement 03)

Bibliographies and abstracts are listed for 1221 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1991 and June 30, 1991. Topics covered include large space structures and systems, space stations, extravehicular activity, thermal environments and control, tethering, spacecraft power supplies, structural concepts and control systems, electronics, advanced materials, propulsion, policies and international cooperation, vibration and dynamic controls, robotics and remote operations, data and communication systems, electric power generation, space commercialization, orbital transfer, and human factors engineering

Large space structures and systems in the space station era: A bibliography with indexes (supplement 04)

Bibliographies and abstracts are listed for 1211 reports, articles, and other documents introduced into the NASA scientific and technical information system between 1 Jul. and 30 Dec. 1991. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems

DIGITISING AGRIFOOD Pathways and Challenges. November 2019

As climate change increasingly poses an existential risk for the Earth, scientists and policymakers turn to agriculture and food as areas for urgent and bold action, which need to return within acceptable Planet Boundaries. The links between agriculture, biodiversity and climate change have become so evident that scientists propose a Great Food Transformation towards a healthy diet by 2050 as a major way to save the planet. Achieving these milestones, however, is not easy, both based on current indicators and on the gloomy state of global dialogue in this domain. This is why digital technologies such as wireless connectivity, the Internet of Things, Arti cial Intelligence and blockchain can and should come to the rescue. This report looks at the many ways in which digital solutions can be implemented on the ground to help the agrifood chain transform itself to achieve more sustainability. Together with the solution, we identify obstacles, challenges, gaps and possible policy recommendations. Action items are addressed at the European Union both as an actor of change at home, and in global governance, and are spread across ten areas, from boosting connectivity and data governance to actions aimed at empowering small farmers and end users

Belief Space-Guided Navigation for Robots and Autonomous Vehicles

Navigating through the environment is a fundamental capability for mobile robots, which is still very challenging today. Most robotic applications these days, such as mining, disaster response, and agriculture, require the robots to move and perform tasks in a variety of environments which are stochastic and sometimes even unpredictable. A robot often cannot directly observe its current state but instead estimates a distribution over the set of possible states based on sensor measurements that are both noisy and partial. The actual robot position differs from its prediction after applying a motion command, due to actuation noise. Classic algorithms for navigation should adapt themselves to where the behavior of the environment is stochastic, and the execution of the motions has great uncertainty. To solve such challenging problems, we propose to guide the robot's navigation in the belief space. Belief space-guided navigation differs fundamentally from planning without uncertainty where the state of the robot is always assumed to be known precisely. The robot senses its environment, estimates its current state due to perception uncertainty, and decides whether a new (or priori) action is appropriate. Based on that determination, it actuates its sensors to move with motion uncertainty in the environment. This inspires us to connect robot perception and motion planning, and reason about the uncertainty to improve the quality of plan so that the robot can follow a collision-free, feasible kinodynamic, and task-optimal trajectory. In this dissertation, we explore the belief space-guided robotic navigation problems, which include belief space-based scene understanding for autonomous vehicles and introduce belief space guided robotic planning. We first investigate how belief space can facilitate scene understanding under the context of lane marking quality assessment in the application of autonomous driving. We propose a new problem by measuring the quality of roads and ensuring they are ready for autonomous driving. We focus on developing three quality metrics for lane markings (LMs), correctness metric, shape metric, and visibility metric, and algorithms to assess LM qualities to facilitate scene understanding. As another example of using belief space for better scene understanding, we utilize crowdsourced images from multiple vehicles to help verify LMs for high-definition (HD) map maintenance. An LM is consistent if belief functions from the map and the image satisfy statistical hypothesis testing. We further extend the Bayesian belief model into a sequential belief update using crowdsourced images. LMs with a higher probability of existence are kept in the HD map whereas those with a lower probability of existence are removed from the HD map. Belief space can also help us to tightly connect perception and motion planning. As an example, we develop a motion planning strategy for autonomous vehicles. Named as virtual lane boundary approach, this framework considers obstacle avoidance, trajectory smoothness (to satisfy vehicle kinodynamic constraints), trajectory continuity (to avoid sudden movements), global positioning system (GPS) following quality (to execute the global plan), and lane following or partial direction following (to meet human expectation). Consequently, vehicle motion is more human-compatible than existing approaches. As another example of how belief space can help guide robots for different tasks, we propose to use it for the probabilistic boundary coverage of unknown target fields (UTFs). We employ Gaussian processes as a local belief function to approximate a field boundary distribution in an ellipse-shaped local region. The local belief function allows us to predict UTF boundary trends and establish an adjacent ellipse for further exploration. The process is governed by a depth-first search process until UTF is approximately enclosed by connected ellipses when the boundary coverage process ends. We formally prove that our boundary coverage process guarantees the enclosure above a given coverage ratio with a preset probability threshold