This paper proposes an analytical framework for modelling resource contention in multi-robot systems, where the travel times and task durations are uncertain. It uses several approximation methods to quickly and accurately calculate the probability distributions describing the times at which the tasks start and finish. Specific contributions include exact and fast approximation methods for calculating the probability of a set of independent normally distributed random events occurring in a given order, a method for calculating the most likely and n-th most likely orders of occurrence for a set of independent normally distributed random events that have equal standard deviations, and a method for approximating the conditional probability distributions of the events given a specific order of the events. The complete framework is shown to be faster than a Monte Carlo approach for the same accuracy in two multi-robot task allocation problems. In addition, the importance of incorporating uncertainty is demonstrated through a comparison with a deterministic method. This is a general framework that is agnostic to the optimisation method and objective function used, and is applicable to a wide range of problems.

Outdoor robotic systems rely on perception modules that must be robust to variations in environmental conditions. In particular, vision-based perception systems are affected by illumination variations caused by occlusions. We propose an approach to calculate the lighting distribution of outdoor scenes. The new approach enables us to compensate for shadows and therefore obtain images which are invariant to the sun position and scene geometry, while also retaining the dimensionality of the original data. The method combines images with geometric information provided by range sensors to infer shadows. We select a pair of points on a shadow boundary from a single material and estimate a terrestrial sunlight-skylight ratio. Individual scaling factors are then calculated for all points based on their orientation and incident illumination sources. The result is a coloured point cloud that is independent of illumination variation due to occlusions and geometry. To demonstrate the effectiveness and generalisation of the approach, we present evaluations using two datasets with different cameras. The first uses a hyperspectral sensor that allows us to analyse the results for a large number of wavelengths, while the second dataset uses a standard RGB camera. The approach is shown to consistently provide good illumination compensation in both scenarios.

Robots have a finite supply of resources such as fuel, battery charge, and storage space. The aim of the Stochastic Collection and Replenishment (SCAR) scenario is to use dedicated agents to refuel, recharge, or otherwise replenish robots in the field to facilitate persistent autonomy. This paper explores the optimisation of the SCAR scenario with a single replenishment agent, using several different objective functions. The problem is framed as a combinatorial optimisation problem, and A* is used to find the optimal schedule. Through a computational study, a ratio objective function is shown to have superior performance compared with a total weighted tardiness objective function, with a greater performance advantage present when using shorter schedule lengths. The importance of incorporating uncertainty in the objective function used in the optimisation process is also highlighted, in particular for scenarios in which the replenishment agent is under- or fully-utilised.

This paper addresses the problem of ensuring mobile robot motion safety when reacting to soft and hard hazards in a static environment. The work is aimed at off-road navigation for mobile ground robots where soft hazards are posed by varying terrain conditions (e.g. deformable soil, slopes, vegetation). Soft hazards pose operating constraints (i.e. speed limits) to the mobile robot that need to be satisfied to ensure motion safety. This paper presents a new morphological erosion operator that generalizes binary obstacle growing to mobility space (the space of speed limits) to deal with both hard and soft hazards seamlessly. This ensures that topological constraints due to vehicle size as well as momentum are taken into account, and leads to a straight-forward approach to generalize the concept of `regions of inevitable collision' for soft hazards.

Operating in vegetated environments is a major challenge for autonomous robots. Obstacle detection based only on geometric features causes the robot to consider foliage, for example, small grass tussocks that could be easily driven through, as obstacles. Classifying vegetation does not solve this problem since there might be an obstacle hidden behind the vegetation. In addition, dense vegetation typically needs to be considered as an obstacle. This paper addresses this problem by augmenting probabilistic traversability map constructed from laser data with ultra-wideband radar measurements. An adaptive detection threshold and a probabilistic sensor model are developed to convert the radar data to occupancy probabilities. The resulting map captures the fine resolution of the laser map but clears areas from the traversability map that are induced by obstacle-free foliage. Experimental results validate that this method is able to improve the accuracy of traversability maps in vegetated environments.

This paper introduces two objective functions for computing the expected cost in the Stochastic Collection and Replenishment (SCAR) scenario. In the SCAR scenario, multiple user agents have a limited supply of a resource that they either use or collect, depending on the scenario. To enable persistent autonomy, dedicated replenishment agents travel to the user agents and replenish or collect their supply of the resource, thus allowing them to operate indefinitely in the field. Of the two objective functions, one uses a Monte Carlo method, while the other uses a significantly faster analytical method. Approximations to multiplication, division and inversion of Gaussian distributed variables are used to facilitate propagation of probability distributions in the analytical method when Gaussian distributed parameters are used. The analytical objective function is shown to have greater than 99% comparison accuracy when compared with the Monte Carlo objective function while achieving speed gains of several orders of magnitude.

Automated rock recognition is a key step for building a fully autonomous mine. When characterizing rock types from drill performance data, the main challenge is that there is not an obvious one-to-one correspondence between the two. In this paper, a hybrid rock recognition approach is proposed which combines Gaussian Process (GP) regression with clustering. Drill performance data is also known as Measurement While Drilling (MWD) data and a rock hardness measure - Adjusted Penetration Rate (APR) is extracted using the raw data in discrete drill holes. GP regression is then applied to create a more dense APR distribution, followed by clustering which produces discrete class labels. No initial labelling is needed. Comparisons are made with alternative measures of rock hardness from MWD data as well as state-of-the-art GP classification. Experimental results from an actual mine site show the effectiveness of our proposed approach.

This paper introduces the Time-Optimal Planning and Execution (TOPE) problem, in which the aim is to minimise the total planning and execution time required to achieve a goal. The TOPE process is derived and shown to be capable of solving this problem in dynamic state spaces, by continuously calculating the optimum value of any system parameters that can affect this total time. Procedures are presented to apply this process to an existing replanning system, and to determine its required accuracy and timeliness. It is shown that the TOPE process can yield lower total times than other planning systems if these requirements are met.

The design of a hierarchical planning system in which each level operates in parallel and communicates asynchronously is presented. It is shown that this parallel hierarchical replanner is both reactive, and as close to optimal over all information in the state space as is possible given finite computational power. A comparison with three other hierarchical methods is presented, which demonstrates that for scenarios in which the time taken to achieve a mission goal is of greater importance than the cost incurred, this approach has better performance than related methods in the literature.

This work aims to promote reliability and integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicle (UGV) autonomy. For this purpose, a comprehensive UGV system, comprising many different exteroceptive and proprioceptive sensors has been built. The first contribution of this work is a large, accurately calibrated and synchronised, multi-modal data-set, gathered in controlled environmental conditions, including the presence of dust, smoke and rain. The data have then been used to analyse the effects of such challenging conditions on perception and to identify common perceptual failures. The second contribution is a presentation of methods for mitigating these failures to promote perceptual integrity in adverse environmental conditions.

This paper describes a new methodology for calculating the translational and rotational offsets of a range sensor to a reference coordinate frame on the platform to which it is affixed. The technique consists of observing an environment of known or partially known geometry, from which the offsets are determined by minimizing the error between the sensed data and the known structure. Analytic results are presented which derive the necessary conditions for a successful optimisation. Practical results confirm the analysis and show that it is possible to obtain more precise results than those obtained through hand measurement.

This paper presents the application of a millimetre wave radar technology to environment mapping in surface mining. Sensor requirements for ranging and surface profiling in shovel and dragline operations were determined based on machines performance requirements. Frequency modulated continuous wave (FMCW) technique was selected to achieve the correct range resolution while fast Fourier techniques (FFT) was used to extract the range data from the radar output. Radar data processing was undertaken using stochastic environment representation technique in real-time. The results of field trials show successful radar performance in terms of the system's accurate measurement of excavation terrain, real-time imaging, robustness, reliability and penetration through dust and water

This paper presents two approaches for the problem of multiple target tracking (MTT) and specifically people tracking. Both filters are based on sequential Monte Carlo methods (SMCM) and joint probability data association (JPDA). The filters have been implemented and tested on real data from a laser measurement system. Experiments show that both approaches are able to track multiple moving persons. A comparison of both filters is given and the advantages and disadvantages of the two approaches are presented.

This paper presents an overview of the work being conducted by the Australian Centre for Field Robotics (ACFR) and the CRC for mining technology and equipment (CMTE) into sensors and systems for real-time environment imaging. Radar is perhaps the only sensor capable of performing imaging tasks in environment where high levels of dust, water vapour, and rain must be contended with. Methods of visualizing the radar data for various applications are presented.

Three dimensional terrain maps are useful representations of environments for various robotic applications. Unfortunately, sensor data (from which such maps are built) is uncertain and contains errors which are usually not accounted for in existing terrain building algorithms. In real-time applications, it is necessary to quantify these uncertainties to allow map construction decisions to be made online. This paper addresses this issue by providing a representation that explicitly accounts for sensing uncertainty. This is achieved through the use of stochastic simulation techniques. The result is in an algorithm for online 3D multiresolution surface reconstruction of unknown, and unstructured environments. Results of the surface reconstruction algorithm in a real environment are presented.

Describes the theoretical development and experimental evaluation of a navigation system for an autonomous load, haul, and dump truck based on the results obtained during extensive in-situ field trials. The particular contributions of the theoretical work are in designing the navigation system to be able to cope with vehicle slip in rough uneven terrain using information from inertial sensors, odometry, and a bearing only laser. Results are presented using data obtained during the field trials.

This paper provides an analysis of Kalman filter based systems with respect to fault detection. By using frequency domain techniques, a metric is developed that describes the detectability of a fault by showing how a fault is transmitted to the filter innovations (if at all). Through experiment, it is shown that redundancy must be employed for guaranteed detection of faults, and that unlike sensors should be used. Further, it is shown that modelling errors can be treated within the same framework as "hard" actuator or sensor faults.

Position information obtained from standard GPS receivers is known to be corrupted with coloured (time-correlated) noise. To make effective use of GPS information in a navigation system it is essential to model this coloured noise and to incorporate additional sensing to de-correlate and eliminate its effect. In this paper frequency domain techniques are employed to generate a model for GPS noise sources. This model shows clearly what type and combination of additional sensor information is necessary to de-correlate GPS errors and to make best use of position information in navigation tasks. The frequency-domain methodology proposed has wider application in the design of sensor suites for high-performance navigation systems. Experimental results are presented demonstrating the method in fusing standard GPS latitude and longitude information with information from a velocity sensor.

This paper describes the theoretical development and experimental evaluation of a guidance system for an autonomous load, haul and dump truck (LHD) for use in underground mining. The particular contributions of this paper are in designing the navigation system to be able to cope with vehicle slip in rough uneven terrain using information from an inertial navigation system (INS) and a bearing only laser. Results are presented using data obtained during field trials.

This paper presents the results of an experimental program for evaluating sensors and sensing technologies in an underground mining applications. The objective of the experiments is to infer what combinations of sensors will provide reliable navigation systems for autonomous vehicles operating in a harsh underground environment. Results from a wide range of sensors are presented and analysed. A conclusion as to the best combination of sensors is drawn.