This paper presents a new concept for a travel aid for the blind. A prototype device, called the NavBelt, was developed to test this concept. The device can be used as a primary or secondary aid, and consists of a portable computer, ultrasonic sensors, and stereophonic headphones. The computer applies navigation and obstacle avoidance technologies that were developed originally for mobile robots. The computer then uses a stereophonic imaging technique to process the signals from the ultrasonic sensors and relays their information to the user via stereophonic headphones. The user can interpret the information as an acoustic "picture" of the surroundings, or, depending on the operational mode, as the recommended travel direction. The acoustic signals are transmitted as discrete beeps or continuous sounds. Experimental results with the NavBelt simulator and a portable prototype show that users can travel safely in an unfamiliar and cluttered environment at speeds of up to 0.8 m/s.

Collaborative robots (cobots) are important components of the Industry 4.0 paradigm and smart manufacturing. Cobots are known for their ability to interact with the operators in a shared workspace. Due to their spread in the last decade, cobot research proliferated. However, most individual studies focused on specific aspects of cobot deployment, and only scant attention was given to their evaluation (mostly not based on productivity criteria). Thus, better support is needed for cobot acquisition and deployment decisions. This paper answers this need by presenting a summary of the major considerations related to cobots acquisition and deployment, and providing a productivity analysis procedure that supports cobot acquisition and deployment decisions. Defining the cobots’ required characteristics and capabilities, effectively narrows the possible selection of cobots. However, it does not give information as to the economic value of acquiring and deploying a specific cobot. So, in addition to cobots’ characteristics and capabilities, this paper presents computational techniques to analyse and support this decision for a single workstation per se, and for a station in an assembly line. The difference between these two cases is discussed and analysed, and corresponding models are presented with computational examples.

The dramatic increase in the capabilities and availability of autonomous ground and aerial tools introduces safety and security challenges, particularly in protecting strategic infrastructures. In this context, the interception of multiple mobile threats, aiming to invade restricted spaces of such infrastructures is an important topic. This paper focuses on the problem of path planning for intercepting multiple aerial targets by a swarm of UAVs. 3D path planning for interception of moving targets is a challenging task, in particular when the interception is performed by a swarm of UAVs, as there are multiple kinematic and dynamic constraints. The aim is first to allocate targets to the individual UAVs (task assignment) and to construct a 3D path for each one. Many algorithms have been recognized as noble schemes for solving this kind of problems based on Swarm Intelligence (SI), many of them are based on biological systems such as particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony optimization (ABC), bat-inspired algorithm (BA), etc. The paper presents a comprehensive review of SI algorithms centered on the problems related to 3D path planning for target interception by a swarm of UAVs. It also focuses on the improvement of existing SI algorithms for better path optimization. A comprehensive investigation for each algorithm is presented by analyzing its merits and demerits in the context of target interception. This broad review is an outline for scholars and professionals in the field of the swarm of UAVs.

A blind traveler walking through an unfamiliar environment and a mobile robot navigating through a cluttered environment have an important feature in common: both have the kinematic ability to perform the motion, but are depended on a sensory system to detect and avoid obstacles. This paper describes the use of a mobile robot obstacle avoidance system as a guidance device for blind and visually impaired people. Just like electronic signals are sent to a mobile robot's motor controllers, auditory signals can guide the blind traveler around obstacles, or alternatively, they can provide an “acoustic image” of the surroundings. The concept has been implemented and tested in a new traveling aid for the blind, called the Navbelt. Experimental results of subjects traveling with the Navbelt in different surroundings are presented

Data on traffic accidents clearly point to road black spots, where the accident rate is always high. However, road safety research is still far from understanding why these particular places on a road are risky. The reason is the lack of sufficient knowledge on how pedestrians and drivers interact when facing a potentially dangerous traffic situation, and the lack of an integrated framework that relates the data on human behavior to real-world traffic situations. We attempt to tackle this problem by developing SAFEPED, a multi-agent microscopic three-dimensional (3D) simulation of vehicle and pedestrian dynamics at a black spot. SAFEPED is a test platform for evaluating experimentally estimated drivers’ and pedestrians’ behavioral rules, and estimating accident risks in different traffic situations. It aims to analyze the design of existing and future black spots and to assess alternative architectural and environmental solutions in order to identify maximally efficient safety countermeasures.

Modular analysis using the Design Structure Matrix (DSM) identifies the interactions between groups of components, and clusters them into modules in order to achieve competitive advantages in the product design processes. In this paper, a hybrid approach, based on multidimensional scaling (MDS) and clustering methods, is applied to component DSM for product architecting. The motivation is to facilitate better modularizations that enhance different product attributes in various product lifecycle stages. An experimental framework is developed to evaluate the performance of MDS clustering. Three MDS methods and four ubiquitous clustering methods are compared to reveal the most suitable for DSMs. The experimental results with several examples demonstrate that the effectiveness of MDS clustering, and show the superiority of non-metric MDS, SMACOF (Scaling by MAjorizing a Complicated Function) and hierarchical/cosine methods. These methods are capable of partitioning the product architecture into a set of modules and outperform the Newman-Girven algorithm, which has been extensively applied to DSM clustering. The proposed method is capable of partitioning the product architecture into reasonable modules. In addition, it can produce the optimal modules for any number of clusters, which is favourable especially when the cluster number is a higher managerial decision.