Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

/pdf/human-robot-interaction-a-survey-4q8yi606a9.pdf

Human-Robot Interaction: A Survey

The problem of matching measured latitude/longitude points to roads is becoming increasingly important This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs The HMM elegantly accounts for measurement noise and the layout of the road network We test our algorithm on ground truth data collected from a GPS receiver in a vehicle Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work

/pdf/hidden-markov-map-matching-through-noise-and-sparseness-1toovu05b9.pdf

Hidden Markov map matching through noise and sparseness

Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines) to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link. A map-matching algorithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS). The required horizontal positioning accuracy of such ITS applications is in the range of 1 m to 40 m (95%) with relatively stringent requirements placed on integrity (quality), continuity and system availability. A number of map-matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting ITS applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements. The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also highlights the potential impacts of the forthcoming European Galileo system and the European Geostationary Overlay Service (EGNOS) on the performance of map matching algorithms. Although not addressed in detail, the paper also presents some ideas for monitoring the integrity of map-matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.

/pdf/current-map-matching-algorithms-for-transport-applications-1mx9nib3cv.pdf

Current map-matching algorithms for transport applications: State-of-the art and future research directions

In-car positioning and navigation has been a killer application for Global Positioning System (GPS) receivers, and a variety of electronics for consumers and professionals have been launched on a large scale. Positioning technologies based on stand-alone GPS receivers are vulnerable and, thus, have to be supported by additional information sources to obtain the desired accuracy, integrity, availability, and continuity of service. A survey of the information sources and information fusion technologies used in current in-car navigation systems is presented. The pros and cons of the four commonly used information sources, namely, 1) receivers for radio-based positioning using satellites, 2) vehicle motion sensors, 3) vehicle models, and 4) digital map information, are described. Common filters to combine the information from the various sources are discussed. The expansion of the number of satellites and the number of satellite systems, with their usage of available radio spectrum, is an enabler for further development, in combination with the rapid development of microelectromechanical inertial sensors and refined digital maps.

In-Car Positioning and Navigation Technologies—A Survey

Design issues and implementation of internet-based process control systems

Most car navigation systems estimate the car position from dead reckoning and the Global Positioning System (GPS). However, because of the unknown GPS noise, the estimated position has an undesirable error. To solve this problem, a map-matching method is introduced, which uses a digital road map to correct the position error. In this paper, a novel adaptive-fuzzy-network-based C-measure algorithm is proposed, which can find the exact road on which a car moves. The C-measure algorithm is easy to calculate, and calculation time does not increase exponentially with the increase of junctions. For the experiments, a car navigation system is implemented with a small number of sensors. The real road experiments demonstrate the effectiveness and applicability of the proposed algorithm and the developed car navigation system.

Adaptive fuzzy-network-based C-measure map-matching algorithm for car navigation system

This paper proposes a novel direct internet control architecture for internet-based personal robot, which is insensitive to the inherent internet time delay. The personal robot can be controlled using a simulator provided at a local site. Since the internet time delay is affected by the number of nodes and the internet loads, it is variable and unpredictable so that a large internet delay makes some control inputs distorted. The proposed control architecture guarantees that the personal robot can avoid obstacles and reduce the path error and the time difference between a virtual robot at the local site and a real robot at the remote site. This architecture is extended for an uncertain environment. Simulations and experimental results in the real internet environment demonstrate the effectiveness and applicability of the proposed internet control architecture.

/pdf/internet-control-architecture-for-internet-based-personal-3buavsbtkg.pdf

Internet Control Architecture for Internet-Based Personal Robot

Many hybrid algorithms which estimate car's position and velocity from GPS signals and INS signals suffer from the unknown GPS noise characteristics such as S/A noise. The unknown characteristics make it impossible to remove the noise completely. As a result, the estimated position information from the hybrid algorithms will be contaminated with undesirable position errors. It is very desirable and effective to use a digital road map to correct the position error, which is known as a map matching method. In this paper, a novel map matching method is proposed. The effectiveness of this algorithm is verified by the real road experiments. For experimental testing, a car navigation system is built with a DSP chip. It uses one GPS receiver, one vehicle speed sensor and one vertical gyroscope.

Q-factor map matching method using adaptive fuzzy network

Resolved motion rate control (RMRC) is used to map the Cartesian space trajectory to the joint space trajectory. The RMRC for the redundant robot requires the pseudo-inverse of the Jacobian matrix. However the pseudo-inverse is not easy to implement on a digital computer is real time and is mathematically complex. A simple fuzzy RMRC (FRMRC) that can replace the RMRC using the pseudo-inverse of the Jacobian is proposed. An FRMRC with appropriate fuzzy rules, membership functions, and reasoning method can solve the problem of mapping between the spaces without complexity. It is shown that this FRMRC is robust even when passing the singularity points. The mapped joint space trajectory can be directly used to control redundant manipulators. The simulation results verify the efficiency of the method. >

Resolved motion rate control of redundant robots using fuzzy logic

A kinematically redundant robot manipulator has a lot of useful properties, such as avoiding singularities, obstacles and optimized motions, because the redundant manipulator has an infinite number of solutions of inverse kinematics. But, unfortunately it is very hard to get the solution of inverse kinematics when the manipulator is redundant. We propose two schemes which find the globally optimized solution of the redundant manipulator's inverse kinematics using evolutionary programming (EP). Evolutionary programming is a stochastic search method based on principles of evolution and heredity. The two schemes are mainly different in the chromosome representation method. The chromosome of the first scheme is composed of all joint angles while the chromosome of the second is composed of only redundant joint angles. To make a complete trajectory, interpolation is needed and the bound of interpolation error is calculated. We find the globally optimized trajectory of a 3-DOF RRR-type planar manipulator.

Sinn Kim

Papers

Adaptive fuzzy-network-based C-measure map-matching algorithm for car navigation system

Internet Control Architecture for Internet-Based Personal Robot

Q-factor map matching method using adaptive fuzzy network

Resolved motion rate control of redundant robots using fuzzy logic

Optimal trajectory planning of a redundant manipulator using evolutionary programming