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.

Modern air battles are very dynamic and fast, and put extreme pressure on pilots. In some unpredictable situations, like new discovered threats or mission plan deviation because of enemy aircraft, the pilots might need to replan their predefined flight route. This is very difficult, if not impossible, to do since numerous factors affect it. A system that can help the pilots to do such a thing is needed. P revious work in this field has involved methods from artificial intelligence like A*-search. In this master thesis, implementation of a replanning system based on a control theory method, Model Predictive Control (MPC), is examined. Different factors influencing the path, such as terrain and threats, are included in the algorithm. The results presented in this thesis show that MPC solves the problem. As with every method there are some drawbacks and advantages, but as a summary the method is a very promising one and is worth further development. Proposals of future work and different improvements of the algorithms used here are presented in this report as well.

<p>Modern air battles are very dynamic and fast, and put extreme pressure on pilots. In some unpredictable situations, like new discovered threats or mission plan deviation because of enemy aircraft, the pilots might need to replan their predefined flight route. This is very difficult, if not impossible, to do since numerous factors affect it. A system that can help the pilots to do such a thing is needed. P</p><p>revious work in this field has involved methods from artificial intelligence like A*-search. In this master thesis, implementation of a replanning system based on a control theory method, Model Predictive Control (MPC), is examined. Different factors influencing the path, such as terrain and threats, are included in the algorithm. </p><p>The results presented in this thesis show that MPC solves the problem. As with every method there are some drawbacks and advantages, but as a summary the method is a very promising one and is worth further development. </p><p>Proposals of future work and different improvements of the algorithms used here are presented in this report as well.</p>

abstract: As robots become mechanically more capable, they are going to be more and more integrated into our daily lives. Over time, human’s expectation of what the robot capabilities are is getting higher. Therefore, it can be conjectured that often robots will not act as human commanders intended them to do. That is, the users of the robots may have a different point of view from the one the robots do. The first part of this dissertation covers methods that resolve some instances of this mismatch when the mission requirements are expressed in Linear Temporal Logic (LTL) for handling coverage, sequencing, conditions and avoidance. That is, the following general questions are addressed: * What cause of the given mission is unrealizable? * Is there any other feasible mission that is close to the given one? In order to answer these questions, the LTL Revision Problem is applied and it is formulated as a graph search problem. It is shown that in general the problem is NP-Complete. Hence, it is proved that the heuristic algorihtm has 2-approximation bound in some cases. This problem, then, is extended to two different versions: one is for the weighted transition system and another is for the specification under quantitative preference. Next, a follow up question is addressed: * How can an LTL specified mission be scaled up to multiple robots operating in confined environments? The Cooperative Multi-agent Planning Problem is addressed by borrowing a technique from cooperative pathfinding problems in discrete grid environments. Since centralized planning for multi-robot systems is computationally challenging and easily results in state space explosion, a distributed planning approach is provided through agent coupling and de-coupling. In addition, in order to make such robot missions work in the real world, robots should take actions in the continuous physical world. Hence, in the second part of this thesis, the resulting motion planning problems is addressed for non-holonomic robots. That is, it is devoted to autonomous vehicles’ motion planning in challenging environments such as rural, semi-structured roads. This planning problem is solved with an on-the-fly hierarchical approach, using a pre-computed lattice planner. It is also proved that the proposed algorithm guarantees resolution-completeness in such demanding environments. Finally, possible extensions are discussed. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

<p> </p> <p>This work proposes a proof of concept along with a prototype of a multi-agent autonomous drone system that can be used to detect, and capture a intruding adversarial drone. The functional Counter Unmanned Aerial System (CUAS) prototype is used to convey the feasibility of a persistent multi-agent aerial protection and apprehension system by demonstrating important features of the mission through both simulation and field testing.<br> </p> <p> </p>

International Telemetering Conference Proceedings / October 29-November 02, 1990 / Riviera Hotel and Convention Center, Las Vegas, Nevada / TASC is currently developing for the GPS Range Applications Joint Program Office (RAJPO) the mission planner which will be used by test ranges procuring RAJPOdeveloped GPS test range instrumentation. Test Range User Mission Planner (TRUMP) is a user-friendly, PC-resident tool which aids in deploying and utilizing GPS-based test range assets. In addition to providing satellite/jammer visibility (for a Digital Terrain Elevation Data (DTED) range map) and dilution-of-precision (DOP) information, TRUMP features: C Time history plots of time-space-position information (TSPI) C Performance based on a dynamic GPS/inertial system simulation C Time history plots of TSPI data link connectivity C DTED maps with user-defined cultural features C Two-dimensional coverage plots of ground-based test range assets. This paper will discuss TRUMP’s role on the test ranges and its current features. In addition, the functionality to be added during the next development phase will be presented.

Geospatial data almost always contains some amount of uncertainty due to inaccuracies in its acquisition and transformation. While the data is commonly visualized (e.g. on digital maps), there are unanswered needs for visualizing uncertainty along with it. Most research on effectively doing this addresses uncertainty in data values at geospatial positions, e.g. water depth, human population, or land-cover classification. Uncertainty in the data’s geospatial positions themselves (positional uncertainty) has not been previously focused on in this regard. In this thesis, techniques were created for visualizing positional uncertainty using World Vector Shoreline as an example dataset. The techniques consist of a shoreline buffer zone to which visual effects such as gradients, transparency, and randomized dots were applied. They are viewed interactively via Web Map Service (WMS). In clutter testing with human subjects, a transparency-gradient technique performed the best, followed by a solid-fill technique, with a dots-density-gradient technique performing worst.

MATS Satellite

Approved for public release; distribution is unlimited / Unmanned vehicle technology has matured significantly over the last two decades. This is evidenced by its widespread use in industrial and military applications ranging from deep-ocean exploration to anti-submarine warefare. Indeed, the feasiblity of short-range, special-purpose vehicles (whether aunonomous or remotely operated) is no longer in question. The research efforts have now begun to shift their focus on development of reliable, longer-range, high-endurance and fully autonomous systems. One of the major underlying technologies required to realize this goal is Artificial Intelligence (AI). The latter offers great potential to endow vehicles with the intelligence needed for full autonomy and extended range capability; this involves the increased application of AI technologies to support mission planning and execution, navigation and contingency planning. This thesis addresses two issues associated with the above goal for Autonomous Underwater Vehicles (AUV's). Firstly, a new approach is proposed for path planning in underwater environments that is capable of dealing with uncharted obstacles and which requires significantly less planning time and computer memory. Secondly, it explores the use of expert system technology in the planning of AUV missions.

Detta arbete visar, delvis var för sig och delvis i kombination, hur regelbaserat och fallbaserat beslutsstöd kan användas vid uppdragsplanering. Uppdragsplanering utförs med hjälp av Mission Support System (MSS). Uppdragsplanering kräver en hel del arbete och en hel del erfarenhet för att den ska bli bra. Detta kan underlättas om man kan få hjälp av olika verktyg som kan dra nytta av uppsatta regler för respektive uppdrag och även dra nytta av tidigare uppdrag av samma karaktär. Sedan tidigare finns det två olika examensarbete som har undersökt respektive del av detta. Målet med detta arbete är delvis att demonstrera metoderna i en prototyp, var för sig och i kombination med varandra, och delvis att försöka besvara frågan om en kombination av metoderna presterar ett bättre beslutsstöd än när metoderna används var för sig. Detta arbete bygger på två tidigare examensarbeten. Metoden som används för att kunna bedöma vilken metod som är att föredra är att man har implementerat både verktygen i en prototyp. I prototypen planerar man sedan ett antal uppdrag och applicerar sedan dem olika metoderna var för sig och även i kombination och utvärderar resultatet. Resultatet pekar på att det ur planeringssynpunkt bör användas en kombination av de två presenterade metoderna. Däremot om man tar in tidsaspekten, så är den erfarenhetsbaserade metoden inte att rekommendera i det utförande som den är i nu. Detta eftersom den tar lång tid att applicera. Tidsåtgången är uppemot 12 timmar. Vilket inte fungerar i verkligheten. / This thesis shows, partly alone, partly in combination, how rule based and case based decision support can be used in mission planning. For mission planning Mission Support System (MSS) is used. Mission planning requires a lot of effort and experience to make a good plan. This can be facilitated if there are tools that can benefit from rules for actual or previously missions of the same character. Thera are two theses that have investigated these different aspects. The goal with this thesis is to partly demonstrate these methods in a prototype, alone and in combination, and partly try to answer the question if a combination of the methods is performing a better decision support than each of them alone. This thesis is based on two previously thesis. The method that is used to to be able to assess which method is preferable is to implement the both tools in a prototype. The prototype is then used to plan a few missions and applying the different methods alone and in combination and evaluate the result. The results indicate that from a planning point, a combination of the two methods should be used. However if one takes the time in consideration, then the case based method is not to recomend in its current status. This because the execution time is long. The execution time is up to 12 hours. Which does not work in reality.