The potential benefits that automation could bring to a wide variety of real-world tasks are numerous and well recognised. There has been significant research undertaken into automation in general, but for real-time automation of complex systems (involving complex geometries and dynamics) the problem is far from a solved one. One of the key tasks in a surface mining operation is that of using shovels or excavators to load material onto haul trucks for transportation. Since it is such a crucial task to a number of production cycles, it is a clear area where the productivity and safety benefits of automation could have a large impact. A number of projects are being undertaken concurrently to move towards first partial, and then full, automation of this mining subsystem. This thesis focusses on the collision avoidance problem, specifically on forming a collision hull that distinguishes between intersecting and non-intersecting configurations of two objects. Techniques from computer graphics are leveraged to develop a data structure that stores and organises relevant information about real-world systems for motion-planning tasks, ensuring that the necessary data is available and in a form suited to the task at hand. The Minkowski Sum operation, which can be used fairly directly to form the collision hull of two convex objects under translation, is extended to develop an operation to form the exact collision hull of two arbitrary objects to determine the applicability of such a scheme to complex systems in real-time. A level of detail solution is then proposed, where the Minkowski Hull of bounding hierarchies allows unnecessary parts of the hull to be calculated only in a coarse manner, thus offsetting a lot of the computational cost for any given test. This approach is investigated for both translational motion and joint-space motion. Collision detection is not collision avoidance, and so the algorithms developed in the thesis are tested in a number of applications, to demonstrate their suitability to the collision avoidance task. The applications (discrete collision prediction, visibility graph path planning, and the formulation of a Model Predictive Controller) are restricted versions of the true problems with some simplifying assumptions, but they show the algorithms to be capable both in their execution speed and the information that they provide.

Development drilling is used in underground mining to create access tunnels. A common method involves using a drilling rig, known as a jumbo, to drill holes into the face of a tunnel. Jumbo drill rigs have two or more articulated arms with drills as end-effectors, that extend outwards from a vehicle. Once drilled, the holes are charged with explosives and fired to advance the tunnel. There is an ongoing imperative within the mining industry to reduce development times and reducing time spent drilling is seen as the best opportunity for achieving this. Notwithstanding that three-boom jumbos have been available for some years, the industry has maintained a preference for using jumbo rigs with two drilling booms. Three-boom machines have the potential to reduce drilling time by as much as one third, but they have proven difficult to operate and, in practice, this benefit has not been realized. The key difficulty lies in manoeuvering the booms within the tight confines of the tunnel and ensuring sequencing the drilling of holes so that each boom spends maximum time drilling. This thesis addresses the problem of optimally sequencing multi-boom jumbo drill rigs to minimize the overall time to drill a blast hole pattern, taking into account the various constraints on the problem including the geometric constraints restricting motion of the booms. The specific aims of the thesis are to: ² develop the algorithmic machinery needed to determine minimum- or near-minimum-time drill assignment for multi-boom jumbos which is suitable for "real-time" implementation; ² use this drill pattern assignment algorithm to quantify the benefits of optimal drill pattern assignment with three-boom jumbos; and ² investigate the management of unplanned events, such as boom breakdowns, and assess the potential of the algorithm to assist a human operator with the forward planning of drill-hole selection. Jumbo drill task assignment is a combinatorial optimization problem. A methodology based around receding horizon mixed integer programming is developed to solve the problem. At any time the set of drill-holes available to a boom is restricted by the location of the other booms as well as the tunnel perimeter. Importantly these constraints change as the problem evolves. The methodology builds these constraints into problem through use of a feasibility tensor that encodes the moves available to each boom given configurations of other booms. The feasibility tensor is constructed off-line using a rapidly exploring random tree algorithm. Simulations conducted using the sequencing algorithm predict, for a standard drill-hole pattern, a 10 - 22% reduction in drilling time with the three-boom rig relative to two-boom machines. The algorithms developed in this thesis have two intended applications. The first is for automated jumbo drill rigs where the capability to plan drilling sequences algorithmically is a prerequisite. Automated drill rigs are still some years from being a reality. The second, and more immediate application is in providing decision support for drill rig operators. It is envisaged that the algorithms described here might form the basis of a operator assist that provides guidance on which holes to drill next with each boom, adapting this plan as circumstances change.

The research introduces the fourth industrial revolution philosophically, exploring the application of innovation and automation in broad terms and the Internet of Things (IoT) specifically within the mining industry. It explains the business and societal motivation for such interventions, highlighting some of the key benefits. It further explores the inadvertent risks, some of which have already manifested in mining applications and others which can be inferred from other industrial and social applications. A critical analysis is conducted of the application of the South African Mine Health and Safety Act and Regulations on such applications in the mining environment, as well as considering key other pieces of South African legislation. A comparative analysis with Australian legislation confirms that Western Australia has recognised the need for regulation and have started regulating, primarily mining automation, at least. Through these analyses it is established that a legislative vacuum exists, despite the general application of many requirements in relation to safety considerations during the utilisation of IoT applications. The paper concludes by recommending collaboration between the Department of Mineral Resources and the Chamber of Mines to seek ways to lead legislative and regulatory developments in this space in order to enable the sustainability of the South African mining industry. In particular, the research suggests the emphasis should be to legally encourage and permit the implementation of IoT solutions in the mining industry in as many instances as reasonably possible, whilst consecutively addressing the new and emerging risks created through such. / Mini Dissertation (LLM)--University of Pretoria, 2016. / Public Law / LLM / Unrestricted