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The purpose of this paper is to study how to improve the performance of RFID robot system by anti-collision algorithms. For radio frequency identification (RFID) robots operating in mobile scenes, effective anti-collision algorithm not only reduces missed reading but also enhances the speed of RFID robots movement.

An effective anti-collision algorithm is proposed to accelerate tag identification in RFID robots systems in this paper. The tag collisions in the current time slot are detected by a new method, and then further resolve each small tag collision to improve system throughput, rather than the total tags number estimation. After the reader detected the collision, three different collision resolution methods were described and studied, and the situation of missing tag caused by reader moving is also discussed.

The proposed algorithm achieves theoretical system throughput of about 0.48, 0.50 and 0.61 and simulates to show that the proposed algorithm performance is significantly improved compared with the existing ALOHA-based algorithm.

The proposed RFID anti-collision algorithm is beneficial to improve the moving speed and identification reliability of the RFID robots in complex environments.

In manufacturing environments, mobile radio frequency identification (RFID) robots need to quickly identify and collect various types of passive tag and active tag sensor data. The purpose of this paper is to design a robot system compatible with ultra high frequency (UHF) band passive and active RFID applications and to propose a new anti-collision protocol to improve identification efficiency for active tag data collection.

A new UHF RFID robot system based on a cloud platform is designed and verified. For the active RFID system, a grouping reservation–based anti-collision algorithm is proposed in which an inventory round is divided into reservation period and polling period. The reservation period is divided into multiple sub-slots. Grouped tags complete sub-slot by randomly transmitting a short reservation frame. Then, in the polling period, the reader accesses each tag by polling. When tags’ reply collision occurs, the reader tries to re-query collided tags once, and the pre-reply tags avoid collisions through random back-off and channel activity detection.

The proposed algorithm achieves a maximum theoretical system throughput of about 0.94, and very few tag data frame transmissions overhead. The capture effect and channel activity detection in physical layer can effectively improve system throughput and reduce tag data transmission.

In this paper, the authors design and verify the UHF band passive and active hybrid RFID robot architecture based on cloud collaboration. And, the proposed anti-collision algorithm would improve active tag data collection speed and reduce tag transmission overhead in complex manufacturing environments.

The purpose of this paper is to describe simulation research carried out for the needs of multi-sensor anti-collision system for light aircraft and unmanned aerial vehicles.

This paper presents an analysis related to the practical possibilities of detecting intruders in the air space with the use of optoelectronic sensors. The theoretical part determines the influence of the angle of view, distance from the intruder and the resolution of the camera on the ability to detect objects with different linear dimensions. It has been assumed that the detection will be effective for objects represented by at least four pixels (arranged in a line) on the sensor matrix. In the main part devoted to simulation studies, the theoretical data was compared to the obtained intruders’ images. The verified simulation environment was then applied to the image processing algorithms developed for the anti-collision system.

A simulation environment was obtained enabling reliable tests of the anti-collision system using optoelectronic sensors.

The integration of unmanned aircraft operations in civil airspace is a serious problem on a global scale. Equipping aircraft with autonomous anti-collision systems can help solve key problems. The use of simulation techniques in the process of testing anti-collision systems allows the implementation of test scenarios that may be burdened with too much risk in real flights.

This paper aims for possible improvement of safety in light-sport aviation.

This paper conducts verification of classic flight simulator software suitability for carrying out anti-collision systems tests and development of a flight simulator platform dedicated to such tests.

An area navigation system, RNAV, has dual way‐point selection for advance flight programming and tenth unit readouts for better navigation accuracy. Slant range corrected, linearised horizontal guidance circuits provide constant course widths regardless of distance from VOR stations. Course width (full scale needle deflection on course deviation indicator) is ±5 miles in the ENROUTE Mode and ±1·25 miles in the APPROACH Mode. Distance‐proportional, zero‐time‐lag filtering is included to provide instantaneous course deviation data in cross track manoeuvres.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

The purpose of this study is to monitor the progress of construction activities in an automated way by using sensor-based technologies for tracking multiple resources that are used in building construction.

An automated on-site progress monitoring approach was proposed and a proof-of-concept prototype was developed, followed by a field experimentation study at a high-rise building construction site. The developed approach was used to integrate sensor data collected from multiple resources used in different steps of an activity. It incorporated the domain-specific heuristics that were related to the site layout conditions and method of activity.

The prototype estimated the overall progress with 95% accuracy. More accurate and up-to-date progress measurement was achieved compared to the manual approach, and the need for visual inspections and manual data collection from the field was eliminated. Overall, the field experiments demonstrated that low-cost implementation is possible, if readily available or embedded sensors on equipment are used.

Previous studies either monitored one particular piece of equipment or the developed approaches were only applicable to limited activity types. This study demonstrated that it is technically feasible to determine progress at the site by fusing sensor data that are collected from multiple resources during the construction of building superstructure. The rule-based reasoning algorithms, which were developed based on a typical work practice of cranes and hoists, can be adapted to other activities that involve transferring bulk materials and use cranes and/or hoists for material handling.

The objective of collision avoidance is to assure the separation of aircraft during flight operations — to avoid the situation occurring where two or more aircraft could occupy the same point in airspace at the same time. The problem centres on how best to avoid such a spatial conflict—through ground‐derived data and air traffic control, by means of air‐derived data and pilot judgment, or by some combination of the two? In the quest for a solution, many kinds of systems have been studied and prototype hardware built and tested. Some provided only partial answers; some might meet the safety needs but create implementation difficulties; several have demonstrated the potential for world‐wide application. It is with the last group that we are concerned here. Any collision avoidance system (CAS) selected must be one that meets international requirements; this implies that protection can be offered to all aircraft, that the CAS is compatible with other existing systems and that costs would not be excessive. It goes without saying that reliability must be very high to achieve international acceptance. Several approaches are in development in the United Kingdom and in the United States that could meet these criteria, according to their proponents. However, a controversy continues over which approach is best — one depending heavily on ground‐computed data, the other on air‐derived data. And, there are associated concerns with the presentation of data in the cockpit and the evasive‐manoeuvre capability offered. Finally, with regard to a developmental system favoured by both the United Kingdom and the United States, there appears to be serious divergence of opinions among critics of the system concept related to the impact of radio‐spectrum utilization on existing ICAO secondary surveillance radar standards. Because there is honest technical disagreement on what the optimum collision avoidance system should be, we have made the following pages available as a forum for discussion of the subject of those representing differing sides of the argument: air traffic control, CAS developers, the airlines, the air transport pilots, general aviation and radio frequency allocation. As is usual in the presentation in ICAO Bulletin, the views expressed herein are those of the authors and do not imply in any way acceptance or rejection of those views by ICAO.

The purpose of this paper is to present an implementation of a soft‐computing (SC) based navigation approach on a bi‐steerable mobile robot, Robucar. This approach must provide Robucar with capability to acquire the obstacle avoidance, target localization, decision‐making and action behaviors after learning and adaptation. This approach uses three neural networks (NN) and fuzzy logic (FL) controller to achieve the desired task. The NNs corresponding to the obstacle avoidance and target localization are trained using the back‐propagation algorithm and the last one is based on the reinforcement learning paradigm while the FL controller uses the Mamdani search and match algorithm. Simulation and experimental results are presented, showing the effectiveness of the overall navigation control system.

In this paper, an interesting navigation approach is applied to a car‐like robot, Robucar, with addition of an action behavior to deal with the generation of smooth motions. Indeed, this approach is based on four basic behaviors; three of them are fused under a neural paradigm using Gradient Back‐Propagation (GBP) and reinforcement learning (RL) algorithms and the last behavior uses a FL controller. It uses a set of suggested rules to describe the control policy to achieve the action behavior.

In the implemented SC‐based navigation, the intelligent behaviors necessary to the navigation are acquired by learning using GBP algorithm and adaptation using FL. The proposed approach provides Robucar with more autonomy, intelligence and real‐time processing capabilities. Indeed, the proposed NNs and FLC are able to remedy problems of analytical approaches, missing or incorrect environment knowledge and uncertainties which can lead to undesirable effects as the rough velocity changes. The simulation and experimental results display the ability of the proposed SC‐based navigation approach to provide Robucar with capability to intelligently navigate in a priori unknown environment, illustrating the robustness and adaptation capabilities of the approach.

This work can be extended to consider mobile obstacles with a velocity higher than the velocity of the robot.

This paper presents a learning approach to navigating a bi‐steerable mobile robot in an unknown environment using GBP and RL paradigms.

The Paris/Le Bourget International Air and Space Show is the world's oldest international show and is of considerable importance. It will have 548 exhibitors from 23 countries and there will be 165,000 square metres of display area — with 10,000 square metres for the out‐door static exhibits. The numbers of chalets is to be increased by 55.