New topics of discussion emerge continuously as political and cultural environments shift. These topics can convey a range of ideas, stories, values, and shared experiences. In open source data, discourse is often infused with emotionally-charged prose expressed by large and diverse pools of authors. Manually identifying and analyzing the emotional content of combined discussions from millions of individuals is an impossible task for any single analyst or decision maker. Technologies that can automatically identify emerging topics of discussion and align them with public opinions can provide early warning to acts of violence or other threats [1]. We present an approach to detecting basic emotions associated with topics by combining work in topic modeling with affect analysis. We apply the correspondence LDA topic model (CorrLDA2) [2] to correlate emotional states with topics. Using this technique, topics have associated emotions and emotion categories are correlated over a corpus of text.

This paper shows that the Con metric from Dempster-Shafer theory is a good indicator of sensing quality, where an increase in the conflict metric value correlates with a decrease in map quality (at p ≤ 0.05). Two sets of experiments were conducted. In one, sonar data were gathered from a Nomad 200 robot operating in typical indoor hallways. Another used sonars on an RWI Urban robot in a confined, irregular tunnel built from eight different construction materials. For each set, an occupancy grid map was built and evaluated through a quantitative comparison with the ground truth. It is expected that the results of this study will generalize not only to other sensors and multi-sensor fusion, but any application where consistency can be assumed. It also contributes a design for an inexpensive reconfigurable confined space testbed.

This paper presents a detailed look at how unmanned ground vehicles (UGVs) fail in the field using information from 10 studies and 15 different models in Urban Search and Rescue or military field applications. One explores failures encountered in a limited amount of time in a real crisis (World Trade Center rescue response). Another covers regular use of 13 robots over two years. The remaining eight studies are field tests of robots performed by the Test and Evaluation Coordination Office at Fort Leonard Wood. A novel taxonomy of UGV failures is presented which categorizes failures based on the cause (physical or human), its impact, and its repairability. Important statistics are derived and illustrative examples of physical failures are examined using this taxonomy. Reliability in field environments is low, between 6 and 20 hours mean time between failures. For example, during the PANTHER study (F. Cook, 1997) 35 failures occurred in 32 days. The primary cause varies: one study showed 50% of failures caused by effectors; another study showed 54% of failures occurred in the control system. Common causes are: unstable control systems, platforms designed for a narrow range of conditions, limited wireless communication range, and insufficient bandwidth for video-based feedback.

Mobile robot reliability must be guaranteed before they can be employed in hazardous domains like mine clearing or nuclear waste handling, but recent studies of robots used in urban search and rescue and military scenarios have shown a mean time between failures (MTBF) in the field of 6 to 20 hours. This paper extends previous work characterizing robot failures by including recent data and organizing failures according to a novel taxonomy , which includes human failures. Failure type and frequency data were collected from 15 robots representing three manufacturers and seven models over a period of three years, in a variety of environments. Standard manufacturing measures for product reliability were used. The results show that overall MTBF and availability have improved since the previous analysis but are still low. The MTBF across all robot types was 24 hours and availability was 54%. The control system was the most common source of failures (32%), followed by the mechanical platform. Statistical analysis shows that the time between failures, time to repair, and downtime vary widely. For this reason the means reported here are not reliable predictors for future failures, but still provide information on the overall frequency and consequences of mobile robot failures.

The implementations of a distributed, autonomous error handler (EH) and a human-robot interface (HRI) are presented. The interface is combined with the EH to allow a human operator to see that a failure has occurred on a robot and whether or not it has been served by the EH. An experiment was run to test how well the EH and the interface work together, as well as the usefulness of the EH. The results were inconclusive, although the EH and interface worked together successfully.

The purpose of this study is to evaluate the utility of a diagnosis technique, which uses minimum message length (MML) for autonomous mobile robot fault diagnosis. A simulator was developed for a behavior-based robotic system and results were gathered for over 24,000 simulations varying the level of test noise and the components with simulated failures. The results showed that the MML diagnosis technique did not perform well as a turn-key solution. In two different data sets, only 0.59% and 1.19% of the test cases were correctly diagnosed and none of the cases with multiple failures were identified correctly. This paper presents the approach used to evaluate the new technique, the results, and a discussion of why MML diagnosis may not be appropriate for mobile robotics.

Failure data on mobile robots is critical for three reasons: to support the theory of autonomous fault detection, identification, and recovery necessary for success in new domains; provide design and manufacturing feedback to the robotics community; and permit project managers to accurately create development schedules. The failures considered in this paper occurred over a period of 2 years, in a variety of environments. Failure type and frequency data were collected from thirteen robots representing three manufacturers and seven models. The data was analyzed using standard manufacturing measures for the reliability of a product. The mean time between failures represents the average time to the next failure. Availability was used to gauge the impact of a failure on a project. The results show that the reliability for mobile robots is low, with an average MTBF of 8 hours and availability of less than 50%. The platform itself was the source of most failures (42%) for field robots, while the control system was responsible for 29% of the failures.