Robotics Evolution: from Remote Brain to Cloud

Robotic systems have been evolving since decades and touching almost all aspects of life, either for leisure or critical applications. Most of traditional robotic systems operate in well-defined environments utilizing pre-configured on-board processing units. However, modern and foreseen robotic applications ask for complex processing requirements that exceed the limits of on-board computing power. Cloud computing and the related technologies have high potential to overcome on-board hardware restrictions and can improve the performance efficiency. This research highlights the advancements in robotic systems with focus on cloud robotics as an emerging trend. There exists an extensive amount of effort to leverage the potentials of robotic systems and to handle arising shortcomings. Moreover, there are promising insights for future breed of intelligent, flexible, and autonomous robotic systems in the Internet of Things era

Common Metrics to Benchmark Human-Machine Teams (HMT): A Review

A significant amount of work is invested in human-machine teaming (HMT) across multiple fields. Accurately and effectively measuring system performance of an HMT is crucial for moving the design of these systems forward. Metrics are the enabling tools to devise a benchmark in any system and serve as an evaluation platform for assessing the performance, along with the verification and validation, of a system. Currently, there is no agreed-upon set of benchmark metrics for developing HMT systems. Therefore, identification and classification of common metrics are imperative to create a benchmark in the HMT field. The key focus of this review is to conduct a detailed survey aimed at identification of metrics employed in different segments of HMT and to determine the common metrics that can be used in the future to benchmark HMTs. We have organized this review as follows: identification of metrics used in HMTs until now, and classification based on functionality and measuring techniques. Additionally, we have also attempted to analyze all the identified metrics in detail while classifying them as theoretical, applied, real-time, non-real-time, measurable, and observable metrics. We conclude this review with a detailed analysis of the identified common metrics along with their usage to benchmark HMTs

2009 Performance Metrics for Intelligent Systems Workshop (PERMIS '09) Gaithersburg, MD. Measuring Robot Performance in Real-time for NASA Robotic Reconnaissance Operations

Technical advances since Apollo make it possible to perform robotic reconnaissance to gain a better understanding of lunar sites prior to human exploration. NASA is conducting analog field tests to investigate these operations concepts with advanced robots and simulated flight operations. We have developed robot performance monitoring software for use during robotic reconnaissance operations. We measure robot performance by monitoring robot data in real-time and computing robot performance metrics from that data. Metrics are computed for two regimes of flight operations – remote supervision of autonomous robot operations and debrief support after a flight operations shift. In this paper we describe our performance monitoring software, define the metrics we compute, discuss how these metrics are used in flight operations, and summarize results from recent field tests. Categories and Subject Descriptors C.4.3 [Performance of Systems]: Measurement techniques, Performance attributes, Reliability, availability, an