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Introduction: In head and neck cancer the location, size, shape of disease, and normal anatomy change in 6-7 weeks radiotherapy. As a result, steep dose gradients move across target and critical structures resulting in underdose to target and overdose to critical structures. Aim: comparison of target coverage in initial IMRT plan and replan and to quantify dose changes to normal structures in two plans. Methods and Material: 30 patients with locally advanced head and neck cancer patients planned for curative radiotherapy were selected and treated with 3DCRT plan. For dosimetric comparison IMRT plan was created for pre-treatment and repeat CT, which was done after 40Gy. Statistical analysis used: Statistical methods (student’s paired t-test) were applied. Results: Both PTV coverage (V95 from 96.29±1.12 to 97.33±0.80) and dose (D95 from 66.64±0.87 to 67.57±0.74) increased in replanned CT. Both max and mean doses to the brainstem and spinal cord along with mean dose to parotid glands increased in replanned CT. Conclusions: Replanning is necessary during mid-treatment to accommodate anatomical and dosimetric changes during curative radiotherapy.

Introduction: In head and neck cancer the location, size, shape of disease, and normal anatomy change in 6-7 weeks radiotherapy. As a result, steep dose gradients move across target and critical structures resulting in underdose to target and overdose to critical structures. Aim: comparison of target coverage in initial IMRT plan and replan and to quantify dose changes to normal structures in two plans. Methods and Material: 30 patients with locally advanced head and neck cancer patients planned for curative radiotherapy were selected and treated with 3DCRT plan. For dosimetric comparison IMRT plan was created for pre-treatment and repeat CT, which was done after 40Gy. Statistical analysis used: Statistical methods (student’s paired t-test) were applied. Results: Both PTV coverage (V95 from 96.29±1.12 to 97.33±0.80) and dose (D95 from 66.64±0.87 to 67.57±0.74) increased in replanned CT. Both max and mean doses to the brainstem and spinal cord along with mean dose to parotid glands increased in replanned CT. Conclusions: Replanning is necessary during mid-treatment to accommodate anatomical and dosimetric changes during curative radiotherapy.

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An integrated approach in spatial and environmental planning in European countries is one of the leading principles when it comes to development strategies. The approach is embedded in the institutional-organizational models that strive to coordinate multiple roles of planning (economic, environmental and social) and to approach closer to the territorial cohesion, particularly on vulnerable territories such as mountains and nature protected areas. The Alpine region is one of the most comprehensively analysed mountain areas in Europe, including research topics such as mountain resorts in a context of climate change and nature preservation. Following this context, the poster aims to present several tourism resorts positioned in nature protected areas of Serbia. A trajectory of their development through time will be considered in light of their relation to climate change: climate change impact on tourism resorts, but also mitigation and adaptation measures taken in order to decrease development impacts on the environment through replanning activities. It is also expected to accentuate the way planning for mountain areas aims to consolidate conflicts between development and environmental protection/preservation, and how far planers have succeeded in it using legislative and spatial plans as tools. In conclusion, the analysis of few mountain tourism centres of national and international importance (e.g. Kopaonik, Stara Planina, Brezovica) will showcase recommendations for improved balancing between development and preservation according to international norms (e.g. IUCN), understanding on capacities of a particular space and an extent to which the capacities should be used in order to provide not only tourism development but also decent life quality for the local population.

An integrated approach in spatial and environmental planning in European countries is one of the leading principles when it comes to development strategies. The approach is embedded in the institutional-organizational models that strive to coordinate multiple roles of planning (economic, environmental and social) and to approach closer to the territorial cohesion, particularly on vulnerable territories such as mountains and nature protected areas. The Alpine region is one of the most comprehensively analysed mountain areas in Europe, including research topics such as mountain resorts in a context of climate change and nature preservation. Following this context, the poster aims to present several tourism resorts positioned in nature protected areas of Serbia. A trajectory of their development through time will be considered in light of their relation to climate change: climate change impact on tourism resorts, but also mitigation and adaptation measures taken in order to decrease development impacts on the environment through replanning activities. It is also expected to accentuate the way planning for mountain areas aims to consolidate conflicts between development and environmental protection/preservation, and how far planers have succeeded in it using legislative and spatial plans as tools. In conclusion, the analysis of few mountain tourism centres of national and international importance (e.g. Kopaonik, Stara Planina, Brezovica) will showcase recommendations for improved balancing between development and preservation according to international norms (e.g. IUCN), understanding on capacities of a particular space and an extent to which the capacities should be used in order to provide not only tourism development but also decent life quality for the local population.

An integrated approach in spatial and environmental planning in European countries is one of the leading principles when it comes to development strategies. The approach is embedded in the institutional-organizational models that strive to coordinate multiple roles of planning (economic, environmental and social) and to approach closer to the territorial cohesion, particularly on vulnerable territories such as mountains and nature protected areas. The Alpine region is one of the most comprehensively analysed mountain areas in Europe, including research topics such as mountain resorts in a context of climate change and nature preservation. Following this context, the poster aims to present several tourism resorts positioned in nature protected areas of Serbia. A trajectory of their development through time will be considered in light of their relation to climate change: climate change impact on tourism resorts, but also mitigation and adaptation measures taken in order to decrease development impacts on the environment through replanning activities. It is also expected to accentuate the way planning for mountain areas aims to consolidate conflicts between development and environmental protection/preservation, and how far planers have succeeded in it using legislative and spatial plans as tools. In conclusion, the analysis of few mountain tourism centres of national and international importance (e.g. Kopaonik, Stara Planina, Brezovica) will showcase recommendations for improved balancing between development and preservation according to international norms (e.g. IUCN), understanding on capacities of a particular space and an extent to which the capacities should be used in order to provide not only tourism development but also decent life quality for the local population.

An integrated approach in spatial and environmental planning in European countries is one of the leading principles when it comes to development strategies. The approach is embedded in the institutional-organizational models that strive to coordinate multiple roles of planning (economic, environmental and social) and to approach closer to the territorial cohesion, particularly on vulnerable territories such as mountains and nature protected areas. The Alpine region is one of the most comprehensively analysed mountain areas in Europe, including research topics such as mountain resorts in a context of climate change and nature preservation. Following this context, the poster aims to present several tourism resorts positioned in nature protected areas of Serbia. A trajectory of their development through time will be considered in light of their relation to climate change: climate change impact on tourism resorts, but also mitigation and adaptation measures taken in order to decrease development impacts on the environment through replanning activities. It is also expected to accentuate the way planning for mountain areas aims to consolidate conflicts between development and environmental protection/preservation, and how far planers have succeeded in it using legislative and spatial plans as tools. In conclusion, the analysis of few mountain tourism centres of national and international importance (e.g. Kopaonik, Stara Planina, Brezovica) will showcase recommendations for improved balancing between development and preservation according to international norms (e.g. IUCN), understanding on capacities of a particular space and an extent to which the capacities should be used in order to provide not only tourism development but also decent life quality for the local population.

The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents' positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis for e

The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents' positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis for e

Unmanned aerial vehicles (UAVs) frequently operate in partially or entirely unknown environments. As the vehicle traverses the environment and detects new obstacles, rapid path replanning is essential to avoid collisions. This thesis presents a new algorithm called Hierarchical D* Lite (HD*), which combines the incremental algorithm D* Lite with a novel hierarchical path planning approach to replan paths sufficiently fast for real-time operation. Unlike current hierarchical planning algorithms, HD* does not require map corrections before planning a new path. Directional cost scale factors, path smoothing, and Catmull-Rom splines are used to ensure the resulting paths are feasible. HD* sacrifices optimality for real-time performance. Its computation time and path quality are dependent on the map size, obstacle density, sensor range, and any restrictions on planning time. For the most complex scenarios tested, HD* found paths within 10% of optimal in under 35 milliseconds.

Measuring timetable robustness is a complex task. Previous efforts have mainly been focused on simulation studies or measurements of time supplements. However, these measurements don't capture the production flexibility of a timetable, which is essential for measuring the robustness with regard to the trains' commercial activity commitments, and also for merging the goals of robustness and efficiency. In this article we differentiate between production timetables and delivery timetables. A production timetable contains all stops, meetings and switch crossings, while a delivery timetable only contains stops for commercial activities. If a production timetable is constructed such that it can easily be replanned to cope with delays without breaking any commercial activity commitments it provides delivery robustness without compromising travel efficiency. Changing meeting locations is one of the replanning tools available during operation, and this paper presents a new framework for heuristically optimising a given production timetable with regard to the number of alternative meeting locations. Mixed integer programming is used to find two delivery feasible production solutions, one early and one late. The area between the two solutions represents alternative meeting locations and therefore also the replanning enabled robustness. A case study from Sweden demonstrates how the method can be used to develop better production timetables., Tågplan2015

Measuring timetable robustness is a complex task. Previous efforts have mainly been focused on simulation studies or measurements of time supplements. However, these measurements don't capture the production flexibility of a timetable, which is essential for measuring the robustness with regard to the trains' commercial activity commitments, and also for merging the goals of robustness and efficiency. In this article we differentiate between production timetables and delivery timetables. A production timetable contains all stops, meetings and switch crossings, while a delivery timetable only contains stops for commercial activities. If a production timetable is constructed such that it can easily be replanned to cope with delays without breaking any commercial activity commitments it provides delivery robustness without compromising travel efficiency. Changing meeting locations is one of the replanning tools available during operation, and this paper presents a new framework for heuristically optimising a given production timetable with regard to the number of alternative meeting locations. Mixed integer programming is used to find two delivery feasible production solutions, one early and one late. The area between the two solutions represents alternative meeting locations and therefore also the replanning enabled robustness. A case study from Sweden demonstrates how the method can be used to develop better production timetables., Tågplan2015

Measuring timetable robustness is a complex task. Previous efforts have mainly been focused on simulation studies or measurements of time supplements. However, these measurements don't capture the production flexibility of a timetable, which is essential for measuring the robustness with regard to the trains' commercial activity commitments, and also for merging the goals of robustness and efficiency. In this article we differentiate between production timetables and delivery timetables. A production timetable contains all stops, meetings and switch crossings, while a delivery timetable only contains stops for commercial activities. If a production timetable is constructed such that it can easily be replanned to cope with delays without breaking any commercial activity commitments it provides delivery robustness without compromising travel efficiency. Changing meeting locations is one of the replanning tools available during operation, and this paper presents a new framework for heuristically optimising a given production timetable with regard to the number of alternative meeting locations. Mixed integer programming is used to find two delivery feasible production solutions, one early and one late. The area between the two solutions represents alternative meeting locations and therefore also the replanning enabled robustness. A case study from Sweden demonstrates how the method can be used to develop better production timetables., Tågplan2015

This study has investigated novel Automated Red Teaming methods that support replanning. Traditional Automated Red Teaming (ART) approaches usually use evolutionary computing methods for evolving plans using simulations. A drawback of this method is the inability to change a team’s strategy part way through a simulation. This study focussed on a Monte-Carlo Tree Search (MCTS) method in an ART environment that supports re-planning to lead to better strategy decisions and a higher average score

This study has investigated novel Automated Red Teaming methods that support replanning. Traditional Automated Red Teaming (ART) approaches usually use evolutionary computing methods for evolving plans using simulations. A drawback of this method is the inability to change a team’s strategy part way through a simulation. This study focussed on a Monte-Carlo Tree Search (MCTS) method in an ART environment that supports re-planning to lead to better strategy decisions and a higher average score

This study has investigated novel Automated Red Teaming methods that support replanning. Traditional Automated Red Teaming (ART) approaches usually use evolutionary computing methods for evolving plans using simulations. A drawback of this method is the inability to change a team’s strategy part way through a simulation. This study focussed on a Monte-Carlo Tree Search (MCTS) method in an ART environment that supports re-planning to lead to better strategy decisions and a higher average score

This study has investigated novel Automated Red Teaming methods that support replanning. Traditional Automated Red Teaming (ART) approaches usually use evolutionary computing methods for evolving plans using simulations. A drawback of this method is the inability to change a team’s strategy part way through a simulation. This study focussed on a Monte-Carlo Tree Search (MCTS) method in an ART environment that supports re-planning to lead to better strategy decisions and a higher average score

This study has investigated novel Automated Red Teaming methods that support replanning. Traditional Automated Red Teaming (ART) approaches usually use evolutionary computing methods for evolving plans using simulations. A drawback of this method is the inability to change a team’s strategy part way through a simulation. This study focussed on a Monte-Carlo Tree Search (MCTS) method in an ART environment that supports re-planning to lead to better strategy decisions and a higher average score

There are a host of human factors issues involved in operating unmanned aerial vehicles (UAVs). The challenges facing UAV operators are intensified when dynamic mission environments require route replanning on-the-fly, under time pressure. The focus of this work is to characterize the specific 3-D spatial awareness challenges encountered during UAV replanning, in order to design display and automation interventions and assistance to support replanning. To elucidate these 3-D difficulties, we conducted a cognitive task analysis of the replanning problem with the Navy's VC-6 Squadron recently returned from Iraq. Key 3-D spatial challenges involved rationalizing complex combinations of avoiding airspace and terrain regions while simultaneously staying inside or near other airspace, target, and ground regions. Conventional UAV ground control displays did not support the integration or understanding of spatial aspects of the complex information necessary for UAV replanning. Replanning events were operationalized in terms of their 3-D spatial attributes and display requirements, and organized into a taxonomy pairing the events to replanning display interventions. A synthetic task abstraction of the key 3-D spatial replanning challenges was created, and experimentally validated. The display-to-event taxonomy guides future display designs, and experiment data and conventional displays serve as a baseline for future replanning displays., Proceedings of the International Command and Control Research and Technology Symposium (15th) (ICCRTS '10), Santa Monica, CA, June 22-24, 2010. Document includes briefing charts (24 slides, title same as report). U.S. Government or Federal Rights License. The original document contains color images.

[EN] The trajectory generation and its replanning in hostile environments for UAVs (Unmanned Aerial Vehicles) is a discipline in expansion. The hostile environments contains threats, modelled here as radars. Initially a route is planned. Then, if during the fly arise pop-up threats, a replanning is carried out. In both cases the routes are obtained via the A* algorithm. When replanning, the UAV makes a decision about whether to continue the initial plan or to follow the replanned route. The multiattribute decision making theory is a suitable strategy., [ES] La generación de trayectorias y la replanificación de las mismas en entornos hostiles para UAVs (Unmanned Aerial Vehicles) es una disciplina en auge. Los entornos hostiles se caracterizan por la presencia de amenazas, modeladas aquí como radares. Inicialmente se planifica una ruta. Si en vuelo surgen nuevas amenazas, la ruta inicial se replanifica. En ambos casos las rutas se obtienen mediante el algoritmo A*. Ante una replanificación el UAV toma una decisión sobre si continuar por la ruta inicial o por la replanificada. La teoría de la decisión multiatributo proporciona en este caso el mecanismo adecuado.

[EN] The trajectory generation and its replanning in hostile environments for UAVs (Unmanned Aerial Vehicles) is a discipline in expansion. The hostile environments contains threats, modelled here as radars. Initially a route is planned. Then, if during the fly arise pop-up threats, a replanning is carried out. In both cases the routes are obtained via the A* algorithm. When replanning, the UAV makes a decision about whether to continue the initial plan or to follow the replanned route. The multiattribute decision making theory is a suitable strategy., [ES] La generación de trayectorias y la replanificación de las mismas en entornos hostiles para UAVs (Unmanned Aerial Vehicles) es una disciplina en auge. Los entornos hostiles se caracterizan por la presencia de amenazas, modeladas aquí como radares. Inicialmente se planifica una ruta. Si en vuelo surgen nuevas amenazas, la ruta inicial se replanifica. En ambos casos las rutas se obtienen mediante el algoritmo A*. Ante una replanificación el UAV toma una decisión sobre si continuar por la ruta inicial o por la replanificada. La teoría de la decisión multiatributo proporciona en este caso el mecanismo adecuado.

[EN] The trajectory generation and its replanning in hostile environments for UAVs (Unmanned Aerial Vehicles) is a discipline in expansion. The hostile environments contains threats, modelled here as radars. Initially a route is planned. Then, if during the fly arise pop-up threats, a replanning is carried out. In both cases the routes are obtained via the A* algorithm. When replanning, the UAV makes a decision about whether to continue the initial plan or to follow the replanned route. The multiattribute decision making theory is a suitable strategy., [ES] La generación de trayectorias y la replanificación de las mismas en entornos hostiles para UAVs (Unmanned Aerial Vehicles) es una disciplina en auge. Los entornos hostiles se caracterizan por la presencia de amenazas, modeladas aquí como radares. Inicialmente se planifica una ruta. Si en vuelo surgen nuevas amenazas, la ruta inicial se replanifica. En ambos casos las rutas se obtienen mediante el algoritmo A*. Ante una replanificación el UAV toma una decisión sobre si continuar por la ruta inicial o por la replanificada. La teoría de la decisión multiatributo proporciona en este caso el mecanismo adecuado.

[EN] The trajectory generation and its replanning in hostile environments for UAVs (Unmanned Aerial Vehicles) is a discipline in expansion. The hostile environments contains threats, modelled here as radars. Initially a route is planned. Then, if during the fly arise pop-up threats, a replanning is carried out. In both cases the routes are obtained via the A* algorithm. When replanning, the UAV makes a decision about whether to continue the initial plan or to follow the replanned route. The multiattribute decision making theory is a suitable strategy., [ES] La generación de trayectorias y la replanificación de las mismas en entornos hostiles para UAVs (Unmanned Aerial Vehicles) es una disciplina en auge. Los entornos hostiles se caracterizan por la presencia de amenazas, modeladas aquí como radares. Inicialmente se planifica una ruta. Si en vuelo surgen nuevas amenazas, la ruta inicial se replanifica. En ambos casos las rutas se obtienen mediante el algoritmo A*. Ante una replanificación el UAV toma una decisión sobre si continuar por la ruta inicial o por la replanificada. La teoría de la decisión multiatributo proporciona en este caso el mecanismo adecuado.

[EN] The trajectory generation and its replanning in hostile environments for UAVs (Unmanned Aerial Vehicles) is a discipline in expansion. The hostile environments contains threats, modelled here as radars. Initially a route is planned. Then, if during the fly arise pop-up threats, a replanning is carried out. In both cases the routes are obtained via the A* algorithm. When replanning, the UAV makes a decision about whether to continue the initial plan or to follow the replanned route. The multiattribute decision making theory is a suitable strategy., [ES] La generación de trayectorias y la replanificación de las mismas en entornos hostiles para UAVs (Unmanned Aerial Vehicles) es una disciplina en auge. Los entornos hostiles se caracterizan por la presencia de amenazas, modeladas aquí como radares. Inicialmente se planifica una ruta. Si en vuelo surgen nuevas amenazas, la ruta inicial se replanifica. En ambos casos las rutas se obtienen mediante el algoritmo A*. Ante una replanificación el UAV toma una decisión sobre si continuar por la ruta inicial o por la replanificada. La teoría de la decisión multiatributo proporciona en este caso el mecanismo adecuado.

In adapting C2 to the 21st century we plan to conduct a controlled Human-in-the-Loop (HITL) experiment with new Network Centric Warfare (NCW) technology which will be introduced to sixteen experienced warfighters in the form of a collaborative User Defined Operational Picture (UDOP) with Blue Force Readiness and intelligence database access enabled by an IP wide area network as a possible improvement over their use of current baseline technology in the form of the GCCS with Common Operational Picture (COP) capability. We examine here the general methodology of using controlled HITL experiments employing combat scenarios as an Operational Assessment for testing and evolving more effective C2 technology for the warfighter. (See the Award-winning TTCP GUIDEx, 2006.) Our general theoretical hypothesis views the warfighting team as a Complex Adaptive System (CAS), and asserts that a team's use of shared informational schema contributes to their shared mental models resulting in increased combat effectiveness in the battlespace.(See Gell-Mann, 1994,1997.) In particular, we hypothesize that the results of this experiment will show significant improvements on the NCW performance metrics of Situational Awareness, Shared Situational Awareness and bottom-line Combat Effectiveness due to use of the new NECC (Net-Enabled Command Capability) technology employed in the experiment trials. The especially important role of enhanced operational replanning quality, and speed, enabled by the new technology, will be carefully examined here, since recent experimentation results suggest them as NCW metrics that warrant more scrutiny by the research community. (See Hiniker and Entin, 2006.) Thus, we expect that collaboration and synchronized replanning will play important roles impacting combat effectiveness in this C2 experiment., Presented at the International Command and Control Research and Technology Symposium (12th) held in Newport, RI on 19-21 Jun 2007. Document contains briefing charts in addition to text. The original document contains color images.

The Ez.ast; algorithm is a path planning method capable of dynamic replanning and user-configurable path cost interpolation. It calculates a navigation function as a sampling of an underlying smooth goal distance that takes into account a continuous notion of risk that can be controlled in a fine-grained manner. E* results in more appropriate paths during gradient descent. Dynamic replanning means that changes in the environment model can be repaired to avoid the expenses of complete replanning. This helps compensating for the increased computational effort required for interpolation. We present the theoretical basis and a working implementation, as well as measurements of the algorithm's precision, topological correctness, and computational effort. © 2005 IEEE.

Planning can be a valuable tool for supporting a wide array of real-world problems, such as logistics, manufacturing and control. However, these applications are often highly dynamic, resulting in plans that require updating. In such situations, plan repair methods can be used to adapt the plan. In this paper, we propose a general framework for plan repair. This framework is based on an existing general framework for planning, the so-called refinement planning approach. One of the advantages of a general framework is that it helps to understand existing techniques and improve upon them. As an example of this, we show how we can extend an existing planning method into a system that can also deal with plan repair problems. This system is tested on a number of benchmark problems that deal with abstract transportation problems., Software Computer Technology, Electrical Engineering, Mathematics and Computer Science

Planning can be a valuable tool for supporting a wide array of real-world problems, such as logistics, manufacturing and control. However, these applications are often highly dynamic, resulting in plans that require updating. In such situations, plan repair methods can be used to adapt the plan. In this paper, we propose a general framework for plan repair. This framework is based on an existing general framework for planning, the so-called refinement planning approach. One of the advantages of a general framework is that it helps to understand existing techniques and improve upon them. As an example of this, we show how we can extend an existing planning method into a system that can also deal with plan repair problems. This system is tested on a number of benchmark problems that deal with abstract transportation problems., Software Computer Technology, Electrical Engineering, Mathematics and Computer Science

Scheduling an air campaign is time and labor intensive. Exacerbating the problem, combat planners use manual methods to accomplish much of this daunting but critical task. While some work has been done to automate the process, the approaches used generate schedules that must undergo major modifications before they are "flyable." Planners, therefore, distrust the results and use the automated features of the software sparingly. Additional problems arise due to the lag time between the publication of the Air Tasking Order (ATO) and the start of the ATO day. During this time, conditions change in the dynamic battlespace (aircraft break, runways are damaged, etc.). Current execution software performs a validation check against current conditions, but yields no replanning options. This research explores using heuristics to determine good solutions for the initial air campaign plan and extends previous work by including air-tasking priorities. Additionally, the heuristics are adapted to generate replanning options for the ATO Execution Managers. Java was used for portability and for object reusability elsewhere in the planning hierarchy. Furthermore, the method may be applied to other areas in commercial, government, and military organizations. The heuristic can be modified for use in any enterprise where re-scheduling is common.

Contents: Analysis and Technical Approach; Functional and Technical Characteristics; Mission and Flight Planning and Replanning; Orientation and Briefing; Checkout and Maintenance; Reporting; Launch; Handoffs; Enroute Vehicle and Mode Control; Situation Monitoring; Status Monitoring; Mission Execution; Target Acquisition and Cueing Aids; Vehicle Control During Weapon Delivery; Bomb Damage Assessment; Recovery; Specific Areas of Investigation; and Conclusions WITH Recommendations., See also Volume 3, Part 3 AD0919770.

Creating simplicity out of complexity, this research abandons the traditional guidance and control architecture for aerospace vehicles and embraces a revolutionary concept based on the principles of nonlinear optimal control theory. Motivated by the emerging needs of the next generation of reusable space vehicles, an autonomous "integrated" guidance and control system is developed that provides a safe approach to the highly constrained and nonlinear reentry problem. A pseudospectral-based optimal guidance scheme is used to generate high-fidelity, vehicle-tailored solutions to reentry trajectory optimization and guidance problems. To provide an autonomous, onboard capability of satisfying final-approach requirements, a new method is developed that includes an automatic generation of landing constraints given any runway geometry. This unique and simple approach avoids significant complexities arising from previous ideas of trajectory segmentation, trimmed flight, and trajectory tracking schemes. When demonstrating the new ideas, it is shown that the proposed approach can easily compensate for large uncertainties and disturbances consisting of hurricane-force wind gusts. An investigation of these new principles for the complete, nonlinear six degree-of-freedom system dynamics indicates that while the results are quite promising, a substantial amount of new theoretical and computational problems remain open, particularly in the area of over-actuated dynamical systems., http://archive.org/details/highfidelityreal1094510056, US Air Force (USAF) author., Approved for public release; distribution is unlimited.