998 resultados para Longest path


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Mission and path planning for multiple robots in dynamic environments is required when multiple mobile robots or unmanned vehicles are used for geographically distributed tasks. Assigning tasks and paths for robots for cooperatively accomplishing a mission of reaching to number of target points are addressed in this paper. The methodology that is proposed is based on using an adjustable force field which is suitable for dynamic environment. From the force field analysis, the decisions to assign tasks for each robot are then made. The force field is also used to plan a collision free path for each robot. Adjustable weights for the force field model are proposed to satisfy the constraints of the motion. In this research, the constraints are the cooperation of the robots, the precedence between the targets and between robots, and the discrimination between different obstacles. Two simulations for mission and path planning in 2D and 3D dynamic spaces with multiple robots are presented based on the proposed adjustable force filed. The result of the mission and path planning for three robots cooperatively doing eight target points are shown.

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Demutualisation became a global trend amongst financial sector firms in the last two decades of the twentieth century. Changes to the organisational foundations of mutual firms represented a shift in operational cultures and have often been viewed as an end point or demise of the co-operative business model. It is the intention of this article to investigate the extent to which this was the case within a major mutual institution, the Australian Mutual Provident, Australia's oldest and largest mutual insurer. The article's key argument is that the concept of mutuality is organic, and that within this organisation it evolved as the structure of the firm became more sophisticated as it developed from a supplier of life insurance products into a sophisticated financial services provider, which ultimately generated internal pressures to demutualise.

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Australians have long worried about whether Indonesia is ‘special’ or ‘normal’. Instead, we need to deal with Indonesia as it really is—a country experiencing simultaneously the challenges of political reform, economic development and a shifting regional security environment. The country’s political future is less certain than we would hope: after SBY’s term of government ends, the choice of a successor will be critical in determining the future of reform. We can’t rule out that Indonesia might slide back to old ways of doing business—democratisation is a fraught process.
As the Indonesian economy grows, so too do the prospects for Indonesia to establish its natural position as the leader of Southeast Asia. As the world is re-examining Indonesia, so too Indonesia is looking afresh at the world—more interested in external issues than it was a decade ago. The Southeast Asian subregion increasingly finds itself at the centre of a more strongly interconnected Indo-Pacific region—so Indonesia’s strategic importance is going up.
It’s important for Australia to build a better strategic relationship with Indonesia. The two are complementary partners. Australia should be proactive in exploring new opportunities for cooperation with a reform minded Indonesia—it’s in our interests to draw Indonesia into a more important strategic role in regional security.
Professor Damien Kingsbury, the author of this Strategy, is the Director, Centre for Citizenship, Development and Human Rights, Faculty of Arts and Education, Deakin University, Melbourne.

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In this paper, we propose an algorithm for an upgrading arc median shortest path problem for a transportation network. The problem is to identify a set of nondominated paths that minimizes both upgrading cost and overall travel time of the entire network. These two objectives are realistic for transportation network problems, but of a conflicting and noncompensatory nature. In addition, unlike upgrading cost which is the sum of the arc costs on the path, overall travel time of the entire network cannot be expressed as a sum of arc travel times on the path. The proposed solution approach to the problem is based on heuristic labeling and exhaustive search techniques, in criteria space and solution space, respectively. The first approach labels each node in terms of upgrading cost, and deletes cyclic and infeasible paths in criteria space. The latter calculates the overall travel time of the entire network for each feasible path, deletes dominated paths on the basis of the objective vector and identifies a set of Pareto optimal paths in the solution space. The computational study, using two small-scale transportation networks, has demonstrated that the algorithm proposed herein is able to efficiently identify a set of nondominated median shortest paths, based on two conflicting and noncompensatory objectives.

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This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.

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Seven women shared recovery experiences from substance use: What adversely affected their lives as well as that which provided strength, resilience and openness to health and wellness. Academically this study exemplified emancipatory nursing practice: A grassroots relational approach promoting women’s health through awareness, self-discovery and ongoing engagement with the community.

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In this theoretical paper, we introduce and describe a model, and demonstrate its origins from the disciplines of Enterprise Architecture, cybernetics and systems theory. We use cybernetic thinking to develop a ‘Co-evolution Path Model’ that describes how enterprises as complex systems co-evolve with their complex environments. The model re-interprets Stafford Beer’s Viable System Model, and also uses the theorem of the ‘good regulator’ of Conant and Ashby, exemplifying how various complexity management theories could be synthesised into a cybernetic theory of Enterprise Architecture, using concepts from the generalisation of EA frameworks.

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Pedestrian steering activity is a perception-based decision making process that involves interaction with the surrounding environment and insight into environmental stimuli. There are many stimuli within the environment that influence pedestrian wayfinding behaviour during walking activities. However, compelling factors such as individual physical and psychological characteristics and trip intention cause the behaviour become a very fuzzy concept. In this paper pedestrian steering behaviour is modelled using a fuzzy logic approach. The objective of this research is to simulate pedestrian walking paths in indoor public environments during normal and non-panic situations. The proposed algorithm introduces a fuzzy logic framework to predict the impact of perceived attractive and repulsive stimuli, within the pedestrian's field of view, on movement direction. Environmental stimuli are quantified using the social force method. The algorithm is implemented in a simulated area of an office corridor consist of a printer and exit door. Stochastic simulation using the proposed fuzzy algorithm generated realistic walking trajectories, contour map of dynamic change of environmental effects in each step of movement and high flow areas in the corridor.

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Taking the uncertainty existing in edge weights of networks into consideration, finding shortest path in such fuzzy weighted networks has been widely studied in various practical applications. In this paper, an amoeboid algorithm is proposed, combing fuzzy sets theory with a path finding model inspired by an amoeboid organism, Physarum polycephalum. With the help of fuzzy numbers, uncertainty is well represented and handled in our algorithm. What's more, biological intelligence of Physarum polycephalum has been incorporate into the algorithm. A numerical example on a transportation network is demonstrated to show the efficiency and flexibility of our proposed amoeboid algorithm.