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The article reports on the white paper "Automated Guided Vehicles (AGVs) vs. Autonomous Mobile Robots (AMRs): Debunking the Myths" released by Dematic Corp., a supplier of integrated automated technology, software and services to optimize the supply chain. The white paper discusses the capabilities of automated guided vehicles (AGVs) and autonomous mobile robots (AMRs). Tom Kaminski, vice president, commended the two technologies for enhancing intra-logistics effectiveness.

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]

In this paper, we address an autonomous robot vacuum system, composed of two robots. Conventional robot vacuum has a small dust bin, and its suction power is low. This low cleaning power has been considered as a defect of a conventional robot vacuum. If we use a large robot vacuum with high cleaning power, then we cannot clear various narrow environments, such as narrow space under a sofa. To resolve this problem, we address an autonomous robot vacuum system composed of a small Cleaner Robot (CR) and a large dust Storage Robot (SR). The CR and the SR are connected using a hose, thus they must maneuver as a pair. The role of the small CR is to clean various environments, such as narrow space under a sofa. The CR is small, thus it cannot contain a large motor for increasing its cleaning power. The dust sucked by the CR is transmitted to the SR through the hose. The SR is large, hence it can have a large motor for increasing its cleaning power compared to a conventional robot vacuum. To the best of our knowledge, our paper is novel in addressing an autonomous robot vacuum system composed of a CR and a SR. Our paper is unique in addressing the path plan of these two connected robots, so that the entire open workspace can be cleaned by the CR as fast as possible. Under MATLAB simulations, we demonstrate the effectiveness of path planning for our autonomous robot vacuum system. We show that the proposed tethered robots improve the cleaning efficiency, compared to the case where a single large robot vacuum is applied. [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]

Autonomous manipulation operations represent the high intelligent coordination from robotic vision and control, it is also a symbol of the advances of robotic intelligence. The limitations of visual sensing and the increasingly complex experimental conditions make autonomous manipulation operations more difficult, particularly for deep reinforcement learning methods, which can enhance robotic control intelligence but require a lot of training process. Due to the high-dimensional continuous state space and continuous action space characteristics of underwater operations, this paper adopts a policy-based reinforcement learning method as the foundational approach. To address the issues of instability and low convergence efficiency in traditional policy-based reinforcement learning algorithms during the learning process, this paper proposes a novel policy learning method. This method adopts the Proximal Policy Optimization algorithm (PPO Clip) and optimizes it through an actor-critic network. The aim is to improve the stability and effectiveness of convergence in the learning process. In the underwater training environment, a new reward shaping scheme has been designed to address the issue of reward sparsity during the training process. The manually crafted dense reward function is utilized as attractive and repulsive potential functions for goal manipulation and obstacle avoidance. On the highly complex underwater manipulation and training environment, transferred learning algorithm has been established to reduce the training times and compensate the differences between the simulation and experiment. Simulations and tank experiments have verified the performance of the proposed strategy learning method. [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]

The Connected Autonomous Vehicle (CAV) is an emerging technology that aims to improve traffic flow by allowing autonomous vehicles to communicate with each other. The ability of the network to control and behave as a cooperative entity allows it to reduce the inter-vehicle spacing distance. The used model considers the optimal velocity and relative velocity models, providing an advanced capability to stabilize CAV. In Al-Darabsah et al. (2021), we presented a nonlinear model of the CAV network when it has time-varying communication delays and studied its plant and string stability. This paper aims to validate the model through numerical simulations and experimental data. The goal is to offer comprehensive insights into the theoretical results and their practical applications. Specifically, we explore the impact of the frequency and amplitude of the time-varying delay function on platoon (string) stability of the time-delayed platoon of connected autonomous robots. We present numerical results and an experiment of four autonomous robots and use the proposed method to analyze platoon stability. In the experiment, we observed that the proposed control signal effectively stabilizes a CAV system under time-varying delays. These results show stability in the presence of fluctuating delays. [ABSTRACT FROM AUTHOR]

Researchers from the Spanish National Research Council (CSIC) have developed a new pelvic assistance treadmill-based exoskeleton (PeXo) for gait rehabilitation. The system includes 5 actively controlled Degrees of Freedom (DOFs) to provide full control and assistance of the user's pelvis during walking activities. A pilot study with a stroke survivor showed that PeXo can be used safely and reliably, although it reduced the user's Range of Motion at the pelvis and lower limb joints. The user reported positive feedback, suggesting potential advantages of PeXo that should be explored in future studies. [Extracted from the article]

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]

Copyright of Productronic is the property of Dokumentations- und Informationszentrum (DIZ) Munchen GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a novel algorithm for the problem of trajectory tracking when considering the obstacle avoidance in an unknown environment only using the measurements of the follower is presented. First of all, for the kinematic model, the problem of trajectory tracking is solved based on the measurements collected from the onboard sensors of the follower, and an algorithm with the rotating matrix is proposed to solve the problem of obstacle avoidance. Thereafter, the dynamic surface control is used in the dynamic model, and a low-pass filter is given to decrease the cost of computation. Meanwhile, the disturbance rejection of the closed-loop system is discussed for the existence of an unknown input disturbance. Compared with the existing results, the wireless communication and the leader's velocity are no longer required in the entire control task, which makes it easier to implement in engineering. The results of both numerical simulation and physical experiments are given to verify and demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

The paper presents a new mathematical model to analyze real time applied torques and non-holonomic constraint forces/moment of a security robot, comprised of a wheeled mobile platform and a manipulator for special security task applications. Based on the analysis the optimal selection of the wheels of the robot can be performed so that the slippage on the surface has been minimized. The proposed analysis constitutes a novel component in the modeling and design of the security robots. Numerical experiments illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of WIK-Diskussionsbeiträge is the property of WIK Wissenschaftliches Institut fur Infrastruktur und Kommunikationsdienste GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Keywords for this news article include: Rajasthan, India, Asia, Emerging Technologies, Health and Medicine, Machine Learning, Rehabilitation, Robotics, Robots, Manipal University. Keywords: Rajasthan; India; Asia; Emerging Technologies; Health and Medicine; Machine Learning; Rehabilitation; Robotics; Robots EN Rajasthan India Asia Emerging Technologies Health and Medicine Machine Learning Rehabilitation Robotics Robots 863 863 1 07/10/23 20230714 NES 230714 2023 JUL 14 (NewsRx) -- By a News Reporter-Staff News Editor at Genomics & Genetics Weekly -- Current study results on Robotics have been published. Rajasthan, India, Asia, Emerging Technologies, Health and Medicine, Machine Learning, Rehabilitation, Robotics, Robots. [Extracted from the article]

While some clarity is beginning to emerge on the technologies being explored under pillar two, New Zealand's policy approach to these types of technologies has become increasingly murky. Defence Minister Andrew Little's recent announcement that New Zealand would be "willing to explore" participation in military technology sharing - or "pillar two" - under the AUKUS security arrangement has already divided opinion. [Extracted from the article]

The article discusses how technologies such as internet of things, drones, and robotics will extend the capabilities of autonomous systems. It mentions that Royal Australian Air Force and Boeing Australia are building an autonomous fighter jet for possible operational use in the mid-2020s. It also mentions that retail stores and warehouses to install autonomous robots for inventory management.

Travelers at Pittsburgh International Airport can have smartphone orders delivered to them by autonomous robots, according to a triblive.com report. [ABSTRACT FROM AUTHOR]

The article discusses the work profile of Mohan Trivedi', Distinguished Professor of Engineering, UC San Diego; Founding Director, Computer Vision & Robotics Research and the Laboratory for Intelligent and Safe Automobiles whose research is in machine vision, intelligent transportation systems.

Researchers at South China University of Technology have developed a fully autonomous robotic ultrasound system for thyroid scanning. The system is able to scan thyroid regions without human assistance and can identify malignant nodules. It utilizes human skeleton point recognition, reinforcement learning, force feedback, and Bayesian optimization to overcome challenges in locating thyroid targets. Experimental results have shown that the system can perform high-quality ultrasound scans comparable to those obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data for the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation. [Extracted from the article]

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]

A new concept of autonomous maneuvering tethered-net space robot system is presented in this paper. The autonomous maneuvering tethered-net space robot system consists of platform, tether, a flexible net and several autonomous maneuvering units. The task range and maneuverability have been significantly improved. The concept, task and traits of the autonomous maneuvering tethered-net space robot system are described in detail. In order to research the variation of the net formation, a lumped mass model which divides the net into finite mass points connected with springs without mass, has been established. The maneuvering control thrust has been taken into account. The digital simulations under different conditions are implemented. The simulation results show that the closing up movement will occur when the approaching phase startes, which related to the initial velocity of the net. The approaching trace will also depart from the correct trace. These adverse movements must be controlled by the autonomous maneuvering unit. [ABSTRACT FROM AUTHOR]

Autonomous robots deployed in retail cover millions of square meters a week with inventory scanning robots capturing 26 million images a day. Brain Corp CEO David Pinn shares what he envisions to come. [ABSTRACT FROM AUTHOR]

A report from the Shanghai University of Engineering Science in China discusses the development of a robotic system for autonomous massage path planning and stable interaction control. The study incorporates deep learning, 3D reconstruction, and impedance control to enable the robot to understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot-human contact. The research demonstrates improved accuracy in acupoint recognition and the ability to generate massage trajectories adapted to the shape of the human body. The study was supported by various funding sources and has been peer-reviewed. [Extracted from the article]

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]

Harbor Lockers, a division of Luxer One, a public locker network where businesses can deliver, store and pick up items, has teamed with Ottonomy, a provider of autonomous robots, for its locker-on-robot, Ottobot Locker, for last-mile delivery. The partnership augments... [ABSTRACT FROM AUTHOR]

Ottonomy, a provider of autonomous delivery robots, has teamed with Harbor Lockers to introduce its Ottobot Locker, according to a press release. Harbor Lockers, a division of Luxer One Ventures, is a public locker network where businesses can deliver, store... [ABSTRACT FROM AUTHOR]

Etisalat by e&, a telecom provider in the United Arab Emirates, has introduced an autonomous store known as EASE (Etisalat by e& Autonomous Store Experience), according to a press release. SPAN, a systems integration innovator, teamed with Cleveron, a provider... [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.