Experimental Evaluation of Nearest Neighbor Exploration Approach in Field Environments

© 2017 IEEE. Inspecting surface conditions in 3-D environments such as steel bridges is a complex, time-consuming, and often hazardous undertaking that is an essential part of tasks such as bridge maintenance. Developing an autonomous exploration strategy for a mobile climbing robot would allow for such tasks to be completed more quickly and more safely than is possible with human inspectors. The exploration strategy tested in this paper, called the nearest neighbors exploration approach (NNEA), aims to reduce the overall exploration time by reducing the number of sensor position evaluations that need to be performed. NNEA achieves this by first considering at each time step only a small set of poses near to the current robot as candidates for the next best view. This approach is compared with another exploration strategy for similar robots performing the same task. The improvements between the new and previous strategy are demonstrated through trials on a test rig, and also in field trials on a ferromagnetic bridge structure. Note to Practitioners-This paper was motivated by the problem of inspecting confined spaces for rust and flaking paint with a manipulator robot arm. Existing approaches involve creating a large set of candidate robot poses to take a scan from. Evaluating all these candidate poses is very time consuming if full coverage is guaranteed. This paper suggests a principled method for restricting the size of this set in a way that does not reduce inspection coverage but decreases overall time taken for inspection

A sliding window approach to exploration for 3D map building using a biologically inspired bridge inspection robot

© 2015 IEEE. This paper presents a Sliding Window approach to viewpoint selection when exploring an environment using a RGB-D sensor mounted to the end-effector of an inchworm climbing robot for inspecting areas inside steel bridge archways which cannot be easily accessed by workers. The proposed exploration approach uses a kinematic chain robot model and information theory-based next best view calculations to predict poses which are safe and are able to reduce the information remaining in an environment. At each exploration step, a viewpoint is selected by analysing the Pareto efficiency of the predicted information gain and the required movement for a set of candidate poses. In contrast to previous approaches, a sliding window is used to determine candidate poses so as to avoid the costly operation of assessing the set of candidates in its entirety. Experimental results in simulation and on a prototype climbing robot platform show the approach requires fewer gain calculations and less robot movement, and therefore is more efficient than other approaches when exploring a complex 3D steel bridge structure

Key feature-based approach for efficient exploration of structured environments

© 2015 IEEE. This paper presents an exploration approach for robots to determine sensing actions that facilitate the building of surface maps of structured partially-known environments. This approach uses prior knowledge about key environmental features to rapidly generate an estimate of the rest of the environment. Specifically, in order to quickly detect key features, partial surface patches are used in combination with pose optimisation to select a pose from a set of nearest neighbourhood candidates, from which to make an observation of the surroundings. This paper enables the robot to greedily search through a sequence of nearest neighbour poses in configuration space, then converge upon poses from which key features can best be observed. The approach is experimentally evaluated and found to result in significantly fewer exploration steps compared to alternative approaches

Expanding wavefront frontier detection: An approach for efficiently detecting frontier cells

Frontier detection is a key step in many robot exploration algorithms. The more quickly frontiers can be detected, the more efficiently and rapidly exploration can be completed. This paper proposes a new frontier detection algorithm called Expanding Wavefront Frontier Detection (EWFD), which uses the frontier cells from the previous timestep as a starting point for detecting the frontiers in the current timestep. As an alternative to simply comparing against the naive frontier detection approach of evaluating all cells in a map, a new benchmark algorithm for frontier detection is also presented, called Naive Active Area frontier detection, which operates in bounded constant time. EWFD and NaiveAA are evaluated in simulations and the results compared against existing state-of-the-art frontier detection algorithms, such as Wavefront Frontier Detection and Incremental-Wavefront Frontier Detection

Diethyl­ammonium anilino(meth­oxy)­phosphinate

The title compound, [Et2NH2][(EtO)PO2(C6H5NH)] or C4H12N+·C8H11NO3P−, is a molecular salt with two anions containing PO3N groupings and two cations in the asymmetric unit. A network of N—H⋯O hydrogen bonds link the cations and anions into a two-dimensional network

Exploring in 3D with a climbing robot: Selecting the next best base position on arbitrarily-oriented surfaces

© 2016 IEEE. This paper presents an approach for selecting the next best base position for a climbing robot so as to observe the highest information gain about the environment. The robot is capable of adhering to and moving along and transitioning to surfaces with arbitrary orientations. This approach samples known surfaces, and takes into account the robot kinematics, to generate a graph of valid attachment points from which the robot can either move to other positions or make observations of the environment. The information value of nodes in this graph are estimated and a variant of A∗ is used to traverse the graph and discover the most worthwhile node that is reachable by the robot. This approach is demonstrated in simulation and shown to allow a 7 degree-of-freedom inchworm-inspired climbing robot to move to positions in the environment from which new information can be gathered about the environment

Efficient neighbourhood-based information gain approach for exploration of complex 3D environments

This paper presents an approach for exploring a complex 3D environment with a sensor mounted on the end effector of a robot manipulator. In contrast to many current approaches which plan as far ahead as possible using as much environment information as is available, our approach considers only a small set of poses (vector of joint angles) neighbouring the robot's current pose in configuration space. Our approach is compared to an existing exploration strategy for a similar robot. Our results demonstrate a significant decrease in the number of information gain estimation calculations that need to be performed, while still gathering an equivalent or increased amount of information about the environment. © 2013 IEEE

Climbing Robot for Steel Bridge Inspection: Design Challenges

Inspection of bridges often requires high risk operations such as working at heights, in confined spaces, in hazardous environments; or sites inaccessible by humans. There is significant motivation for robotic solutions which can carry out these inspection tasks. When inspection robots are deployed in real world inspection scenarios, it is inevitable that unforeseen challenges will be encountered. Since 2011, the New South Wales Roads & Maritime Services and the Centre of Excellence for Autonomous Systems at the University of Technology, Sydney, have been working together to develop an innovative climbing robot to inspect high risk locations on the Sydney Harbour Bridge. Many engineering challenges have been faced throughout the development of several prototype climbing robots, and through field trials in the archways of the Sydney Harbour Bridge. This paper will highlight some of the key challenges faced in designing a climbing robot for inspection, and then present an inchworm inspired robot which addresses many of these challenges

Polymorphisms in genes in the androgen pathway and risk of Barrett's esophagus and esophageal adenocarcinoma

The strong male predominance in Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) remains inadequately explained, but sex hormones might be involved. We hypothesized that single nucleotide polymorphisms (SNPs) in the androgen pathway influence risk of developing BE and EAC. This genetic-epidemiological analysis included 14 studies from Australia, Europe and North America. Polymorphisms in 16 genes coding for the androgen pathway were analyzed using a gene-based approach: versatile gene-based test association study. This method evaluates associations between a trait and all SNPs within a specific gene rather than each SNP marker individually as in a conventional GWAS. The data were stratified for sex, body-mass index, waist-to-hip ratio, tobacco smoking and gastroesophageal reflux status. Included were data from 1,508 EAC patients, 2,383 BE patients and 2,170 control participants. SNPs within the gene CYP17A1 were associated with risk of BE in the sexes combined (p = 0.002) and in males (p = 0.003), but not in females separately (p = 0.3). This association was found in tobacco smokers (p = 0.003) and in BE patients without reflux (p = 0.004), but not in nonsmokers (p = 0.2) or those with reflux (p = 0.036). SNPs within JMJD1C were associated with risk of EAC in females (p = 0.001). However, none of these associations replicated in a subsequent sample. Fourteen other genes studied did not reach statistically significant levels of association with BE, EAC or the combination of BE and EAC, after correcting for the number of genes included in the analysis. In conclusion, genetic variants in the androgen-related genes CYP17A1 and JMJD1C might be associated with risk of BE and EAC, respectively, but replication data with larger sample sizes are needed

Metformin plus megestrol acetate compared with megestrol acetate alone as fertility‐sparing treatment in patients with atypical endometrial hyperplasia and well‐differentiated endometrial cancer: a randomised controlled trial

Objective: To assess the efficacy of metformin in megestrol acetate (MA)-based fertility-sparing treatment for patients with atypical endometrial hyperplasia (AEH) and endometrioid endometrial cancer (EEC). Design: A randomised, single-centre, open-label, controlled trial conducted between October 2013 and December 2017. Setting: Shanghai OBGYN Hospital of Fudan University, China. Population: A total of 150 patients (18-45 years old) with primary AEH or well-differentiated EEC were randomised into an MA group (n = 74) and an MA plus metformin group (n = 76). Methods: Patients with AEH or EEC were firstly stratified, then randomised to receive MA (160 mg orally, daily) or MA (160 mg orally, daily) plus metformin (500 mg orally, three times a day). Main outcomes and measures: The primary efficacy parameter was the cumulate complete response (CR) rate within 16 weeks of treatment (16w-CR rate); the secondary efficacy parameters were 30w-CR rate and adverse events. Results: The 16w-CR rate was higher in the metformin plus MA group than in the MA-only group (34.3 versus 20.7%, odds ratio [OR] 2.0, 95% confidence interval [CI] 0.89-4.51, P = 0.09) but the difference was more significant in 102 AEH patients (39.6 versus 20.4%, OR 2.56, 95% CI 1.06-6.21, P = 0.04). This effect of metformin was also significant in non-obese (51.4 versus 24.3%, OR 3.28, 95% CI 1.22-8.84, P = 0.02) and insulin-sensitive (54.8 versus 28.6%, OR 3.04, 95% CI 1.03-8.97, P = 0.04) subgroups of AEH women. No significant result was found in secondary endpoints. Conclusion: As a fertility-sparing treatment, metformin plus MA was associated with a higher early CR rate compared with MA alone in AEH patients. Tweetable abstract For AEH patients, metformin plus MA might be a better fertility-sparing treatment to achieve a higher early CR rate compared with MA alone