Automated control of a dragline using machine vision

The research described in this paper is directed toward increasing productivity of draglines through automation. In particular, it focuses on the swing-to-dump, dump, and return-to-dig phases of the dragline operational cycle by developing a swing automation system. In typical operation the dragline boom can be in motion for up to 80% of the total cycle time. This provides considerable scope for improving cycle time through automated or partially automated boom motion control. This paper describes machine vision based sensor technology and control algorithms under development to solve the problem of continuous real time bucket location and control. Incorporation of this capability into existing dragline control systems will then enable true automation of dragline swing and dump operations.

Persistent navigation and mapping using a biologically inspired SLAM system

The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.

Effect of representation size and visual ambiguity on RatSLAM system performance

RatSLAM is a vision-based SLAM system based on extended models of the rodent hippocampus. RatSLAM creates environment representations that can be processed by the experience mapping algorithm to produce maps suitable for goal recall. The experience mapping algorithm also allows RatSLAM to map environments many times larger than could be achieved with a one to one correspondence between the map and environment, by reusing the RatSLAM maps to represent multiple sections of the environment. This paper describes experiments investigating the effects of the environment-representation size ratio and visual ambiguity on mapping and goal navigation performance. The experiments demonstrate that system performance is weakly dependent on either parameter in isolation, but strongly dependent on their joint values.

The implementation of a novel, bio-inspired, robotic security system

The implementation of a robotic security solution generally requires one algorithm to route the robot around the environment and another algorithm to perform anomaly detection. Solutions to the routing problem require the robot to have a good estimate of its own pose. We present a novel security system that uses metrics generated by the localisation algorithm to perform adaptive anomaly detection. The localisation algorithm is a vision-based SLAM solution called RatSLAM, based on mechanisms within the hippocampus. The anomaly detection algorithm is based on the mechanisms used by the immune system to identify threats to the body. The system is explored using data gathered within an unmodified office environment. It is shown that the algorithm successfully reacts to the presence of people and objects in areas where they are not usually present and is tolerised against the presence of people in environments that are usually dynamic.

Single camera vision-only SLAM on a suburban road network

Simultaneous Localization And Mapping (SLAM) is one of the major challenges in mobile robotics. Probabilistic techniques using high-end range finding devices are well established in the field, but recent work has investigated vision only approaches. This paper presents a method for generating approximate rotational and translation velocity information from a single vehicle-mounted consumer camera, without the computationally expensive process of tracking landmarks. The method is tested by employing it to provide the odometric and visual information for the RatSLAM system while mapping a complex suburban road network. RatSLAM generates a coherent map of the environment during an 18 km long trip through suburban traffic at speeds of up to 60 km/hr. This result demonstrates the potential of ground based vision-only SLAM using low cost sensing and computational hardware.

A Vision for saving the reef — the Starbug AUV

Starbug is an inexpensive, miniature autonomous underwater vehicle ideal for data collection and ecosystem surveys. Starbug is small enough to be launched by one person without the need for specialised equipment, such as cranes, and it operates with minimal to no human intervention. Starbug was one of the first autonomous underwater vehicles (AUVs) in the world where vision is the primary means of navigation and control. More details of Starbug can be found here: http://www.csiro.au/science/starbug.html

The Machine Vision Toolbox : A MATLAB Toolbox for vision and vision-based control

The article described an open-source toolbox for machine vision called Machine Vision Toolbox (MVT). MVT includes more than 60 functions including image file reading and writing, acquisition, display, filtering, blob, point and line feature extraction, mathematical morphology, homographies, visual Jacobians, camera calibration, and color space conversion. MVT can be used for research into machine vision but is also versatile enough to be usable for real-time work and even control. MVT, combined with MATLAB and a model workstation computer, is a useful and convenient environment for the investigation of machine vision algorithms. The article illustrated the use of a subset of toolbox functions for some typical problems and described MVT operations including the simulation of a complete image-based visual servo system.

To err (on the road) is human? An on-road study of driver errors

Human error, its causes and consequences, and the ways in which it can be prevented, remain of great interest to road safety practitioners. This paper presents the findings derived from an on-road study of driver errors in which 25 participants drove a pre-determined route using MUARC's On-Road Test Vehicle (ORTeV). In-vehicle observers recorded the different errors made, and a range of other data was collected, including driver verbal protocols, forward, cockpit and driver video, and vehicle data (speed, braking, steering wheel angle, lane tracking etc). Participants also completed a post trial cognitive task analysis interview. The drivers tested made a range of different errors, with speeding violations, both intentional and unintentional, being the most common. Further more detailed analysis of a sub-set of specific error types indicates that driver errors have various causes, including failures in the wider road 'system' such as poor roadway design, infrastructure failures and unclear road rules. In closing, a range of potential error prevention strategies, including intelligent speed adaptation and road infrastructure design, are discussed.

Developing a hybrid expert system program to aid in the design of plastic injection molding process

Experts in injection molding often refer to previous solutions to find a mold design similar to the current mold and use previous successful molding process parameters with intuitive adjustment and modification as a start for the new molding application. This approach saves a substantial amount of time and cost in experimental based corrective actions which are required in order to reach optimum molding conditions. A Case-Based Reasoning (CBR) System can perform the same task by retrieving a similar case which is applied to the new case from the case library and uses the modification rules to adapt a solution to the new case. Therefore, a CBR System can simulate human e~pertise in injection molding process design. This research is aimed at developing an interactive Hybrid Expert System to reduce expert dependency needed on the production floor. The Hybrid Expert System (HES) is comprised of CBR, flow analysis, post-processor and trouble shooting systems. The HES can provide the first set of operating parameters in order to achieve moldability condition and producing moldings free of stress cracks and warpage. In this work C++ programming language is used to implement the expert system. The Case-Based Reasoning sub-system is constructed to derive the optimum magnitude of process parameters in the cavity. Toward this end the Flow Analysis sub-system is employed to calculate the pressure drop and temperature difference in the feed system to determine the required magnitude of parameters at the nozzle. The Post-Processor is implemented to convert the molding parameters to machine setting parameters. The parameters designed by HES are implemented using the injection molding machine. In the presence of any molding defect, a trouble shooting subsystem can determine which combination of process parameters must be changed iii during the process to deal with possible variations. Constraints in relation to the application of this HES are as follows. - flow length (L) constraint: 40 mm < L < I 00 mm, - flow thickness (Th) constraint: -flow type: - material types: I mm < Th < 4 mm, unidirectional flow, High Impact Polystyrene (HIPS) and Acrylic. In order to test the HES, experiments were conducted and satisfactory results were obtained.

Human preadipocyte proliferation and differentiation

Obesity represents a major health, social and economic burden to many developing and Westernized communities, with the prevalence increasing at a rate exceeding almost all other medical conditions. Despite major recent advances in our understanding of adipose tissue metabolism and dynamics, we still have limited insight into the regulation of adipose tissue mass in humans. Any significant increase in adipose tissue mass requires proliferation and differentiation of precursor cells (preadipocytes) present in the stromo-vascular compartment of adipose tissue. These processes are very complex and an increasing number of growth factors and hormones have been shown to modulate the expression of genes involved in preadipocyte proliferation and differentiation. A number of transcription factors, including the C/EBP family and PP ARy, have been identified as integral to adipose tissue development and preadipocyte differentiation. Together PP ARy and C/EBPa regulate important events in the activation and maintenance of the terminally differentiated phenotype. The ability of PP ARy to increase transcription through its DNA recognition site is dependent on the binding of ligands. This suggests that an endogenous PP ARy ligand may be an important regulator of adipogenesis. Adipose tissue functions as both the major site of energy storage in the body and as an endocrine organ synthesizing and secreting a number of important molecules involved in regulation of energy balance. For optimum functioning therefore, adipose tissue requires extensive vascularization and previous studies have shown that growth of adipose tissue is preceded by development of a microvascular network. This suggests that paracrine interactions between constituent cells in adipose tissue may be involved in both new capillary formation and fat cell growth. To address this hypothesis the work in this project was aimed at (a) further development of a method for inducing preadipocyte differentiation in subcultured human cells; (b) establishing a method for simultaneous isolation and separate culture of both preadipocytes and microvascular endothelial cells from the same adipose tissue biopsies; (c) to determine, using conditioned medium and co-culture techniques, if endothelial cell-derived factors influence the proliferation and/or differentiation of human preadipocytes; and (d) commence characterization of factors that may be responsible for any observed paracrine effects on aspects of human adipogenesis. Major findings of these studies were as follows: (A) Inclusion of either linoleic acid (a long-chain fatty acid reported to be a naturally occurring ligand for PP ARy) or Rosiglitazone (a member of the thiazolidinedione class of insulin-sensitizing drugs and a synthetic PPARy ligand) in differentiation medium had markedly different effects on preadipocyte differentiation. These studies showed that human preadipocytes have the potential to accumulate triacylglycerol irrespective of their stage of biochemical differentiation, and that thiazolidinediones and fatty acids may exert their adipogenic and lipogenic effects via different biochemical pathways. It was concluded that Rosiglitazone is a more potent inducer of human preadipocyte differentiation than linoleic acid. (B) A method for isolation and culture of both endothelial cells and preadipocytes from the same adipose tissue biopsy was developed. Adipose-derived microvascular endothelial cells were found to produce factor/s, which enhance both proliferation and differentiation of human preadipocytes. (C) The adipogenic effects of microvascular endothelial cells can be mimicked by exposure of preadipocytes to members of the Fibroblast Growth Factor family, specifically ~-ECGF and FGF-1. (D) Co-culture of human preadipocytes with endothelial cells or exposure of preadipocytes to either ~-ECGF or FGF-1 were found to 'prime' human preadipocytes, during their proliferative phase of growth, for thiazolidinedione-induced differentiation. (E) FGF -1 was not found to be acting as a ligand for PP ARy in this system. Findings from this project represent a significant step forward in our understanding of factors involved in growth of human adipose tissue and may lead to the development of therapeutic strategies aimed at modifying the process. Such strategies would have potential clinical utility in the treatment of obesity and obesity related disorders such as Type II Diabetes.