Static and expanding grid coverage with ant robots: Complexity results

AbstractIn this paper we study the strengths and limitations of collaborative teams of simple agents. In particular, we discuss the efficient use of “ant robots” for covering a connected region on the Z2 grid, whose area is unknown in advance, and which expands at a given rate, where n is the initial size of the connected region. We show that regardless of the algorithm used, and the robots’ hardware and software specifications, the minimal number of robots required in order for such a coverage to be possible is Ω(n). In addition, we show that when the region expands at a sufficiently slow rate, a team of Θ(n) robots could cover it in at most O(n2lnn) time. This completion time can even be achieved by myopic robots, with no ability to directly communicate with each other, and where each robot is equipped with a memory of size O(1) bits w.r.t. the size of the region (therefore, the robots cannot maintain maps of the terrain, nor plan complete paths). Regarding the coverage of non-expanding regions in the grid, we improve the current best known result of O(n2) by demonstrating an algorithm that guarantees such a coverage with completion time of O(1kn1.5+n) in the worst case, and faster for shapes of perimeter length which is shorter than O(n)

Internet of Robotic Things Intelligent Connectivity and Platforms

The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and “things” have evolved significantly. “Things” now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of “intelligent things” (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.publishedVersio

Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms

Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

JTEC Panel report on electronic manufacturing and packaging in Japan

This report summarizes the status of electronic manufacturing and packaging technology in Japan in comparison to that in the United States, and its impact on competition in electronic manufacturing in general. In addition to electronic manufacturing technologies, the report covers technology and manufacturing infrastructure, electronics manufacturing and assembly, quality assurance and reliability in the Japanese electronics industry, and successful product realization strategies. The panel found that Japan leads the United States in almost every electronics packaging technology. Japan clearly has achieved a strategic advantage in electronics production and process technologies. Panel members believe that Japanese competitors could be leading U.S. firms by as much as a decade in some electronics process technologies

The shape – morphing performance of magnetoactive soft materials

Magnetoactive soft materials (MSMs) are soft polymeric composites filled with magnetic particles that are an emerging class of smart and multifunctional materials with immense potentials to be used in various applications including but not limited to artificial muscles, soft robotics, controlled drug delivery, minimally invasive surgery, and metamaterials. Advantages of MSMs include remote contactless actuation with multiple actuation modes, high actuation strain and strain rate, self-sensing, and fast response etc. Having broad functional behaviours offered by the magnetic fillers embedded within non-magnetic matrices, MSMs are undoubtedly one of the most promising materials in applications where shape-morphing, dynamic locomotion, and reconfigurable structures are highly required. This review article provides a comprehensive picture of the MSMs focusing on the materials, manufacturing processes, programming and actuation techniques, behaviours, experimental characterisations, and device-related achievements with the current state-of-the-art and discusses future perspectives. Overall, this article not only provides a comprehensive overview of MSMs’ research and development but also functions as a systematic guideline towards the development of multifunctional, shape-morphing, and sophisticated magnetoactive devices

Using the RISCI Genetic Screening Platform for Elucidating Apoptosis Signalling Network

Considerable development in the field of nanotechnology is increasingly yielding novel applications of nanoparticles. The unique properties of nanoparticles in particular their high aspect ratio (length : width ratio), however could pose potential risks to the user. A high throughput genetic screening platform, RISCI (robotic single cDNA investigation), was previously established for the systematic evaluation of single gene activities. Here, RISCI was utilised to identify pro-apoptotic genes as well as genes involved in the positive and negative regulation of silica nanoparticle-induced cell death. This project describes the further development of the screening platform by harnessing its capability to screen a cDNA library comprising approximately 30,000 full length, completely annotated, and sequenced human genes for novel regulators of apoptosis. It integrates an extensive skill sets and is broadly organised into three major phases: Setup, Screen and Analysis. The integration of a pro-apoptosis treatment to screen for inhibitors and sensitizers is a novel aspect of the current experimental setup, along with the low redundancy library. The extensive setup phase focused on technical aspects. The cDNA library, acquired as plasmid DNA, was transformed into a bacterial host for replication and subsequent DNA isolation. A new high-throughput process was developed encompassing the production of competent bacteria and a heat shock transformation protocol, which was subsequently transferred onto the robotic platform. In parallel, the software controlling the robots was redeveloped to allow for execution of user-defined protocols while novel transfection protocols were adapted for automation. The screen identified 699 apoptosis inducers, 1,141 inhibitors and 626 sensitizers. Bioinformatics analysis revealed that the inducers were highly enriched for cell death associated terms, while the inhibitors were strongly associated with cancer profiles. Both inducers and sensitizers were predominantly achieving the functional effect on the protein level, but inhibitors were mainly transcription based. Enriched metal response genes also suggest that the silica nanoparticles were causing their toxicity through reactive oxygen species generation. Intriguingly, the screen identified many noncoding sequences as being functionally capable of regulating apoptosis. These noncoding candidates are capable of regulating the protein coding counterparts identified from the screen. The truly interesting part of the project outcome remains those unknown candidates that were implicated in apoptosis regulation for the first time. Dissemination of the consolidated candidate list would help accelerate the experimental validation of these candidates and aid other researchers in deriving novel hypotheses when the candidates are placed in their research context. [For supplementary files please contact author]

Multiscale computation and dynamic attention in biological and artificial intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence

The Future Posponed: Why Declinining Investment in Basic Research Threatens a U.S. Innovation Deficit

This report provides a number of tangible examples of under-exploited areas of science and likely consequences in the form of an innovation deficit, including: opportunities with high potential for big payoffs in health, energy, and high-tech industries;fields where we risk falling behind in critical strategic capabilities such as supercomputing, secure information systems, and national defense technologies;areas where national prestige is at stake, such as space exploration, or where a lack of specialized U.S research facilities is driving key scientific talent to work overseas.This introduction also cites examples of the benefits from basic research that have helped to shape and maintain U.S. economic power, as well as highlighting industry trends that have made university basic research even more critical to future national economic competitiveness

Evolutionary, developmental neural networks for robust robotic control

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 136-143).The use of artificial evolution to synthesize controllers for physical robots is still in its infancy. Most applications are on very simple robots in artificial environments, and even these examples struggle to span the "reality gap," a name given to the difference between the performance of a simulated robot and the performance of a.real robot using the same evolved controller. This dissertation describes three methods for improving the use of artificial evolution as a tool for generating controllers for physical robots. First, the evolutionary process must incorporate testing on the physical robot. Second, repeated structure on the robot should be exploited. Finally, prior knowledge about the robot and task should be meaningfully incorporated. The impact of these three methods, both in simulation and on physical robots, is demonstrated, quantified, and compared to hand-designed controllers.by Bryan Adams.Ph.D