Seasonal total methane depletion in limestone caves

Methane concentration in caves is commonly much lower than the external atmosphere, yet the cave CH4 depletion causal mechanism is contested and dynamic links to external diurnal and seasonal temperature cycles unknown. Here, we report a continuous 3-year record of cave methane and other trace gases in Jenolan Caves, Australia which shows a seasonal cycle of extreme CH4 depletion, from ambient ∼1,775 ppb to near zero during summer and to ∼800 ppb in winter. Methanotrophic bacteria, some newly-discovered, rapidly consume methane on cave surfaces and in external karst soils with lifetimes in the cave of a few hours. Extreme bacterial selection due to the absence of alternate carbon sources for growth in the cave environment has resulted in an extremely high proportion 2-12% of methanotrophs in the total bacteria present. Unexpected seasonal bias in our cave CH4 depletion record is explained by a three-step process involving methanotrophy in aerobic karst soil above the cave, summer transport of soil-gas into the cave through epikarst, followed by further cave CH4 depletion. Disentangling cause and effect of cave gas variations by tracing sources and sinks has identified seasonal speleothem growth bias, with implied palaeo-climate record bias

Market-based Multirobot Coordination Using Task Abstraction

In this paper, we introduce a novel approach to multirobot coordination that works by simultaneously distributing task allocation, mission planning, and execution among members of a robot team. By combining traditional hierarchical task decomposition techniques with recent developments in market-based multirobot control, we obtain an efficient and robust distributed system capable of solving complex problems. Essentially, we have extended the TraderBots market-based architecture to include a mechanism that distributes tasks among robots at multiple levels of abstractions, represented as task trees. Results are presented for a simulated area reconnaissance scenario

A Survey and Analysis

Market-based multirobot coordination approaches have received significant attention and gained considerable popularity within the robotics research community in recent years. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need by providing an introduction to market-based multirobot coordination, a comprehensive review of the state of the art in the field, and a discussion of remaining challenges

Line-based extrinsic calibration of range and image sensors

Creating rich representations of environments requires integration of multiple sensing modalities with complementary characteristics such as range and imaging sensors. To precisely combine multisensory information, the rigid transformation between different sensor coordinate systems (i.e., extrinsic parameters) must be estimated. The majority of existing extrinsic calibration techniques require one or multiple planar calibration patterns (such as checkerboards) to be observed simultaneously from the range and imaging sensors. The main limitation of these approaches is that they require modifying the scene with artificial targets. In this paper, we present a novel algorithm for extrinsically calibrating a range sensor with respect to an image sensor with no requirement of external artificial targets. The proposed method exploits natural linear features in the scene to precisely determine the rigid transformation between the coordinate frames. First, a set of 3D lines (plane intersection and boundary line segments) are extracted from the point cloud, and a set of 2D line segments are extracted from the image. Correspondences between the 3D and 2D line segments are used as inputs to an optimization problem which requires jointly estimating the relative translation and rotation between the coordinate frames. The proposed method is not limited to any particular types or configurations of sensors. To demonstrate robustness, efficiency and generality of the presented algorithm, we include results using various sensor configurations

Automatic segmentation of 3D laser point clouds by ellipsoidal region growing

We present and evaluate two variants of an algorithm for simultaneously segmenting and modeling a mixed-density unstructured 3D point cloud by ellipsoidal (Gaussian) region growing. The base algorithm merges initial ellipsoids into larger ellipsoidal segments with a minimum spanning tree algorithm. The variants differ only in the merge criterion used-a threshold on a generalised distance measure defined on the merge candidates. The first variant (shape-distance) considers the relative shape, orientation and position of the ellipsoids, and can grow regions across missing or sparse data, whilst the second (density-distance) attempts to maintain a good fit to the data by setting a minimum sample density threshold on the merged ellipsoid. Adjusting the threshold in each case changes the quality and degree of segmentation achieved. The threshold parameter is tuned by minimising Akaike's Information Criterion (AIC) with respect to the threshold value. Experiments show that thresholds selected in this way lead to low complexity models and are stable across different environments. The shape-distance measure segments large-scale structures more readily than the density-distance measure, but leads to higher AIC scores, and higher model complexity

Robust Multirobot Coordination in Dynamic Environments

Robustness is crucial for any robot team, especially when operating in dynamic environments. The physicality of robotic systems and their interactions with the environment make them highly prone to malfunctions of many kinds. Three principal categories in the possible space of robot malfunctions are communication failures, partial failure of robot resources necessary for task execution (or partial robot malfunction), and complete robot failure (or robot death). This paper addresses these three categories and explores means by which the TraderBots approach ensures robustness and promotes graceful degradation in team performance when faced with malfunctions