Search

Keywords: Lincoln; United Kingdom; Europe; Emerging Technologies; Food Products; Machine Learning; Nano-robot; Robot; Robotics; Technology EN Lincoln United Kingdom Europe Emerging Technologies Food Products Machine Learning Nano-robot Robot Robotics Technology 202 202 1 09/11/23 20230914 NES 230914 2023 SEP 14 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- Investigators publish new report on Robotics. Lincoln, United Kingdom, Europe, Emerging Technologies, Food Products, Machine Learning, Nano-robot, Robot, Robotics, Technology The paper also discusses the potential benefits of integrating artificial intelligence and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. [Extracted from the article]

The article discusses robot inventions that the author criticizes including a toilet paper portion cutter created by Michael Nichols, a machine by Maurice Bos which writes the time on a white board, and a machine that is mounted on runners' shoulders and feeds them tomatoes for energy.

This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis for which there are, probably, scores of papers. As clarified later, unarguably, the FTA of Markov chains, in general, and of LA, in particular, is far more complex than the asymptotic steady-state analysis. Such an FTA provides rigid bounds for the time required for the LA to attain to a given convergence accuracy. We concentrate on the FTA of the Discretized Pursuit Automaton (DPA), which is probably one of the fastest and most accurate reported LA. Although such an analysis was carried out many years ago, we record that the previous work is flawed. More specifically, in all brevity, the flaw lies in the wrongly “derived” monotonic behavior of the LA after a certain number of iterations. Rather, we claim that the property should be invoked is the submartingale property. This renders the proof to be much more involved and deep. In this paper, we rectify the flaw and reestablish the FTA based on such a submartingale phenomenon. More importantly, from the derived analysis, we are able to discover and clarify, for the first time, the underlying dilemma between the DPA’s exploitation and exploration properties. We also nontrivially confirm the existence of the optimal learning rate, which yields a better comprehension of the DPA itself. [ABSTRACT FROM AUTHOR]

This paper focuses on the novel motivated learning (ML) scheme and opportunistic behavior of an intelligent agent. It extends previously developed ML to opportunistic behavior in a multitask situation. Our paper describes the virtual world implementation of autonomous opportunistic agents learning in a dynamically changing environment, creating abstract goals, and taking advantage of arising opportunities to improve their performance. An opportunistic agent achieves better results than an agent based on ML only. It does so by minimizing the average value of all need signals rather than a dominating need. This paper applies to the design of autonomous embodied systems (robots) learning in real-time how to operate in a complex environment. [ABSTRACT FROM PUBLISHER]

This paper solves the problem of nonrigid point set registration by designing a robust transformation learning scheme. The principle is to iteratively establish point correspondences and learn the nonrigid transformation between two given sets of points. In particular, the local feature descriptors are used to search the correspondences and some unknown outliers will be inevitably introduced. To precisely learn the underlying transformation from noisy correspondences, we cast the point set registration into a semisupervised learning problem, where a set of indicator variables is adopted to help distinguish outliers in a mixture model. To exploit the intrinsic structure of a point set, we constrain the transformation with manifold regularization which plays a role of prior knowledge. Moreover, the transformation is modeled in the reproducing kernel Hilbert space, and a sparsity-induced approximation is utilized to boost efficiency. We apply the proposed method to learning motion flows between image pairs of similar scenes for visual homing, which is a specific type of mobile robot navigation. Extensive experiments on several publicly available data sets reveal the superiority of the proposed method over state-of-the-art competitors, particularly in the context of the degenerated data. [ABSTRACT FROM AUTHOR]

This paper considers a class of reinforcement-based learning (namely, perturbed learning automata) and provides a stochastic-stability analysis in repeatedly played, positive-utility, finite strategic-form games. Prior work in this class of learning dynamics primarily analyzes asymptotic convergence through stochastic approximations, where convergence can be associated with the limit points of an ordinary-differential equation (ODE). However, analyzing global convergence through an ODE-approximation requires the existence of a Lyapunov or a potential function, which naturally restricts the analysis to a fine class of games. To overcome these limitations, this paper introduces an alternative framework for analyzing asymptotic convergence that is based upon an explicit characterization of the invariant probability measure of the induced Markov chain. We further provide a methodology for computing the invariant probability measure in positive-utility games, together with an illustration in the context of coordination games. [ABSTRACT FROM AUTHOR]

Researchers from Tallinn University of Technology in Estonia have published a report on the use of autonomous mobile robots (AMR) in production logistics in the food industry. The study highlights the importance of digital solutions and automation in improving production efficiency. The researchers propose integrating AMR systems with artificial intelligence (AI) functionality to optimize transportation activities and decision-making processes. The paper presents a case study demonstrating the feasibility and relevance of this approach, which involves creating and simulating a 3D layout, monitoring key performance indicators, and using AI for proactive decision-making in production planning. [Extracted from the article]

One method for autonomously exploring unknown environments is to treat the environment as a scalar potential field. In this scenario, one or more robots acquire measurements of the field (e.g., radiation, temperature, or carbon dioxide concentrations) and use those measurements to guide their path as they explore. An important behavior robots may need is that of extremum seeking—that is, the ability to autonomously drive toward a region containing a maximum or a minimum of the field and reliably stay with this extremum even if its location is moving. In this paper, we describe an extremum seeking control law for three-dimensional scalar potential fields built upon a previous two-dimensional law. We derive an equilibrium trajectory, prove its local stability for the case of a radial scalar field, and characterize the stability as a function of parameters by numerically evaluating its Floquet multipliers. Additionally, we consider the case where the field is constant except for a small range around the source and allow the source to be diffusing with an unknown diffusion coefficient. We derive an approximate expected first passage time and use numerical simulations to show how to optimally select parameters so that the expected tracking time is maximized. [ABSTRACT FROM AUTHOR]

In this paper, we develop a distributed intermittent communication and task planning framework for mobile robot teams. The goal of the robots is to accomplish complex tasks, captured by local linear temporal logic formulas, and share the collected information with all other robots and possibly also with a user. Specifically, we consider situations where the robot communication capabilities are not sufficient to form reliable and connected networks, while the robots move to accomplish their tasks. In this case, intermittent communication protocols are necessary that allow the robots to temporarily disconnect from the network in order to accomplish their tasks free of communication constraints. We assume that the robots can only communicate with each other when they meet at common locations in space. Our distributed control framework jointly determines local plans that allow all robots to fulfill their assigned temporal tasks, sequences of communication events that guarantee information exchange infinitely often, and optimal communication locations that minimize a desired distance metric. Simulation results verify the efficacy of the proposed controllers. [ABSTRACT FROM AUTHOR]

This paper looks at the challenge of object recognition from the perspective of achieving the final goals of practical real-world autonomous robot-vision applications; that of identifying a target of interest in the robot’s field of view and properly localizing its position in preparation for the higher-level goal of tracking and navigation. A unified framework is introduced that combines a multidimensional feature histogram approach with a multiscale pyramid approach for training-less color object recognition and localization, with direct application to autonomous robotic agents. This framework addresses the high computational cost associated with multidimensional processing by deriving the Multidimensional “Laplacian Feature Histogram Pyramid”, a novel approach to a unified multidimensional-multiscale histogram representation. Furthermore, a Taylor series formulation is employed to combine the multiscale levels of the multidimensional Laplacian feature histogram pyramid into one efficient multidimensional-multiscale “Laplacian-Taylor Feature Histogram” for rapid object recognition and localization. The paper describes the criteria for target detection and localization by autonomous robots and how this newly developed framework fits these needs. Comparative results demonstrate the robustness of this recognition framework to noise and localization of target objects in cluttered scenes. [ABSTRACT FROM AUTHOR]

We study formation control problems. Our approach is to let a group of systems maximize their pairwise distances while bringing them all to a given submanifold, determining the shape of the formation. The algorithm we propose allows us to initialize the positions of the individual systems in the ambient space of the given submanifold but brings them to the desired formation asymptotically in a stable fashion. Our control inherently consists of a distributed component, maximizing the pairwise distances, and a decentralized component, asymptotically stabilizing the submanifold. We establish a graph-theoretical interpretation of the equilibria that our control enforces and extends our approach to systems living on the special Euclidean group. Throughout this paper, we illustrate our approach on different examples. [ABSTRACT FROM AUTHOR]

The article examines issues of product liability law related to robots and robotics. The application of traditional liability to manufacturers and operators of robots is discussed. The lack of established liability law for operations of autonomous robots and robotic systems is considered. Responsibilities of human operators or owners of autonomous robots are discussed. Laws enacted in U.S. states and the European Union (EU) related to the operation of robotic driverless automobiles are examined. Liability for open source software used in robotics is discussed.

In distributed autonomous robot (agents) systems, each robot (predator or prey) must behave by itself according to its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot have both learning and evolution ability to adapt to dynamic environment. This paper proposes a pursuing system utilizing the artificial life concept where autonomous mobile robots emulate social behaviors of animals and insects and realize their group behaviors. Each robot contains sensors to perceive other robots in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used for behavior decision controller. The input of the neural network is decided by the existence of other robots and the distance to the other robots. The output determines the directions in which the robot moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation. Besides, in this paper, we could have observed the robots’ emergent behaviors during simulation. [Copyright &y& Elsevier]

Deep below the Louisville, Ky., zoo lies a network of enormous caverns carved out of limestone. The caverns are dark. They're dusty. They're humid. And during one week in September 2021, they were full of the most sophisticated robots in the world. The robots (along with their human teammates) were there to tackle a massive underground course designed by DARPA, the Defense Advanced Research Projects Agency, as the culmination of its three-year Subterranean Challenge. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a dynamic fuzzy inference marginal linearization (DFIML) method is proposed for modeling nonlinear dynamic systems. This method can transfer a group of input–output data into a time-variant fuzzy system with variable coefficients. It is shown that solutions of time-variant fuzzy systems generalized by DFIML method are universal approximators to solutions of a class of non-autonomous systems. Also the analytical solutions of these time-variant fuzzy systems can be obtained. Finally, a simulation example is provided to illustrate the validity and potential of the developed techniques. [Copyright &y& Elsevier]

Abstract: In this paper we describe the application of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) on the optimization of the membership functions’ parameters of a fuzzy logic controller (FLC) in order to find the optimal intelligent controller for an autonomous wheeled mobile robot. The results obtained by the simulations performed are statistically compared among them and the previous work results obtained with GAs in order to find which is the best optimization technique for this particular robotics problem. [Copyright &y& Elsevier]

Abstract: The design and development of a computational system to support robot–operator collaboration is a challenging task, not only because of the overall system complexity, but furthermore because of the involvement of different technical and scientific disciplines, namely, Software Engineering, Psychology and Artificial Intelligence, among others. In our opinion the approach generally used to face this type of project is based on system architectures inherited from the development of autonomous robots and therefore fails to incorporate explicitly the role of the operator, i.e. these architectures lack a view that help the operator to see him/herself as an integral part of the system. The goal of this paper is to provide a human-centered paradigm that makes it possible to create this kind of view of the system architecture. This architectural description includes the definition of the role of operator and autonomous behaviour of the robot, it identifies the shared knowledge, and it helps the operator to see the robot as an intentional being as himself/herself. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a modeling paradigm that uses fuzzy sets to represent concepts on which control modules of a behavior-based autonomous robot operate. The primitives defined in the modeling paradigm are expressive enough to represent the knowledge needed by planning, coordination, and reactive control of a multi-robot control system. At the same time, it provides a well-founded tool to represent in a compact way the data interpretations needed to reason effectively about what is happening in the world and what is desired to happen. This modeling paradigm makes the design of behavior, planning, and coordination modules easy, since its primitives are simple and expressive. Moreover, it provides a sound framework to deal with uncertainty in sensing and world modeling. [Copyright &y& Elsevier]

Taking inspiration from the visual system of the fly, we describe and characterize a monolithic analog very large-scale integration sensor, which produces control signals appropriate for the guidance of an autonomous robot to visually track a small moving target. This sensor is specifically designed to allow such tracking even from a moving imaging platform which experiences complex background optical flow patterns. Based on relative visual motion of the target and background, the computational model implemented by this sensor emphasizes any small-field motion which is inconsistent with the wide-field background motion. [ABSTRACT FROM AUTHOR]

The control of autonomous intelligent robotic agent operating in unstructured changing environments includes many objective difficulties. One major difficulty concerns the characteristics of the environment that the agent should operate in. In unstructured and changing environments the inconsistency of the terrain, the irregularity of the product and the open nature of the working environment result in complex problems of identification, sensing and control. Problems can range from the effects of varying environmental conditions on the robot sensors and traction performance through to the need to deal with the presence of unexpected situations. Another major challenge is the large amounts of uncertainty that characterises real-world environments. On the one hand, it is not possible to have exact and complete prior knowledge of these environments. On the other hand, knowledge acquired through sensing is affected by uncertainty and imprecision. The quality of sensor information is influenced by sensor noise, the limited field of view, the conditions of observation, and the inherent difficulty of the perceptual interpretation process. Because environments and users of systems continuously change, robotic agents have to be adaptive. Intelligence helps because it gives systems the capacity to adapt more rapidly to environmental changes or to handle much more complex functions. In his paper we introduce this special issue and introduce the difficulty robots are facing in unstructured environments and how learning and computational intelligence can help the robots to adapt and give them the necessary intelligence they to face the challenges they encounter in their environments. [ABSTRACT FROM AUTHOR]

The article discusses the design of autonomous, adaptive machines through evolutionary robotics that incorporate features based in biological systems as of August 2013. It suggests that useful industrial robots can be produced through simulators and then manufactured, avoiding challenges associated with the manual design of autonomous, adaptive robots. Topics include metaheuristic algorithms, developmental robotics, artificial intelligence, swarm robotics, dynamical systems theory, and challenges resulting from reality gaps.

ETH Zurich in Switzerland researchers have recently developed a multi-camera optical tactile sensor that collects information about the contact force distribution applied to its surface. [ABSTRACT FROM AUTHOR]

This article considers the problem of decentralized goal assignment and trajectory generation for multirobot networks when only local communication is available and proposes an approach based on methods related to switched systems and set invariance. A family of Lyapunov-like functions is employed to encode the (local) decision making among candidate goal assignments, under which a group of connected agents chooses the assignment that results in the shortest total distance to the goals. An additional family of Lyapunov-like barrier functions is activated in the case when the optimal assignment may lead to colliding trajectories, maintaining thus system safety while preserving the convergence guarantees. The proposed switching strategies give rise to feedback control policies that are computationally efficient and scalable with the number of agents and, therefore, suitable for applications, including first-response deployment of robotic networks under limited information sharing. The efficacy of the proposed method is demonstrated via simulation results and experiments with six ground robots. [ABSTRACT FROM AUTHOR]

WITH THEIR JAW-DROPPING agility and animal-like reflexes, Boston Dynamics' bioinspired robots have always seemed to have no equal. But that preeminence hasn't stopped the company from pushing its technology to new heights, sometimes literally. Its latest crop of legged machines can trudge up and down hills, clamber over obstacles, and even leap into the air like a gymnast. There's no denying their appeal: Every time Boston Dynamics uploads a new video to YouTube, it quickly racks up millions of views. These are probably the first robots you could call Internet stars. [ABSTRACT FROM AUTHOR]

This article advocates self-driving, robotic technology for cars. Recent challenges organized by DARPA have induced a significant advance in technology for autopilots for cars; similar to those already used in aircraft and marine vessels. This article reviews this technology, and argues that enormous societal benefits can be reaped by deploying this emerging technology in the marketplace. It lays out a vision for deployment, and discusses some of the key remaining technology obstacles. [ABSTRACT FROM AUTHOR]

The article reports on the developments in space probes as researchers at the Jet Propulsion Laboratory (JPL) of the U.S. National Aeronautics and Space Administration (NASA) in Pasadena, California experiment on robot explorers in deep space. NASA is said to be envisioning a future in which teams of smart space probes seek out water or signs of life autonomously. Information about the on-board autonomous science investigation system (OASIS) software and the Autonomous Exploration for Gathering Increased Science (AEGIS) control system are provided.

The relatively new field of artificial life attempts to study and understand biological life by synthesizing artificial life forms. To paraphrase Chris Langton, the founder of the field, the goal of artificial life is to "model life as it could be so as to understand life as we know it." Artificial life is a very broad discipline which spans such diverse topics as artificial evolution, artificial ecosystems, artificial morphogenesis, molecular evolution and many more. Artificial life shares with artificial intelligence (AI) its interest in synthesizing adaptive autonomous agents. Autonomous agents are computational systems that inhabit some complex, dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed. Autonomous agents can take many different forms, depending on the nature of the environment they inhabit. If the environment is the real physical environment, then the agent takes the form of an autonomous robot.

A nonholonomic constant speed underactuated robot with a bounded control range travels in three dimensions. A group of targets unpredictably moves in all three dimensions. The robot measures only the distances to the targets and also has access to a certain spatial direction and its own coordinate (termed “altitude”) along it. We present a new navigation law that drives the robot to the locus of points at a prespecified root-mean-square distance from the targets and ensures dense sweep coverage of this locus within a given range of “altitudes.” This law is rigorously justified by a nonlocal convergence result supported by recommendations on controller tuning; its applicability and performance are confirmed by extensive computer simulations. [ABSTRACT FROM AUTHOR]

This technical note studies the well-known Vicsek model with bounded time-varying delays. We consider the case that the communication delays appear in both position and heading transmissions, which is more realistic in practice. We derive delay-dependent conditions, imposed only on the initial state, to guarantee synchronization of the delayed linear and nonlinear Vicsek models. It is further seen from our simulations that the Vicsek model is indeed sensitive to communication delays in the sense that a large delay can destroy the synchronization behavior. [ABSTRACT FROM AUTHOR]

Emerging Technologies, Food, Machine Learning, Robotics, Robots, Vineyards Keywords: Emerging Technologies; Food; Machine Learning; Robotics; Robots; Vineyards EN Emerging Technologies Food Machine Learning Robotics Robots Vineyards 215 215 1 04/10/23 20230413 NES 230413 2023 APR 13 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- Data detailed on robotics have been presented. Our news reporters obtained a quote from the research from University of Zagreb: "A robotic system has been developed and tested to perform various tasks in the steep vineyards of the Mediterranean region. [Extracted from the article]

The author discusses control systems and autonomy. Topics include the contribution on technologies such as artificial intelligence (AI) and machine learning on the popularity of autonomy and control systems, the history of autonomy which was first described during Ancient Greek, and various levels or degrees of autonomy like fully autonomous controllers.

Keywords: Shanghai; People's Republic of China; Asia; Emerging Technologies; Machine Learning; Robot; Robotics EN Shanghai People's Republic of China Asia Emerging Technologies Machine Learning Robot Robotics 2023 FEB 13 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Trials Week -- Data detailed on Robotics have been presented. Shanghai, People's Republic of China, Asia, Emerging Technologies, Machine Learning, Robot, Robotics. [Extracted from the article]

The objective of the technical note is to make a unicycle trace hypotrochoid like patterns using a simple range based control scheme. These patterns have applications in surveillance, exploration, coverage, etc. In this technical note, the patterns have been characterized in terms of the minimum and maximum radial distance from the center of the pattern. We have shown that under the control scheme and given any controller gain, the agent always achieves a hypotrochoidal pattern. Further, we have shown the existence of controller gains necessary to achieve any desired hypotrochoidal pattern starting from any initial position. We have also analyzed the conditions necessary for the generating patterns in presence of turn rate constraints of the unicycle. The simulations have been performed for the proposed scheme and the mathematical results have been validated. [ABSTRACT FROM AUTHOR]

The article discusses the use of bots as activists on social media. Topics include research by Saiph Savage and colleagues at West Virginia University in which they used bots, called Botivists, to convince users of the microblogging service Twitter to contribute ideas regarding corruption in Latin American governments, researchers' plans to see whether the Botivists can convince people to physically show up to an event, and the use of bots to post positive messages about vaccinations on Twitter.

Researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) have invented an autonomous robotic system that sniffs out the most scientifically interesting, but difficult-to-find, sampling spots in vast, unexplored waters. Researchers are now collaborating with scientists at WHOI to use PLUMES-powered robots to localize chemical plumes at volcanic sites and study methane releases in melting coastal estuaries in the Arctic. [Extracted from the article]

The article discusses the creation of a robotic version of the Etruscan shrew, called Shrewbot, by Tony Prescott and colleagues at the University of Sheffield, England. Topics include an overview of predation in the Etruscan shrew, the Shrewbot's whiskers, which are designed to match the shrew's as closely as possible, and the creation of a robot called Cricketbot to test Shrewbot's ability to detect, recognize, and follow prey.

The article evaluates the Shark Ion Robot 750 Vacuum.

The article offers information on swarmie, an autonomous robot equipped with wi-fi antennae, global positioning system (GPS) and webcams and could be used for planet exploration, developed by U.S. National Aeronautics & Space Administration (NASA) Kennedy Space Center.