Operations Of Such Applications Research Articles

Teaming with industrial cobots: A socio‐technical perspective on safety analysis

AbstractCollaborative human–machine interaction will be progressively intensified in industrial applications. The aim of this article is to examine current approaches to cobot safety by showing that these approaches can additionally benefit from systems thinking methods. The first part of this article covers a narrative literature review on predominantly techno‐centric robot safety approaches, with a strong focus on containing kinetic energy and ensuring separation with humans. The second part introduces systems thinking methods to analyze a socio‐technical perspective on cobot safety, including joint cognitive systems and distributed cognition perspectives. This explorative research dimension is expected to overcome an overly narrow interpretation of safety issues, anticipating the challenges ahead in ever more complex cobot applications. This article embraces a socio‐technical perspective to explore the potential of Joint Cognitive Systems to manage risk and safety in cobot applications. Three systemic safety analysis approaches are presented and tested with a demonstrator case study concerning their feasibility for cobot applications: System‐Theoretic Accident Model and Processes (STAMP); Functional Resonance Analysis Method (FRAM); and Event Analysis of Systemic Teamwork (EAST). These methods each provide interesting extensions to complement the traditional understanding of risk as required by current and future industrial cobot implementations. The power of systemic methods for safer and more efficient cobot operations lies in revealing the distributed and emergent result from joint actions and overcoming the reductionist view from individual failures or single agent responsibilities. The safe operation of cobot applications can only be achieved through alignment of design, training, and operation of such applications.

Fuzzy human motion analysis: A review

Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.

Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams

There is a growing interest in applications that utilize continuous sensing of individual activity or context, via sensors embedded or associated with personal mobile devices (e.g., smartphones). Reducing the energy overheads of sensor data acquisition and processing is essential to ensure the successful continuous operation of such applications, especially on battery-limited mobile devices. To achieve this goal, this paper presents a framework, called ACQUA, for ‘acquisition-cost’ aware continuous query processing. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the one or more smartphones, with a pull-based asynchronous model, where a smartphone retrieves appropriate blocks of relevant sensor data from individual sensors, as an integral part of the query evaluation process. We describe algorithms that dynamically optimize the sequence (for complex stream queries with conjunctive and disjunctive predicates) in which such sensor data streams are retrieved by the query evaluation component, based on a combination of (a) the communication cost & selectivity properties of individual sensor streams, and (b) the occurrence of the stream predicates in multiple concurrently executing queries. We also show how a transformation of a group of stream queries into a disjunctive normal form provides us with significantly greater degrees of freedom in choosing this sequence, in which individual sensor streams are retrieved and evaluated. While the algorithms can apply to a broad category of sensor-based applications, we specifically demonstrate their application to a scenario where multiple stream processing queries execute on a single smartphone, with the sensors transferring their data over an appropriate PAN technology, such as Bluetooth or IEEE 802.11. Extensive simulation experiments indicate that ACQUA’s intelligent batch-oriented data acquisition process can result in as much as 80 % reduction in the energy overhead of continuous query processing, without any loss in the fidelity of the processing logic.

Energy Efficient MAC Protocol for Low-Energy Critical Infrastructure Monitoring Networks Using Wakeup Radio

Critical infrastructure monitoring applications are rapidly increasing. Application requirements include reliable data transfer, energy efficiency, and long deployment lifetime. These applications must also be able to operate in an extremely low-cost communication environment in order to be attractive to potential users. A low rate wireless personal area network can help control and manage the operations of such applications. In this paper, we present a medium access control (MAC) protocol for low-energy critical infrastructure monitoring (LECIM) applications. The proposed MAC protocol is based on a framed slotted aloha multiple access schemes. For downlink communication, we use a wakeup radio approach to avoid complex bookkeeping associated with the traditional MAC protocols. Analytical expressions for power consumption and delay are derived to analyze and compare the performance of our proposed protocol with the existing well-known T-MAC, B-MAC, X-MAC, ZigBee, and WiseMAC protocols. It is shown that our proposed protocol outperforms all the other protocols in terms of power consumption and delay.