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Towards Intelligent Social Robots: From Naive Robots to Robot Sapiens
The supply chains in the 21st-century manufacturing sector have become more intricate than before this era. Suddenly, the socio-economic and business crescendos that determine corporate entities now require an in-out, demand-driven system. The Fourth Industrial Revolution (4IR) intervention enables the automation of the production processes seamlessly through the Internet of Things (IoT) and Cyber-Physical Systems (CPS). The research aims to review the impact of the 4IR implementation and supply chain management (SCM) strategies, thereby creating an adoption template by considering potential opportunities and threats of the fourth industrial paradigm. A comprehensive literature review was conducted by investigating scholarly articles and industry reports available in the global repositories to explore key drivers and barriers of the 4IR adoption using the supply chain management strategy. Research findings suggest that the 4IR has brought new threats and opportunities in manufacturing industries using the supply chain management strategy.
This paper presents a new solution to haptic based teleoperation to control a large-sized slave robot for space exploration, which includes two specially designed haptic joysticks, a hybrid master-slave motion mapping method, and a haptic feedback model rendering the operating resistance and the interactive feedback on the slave side. Two devices using the 3R and DELTA mechanisms respectively are developed to be manipulated to control the position and orientation of a large-sized slave robot by using both of a user's two hands respectively. The hybrid motion mapping method combines rate control and variable scaled position mapping to realize accurate and efficient master-slave control. Haptic feedback for these two mapping modes is designed with emphasis on ergonomics to improve the immersion of haptic based teleoperation. A stiffness estimation method is used to calculate the contact stiffness on the slave side and play the contact force rendered by using a traditional spring-damping model to a user on the master side stably. Experiments by using virtual environments to simulate the slave side are conducted to validate the effectiveness and efficiency of the proposed solution.
Autonomous vehicles (AV’s) are appearing on roads, based on standard robotic mapping and navigation algorithms. However their ability to interact with other road-users is much less well understood. If AVs are programmed to stop every time another road user obstructs them, then other road users simply learn that they can take priority at every interaction, and the AV will make little or no progress. This issue is especially important in the case of a pedestrian crossing the road in front of the AV. The present methods paper expands the sequential chicken model introduced in (Fox et al., 2018), using empirical data to measure behavior of humans in a controlled plus-maze experiment, and showing how such data can be used to infer parameters of the model via a Gaussian Process. This providing a more realistic, empirical understanding of the human factors intelligence required by future autonomous vehicles.
We present a novel approach to automatically identify driver behaviors from vehicle trajectories and use them for safe navigation of autonomous vehicles. We propose a novel set of features that can be easily extracted from car trajectories. We derive a data-driven mapping between these features and six driver behaviors using an elaborate web-based user study. We also compute a summarized score indicating a level of awareness that is needed while driving next to other vehicles. We also incorporate our algorithm into a vehicle navigation simulation system and demonstrate its benefits in terms of safer real-time navigation, while driving next to aggressive or dangerous drivers.
Cryptography, in particular public-key cryptography, has emerged in the last 20 years as an important discipline that is not only the subject of an enormous amount of research, but provides the foundation for information security in many applications. Standards are emerging to meet the demands for cryptographic protection in most areas of data communications. Public-key cryptographic techniques are now in widespread use, especially in the financial services industry, in the public sector, and by individuals for their personal privacy, such as in electronic mail. This Handbook will serve as a valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography. It is a necessary and timely guide for professionals who practice the art of cryptography. The Handbook of Applied Cryptography provides a treatment that is multifunctional: It serves as an introduction to the more practical aspects of both conventional and public-key cryptography It is a valuable source of the latest techniques and algorithms for the serious practitioner It provides an integrated treatment of the field, while still presenting each major topic as a self-contained unit It provides a mathematical treatment to accompany practical discussions It contains enough abstraction to be a valuable reference for theoreticians while containing enough detail to actually allow implementation of the algorithms discussed Now in its third printing, this is the definitive cryptography reference that the novice as well as experienced developers, designers, researchers, engineers, computer scientists, and mathematicians alike will use.
We address problems underlying the algorithmic question of automating the co-design of robot hardware in tandem with its apposite software. Specifically, we consider the impact that degradations of a robot’s sensor and actuation suites may have on the ability of that robot to complete its tasks. We introduce a new formal structure that generalizes and consolidates a variety of well-known structures including many forms of plans, planning problems, and filters, into a single data structure called a procrustean graph, and give these graph structures semantics in terms of ideas based in formal language theory. We describe a collection of operations on procrustean graphs (both semantics-preserving and semantics-mutating), and show how a family of questions about the destructiveness of a change to the robot hardware can be answered by applying these operations. We also highlight the connections between this new approach and existing threads of research, including combinatorial filtering, Erdmann’s strategy complexes, and hybrid automata.
We examine the problem of target tracking whilst simultaneously preserving the target’s privacy as epitomized by the robotic panda-tracking scenario, which O’Kane introduced at the 2008 Workshop on the Algorithmic Foundations of Robotics to elegantly illustrate the utility of ignorance. The present paper reconsiders his formulation and the tracking strategy he proposed, along with its completeness. We explore how the capabilities of the robot and panda affect the feasibility of tracking with a privacy stipulation, uncovering intrinsic limits, no matter the strategy employed. This paper begins with a one-dimensional setting and, putting the trivially infeasible problems aside, analyzes the strategy space as a function of problem parameters. We show that it is not possible to actively track the target as well as protect its privacy for every non-trivial pair of tracking and privacy stipulations. Secondly, feasibility can be sensitive, in several cases, to the information available to the robot initially. Quite naturally in the one-dimensional model, one may quantify sensing power by the number of perceptual (or output) classes available to the robot. The robot’s power to achieve privacy-preserving tracking is bounded, converging asymptotically with increasing sensing power. We analyze the entire space of possible tracking problems, characterizing every instance as either achievable, constructively by giving a policy where one exists (some of which depend on the initial information), or proving that the instance is impossible. Finally, to relate some of the impossibility results in one dimension to their higher-dimensional counterparts, including the planar panda-tracking problem studied by O’Kane, we establish a connection between tracking dimensionality and the sensing power of a one-dimensional robot.
We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.