Scalable Task Cleanup Assignment for Multi-agents

This paper describes a group of robots for cleaning a simulated environment and proposes an ecient algorithm for navigation based on Path nding A *. No need for vision sensors. As a result it was observed that the robots can work cooperatively to clear the ground and that the navigation algorithm is e ective in cleaning. In order to test its eciency it was compared the combination of the Path nding A* algorithm and the decision algorithm proposed in this paper with Path nding A* and Euclidean distance, resulted in an improvement in time and distance traveled

Subregion graph: A path planning acceleration structure for characters with various motion types in very large environments

Modern computer graphics applications commonly feature very large virtual environments and diverse characters which perform different kinds of motions. To accelerate path planning in such a scenario, we propose the subregion graph data structure. It consists of subregions, which are clusters of locally connected waypoints inside a region, as well as subregion connectivities. We also present a fast algorithm to automatically generate a subregion graph from an enhanced waypoint graph map representation, which also supports various motion types and can be created from large virtual environments. Nevertheless, a subregion graph can be generated from any graphbased map representation. Our experiments show that a subregion graph is very compact relative to the input waypoint graph. By firstly planning a subregion path, and then limiting waypoint-level planning to this subregion path, over 8 times average speedup can be achieved, while average length ratios remain as low as 102.5%

Performance Evaluation of Pathfinding Algorithms

Pathfinding is the search for an optimal path from a start location to a goal location in a given environment. In Artificial Intelligence pathfinding algorithms are typically designed as a kind of graph search. These algorithms are applicable in a wide variety of applications such as computer games, robotics, networks, and navigation systems. The performance of these algorithms is affected by several factors such as the problem size, path length, the number and distribution of obstacles, data structures and heuristics. When new pathfinding algorithms are proposed in the literature, their performance is often investigated empirically (if at all). Proper experimental design and analysis is crucial to provide an informative and non- misleading evaluation. In this research, we survey many papers and classify them according to their methodology, experimental design, and analytical techniques. We identify some weaknesses in these areas that are all too frequently found in reported approaches. We first found the pitfalls in pathfinding research and then provide solutions by creating example problems. Our research shows that spurious effects, control conditions provide solutions to avoid these pitfalls

A comparative study of navigation meshes

International audienceA navigation mesh is a representation of a 2D or 3D virtual environment that enables path planning and crowd simulation for walking characters. Various state-of-the-art navigation meshes exist, but there is no standardized way of evaluating or comparing them. Each implementation is in a different state of maturity, has been tested on different hardware, uses different example environments, and may have been designed with a different application in mind. In this paper, we conduct the first comparative study of navigation meshes. First, we give general definitions of 2D and 3D environments and navigation meshes. Second, we propose theoretical properties by which navigation meshes can be classified. Third, we introduce metrics by which the quality of a navigation mesh implementation can be measured objectively. Finally, we use these metrics to compare various state-of-the-art navigation meshes in a range of 2D and 3D environments. We expect that this work will set a new standard for the evaluation of navigation meshes, that it will help developers choose an appropriate navigation mesh for their application, and that it will steer future research on navigation meshes in interesting directions

Exploiting Problem Structure in Pathfinding

With a given map and a start and a goal position on the graph, a pathfinding algorithm typically searches on this graph from the start node and exploring its neighbour nodes until reaching the goal. It is closely related to the shortest path problem. A* is one of the best and most popular heuristic-guided algorithms used in pathfinding for video games. The algorithm always picks the node with the smallest f value and process this node. The f value is the sum of two parameters g (the actual cost from the start node to the current node) and h (estimated cost from the current node to the goal). At each step of the algorithm, the node with lowest f will be removed from an open list and its neighbour nodes with their f values would be updated in this list. The main cost of this algorithm is the frequent insertion and deleteMin operations of the open list. Typically, implementation of A* uses a priority queue or min-heap to implement the open list, which takes O(log n) for the operations in the worst case. But this is still expensive when using the algorithm in a large and complicated map with numerous nodes. We came up with a new data structure called multi-stack heap for the open list based on the 2D grid map and Manhattan distance, which only costs O(1) for insertion and deleteMin. It is very efficient especially when we have a considerable number of nodes to explore. Additionally, traditional A* requires checking whether the open list contains a duplicated of the being inserted node before every insertion, which takes O(n). We proposed a new implementation method based on admissible and consistent heuristic called “Check From Closed List”, it can reduce the time of this process to O(1)

Can web indicators be used to estimate the citation impact of conference papers in engineering?

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Although citation counts are widely used to support research evaluation, they can only reflect academic impacts, whereas research can also be useful outside academia. There is therefore a need for alternative indicators and empirical studies to evaluate them. Whilst many previous studies have investigated alternative indicators for journal articles and books, this thesis explores the importance and suitability of four web indicators for conference papers. These are readership counts from the online reference manager Mendeley and citation counts from Google Patents, Wikipedia and Google Books. To help evaluate these indicators for conference papers, correlations with Scopus citations were evaluated for each alternative indicator and compared with corresponding correlations between alternative indicators and citation counts for journal articles. Four subject areas that value conferences were chosen for the analysis: Computer Science Applications; Computer Software Engineering; Building & Construction Engineering; and Industrial & Manufacturing Engineering. There were moderate correlations between Mendeley readership counts and Scopus citation counts for both journal articles and conference papers in Computer Science Applications and Computer Software. For conference papers in Building & Construction Engineering and Industrial & Manufacturing Engineering, the correlations between Mendeley readers and citation counts are much lower than for journal articles. Thus, in fields where conferences are important, Mendeley readership counts are reasonable impact indicators for conference papers although they are better impact indicators for journal articles. Google Patent citations had low positive correlations with citation counts for both conference papers and journal articles in Software Engineering and Computer Science Applications. There were negative correlations for both conference papers and journal articles in Industrial and Manufacturing Engineering. However, conference papers in Building and Construction Engineering attracted no Google Patent citations. This suggests that there are disciplinary differences but little overall value for Google Patent citations as impact indicators in engineering fields valuing conferences. Wikipedia citations had correlations with Scopus citations that were statistically significantly positive only in Computer Science Applications, whereas the correlations were not statistically significantly different from zero in Building & Construction Engineering, Industrial & Manufacturing Engineering and Software Engineering. Conference papers were less likely to be cited in Wikipedia than journal articles were in all fields, although the difference was minor in Software Engineering. Thus, Wikipedia citations seem to have little value in engineering fields valuing conferences. Google Books citations had positive significant correlations with Scopus-indexed citations for conference papers in all fields except Building & Construction Engineering, where the correlations were not statistically significantly different from zero. Google Books citations seemed to be most valuable impact indicators in Computer Science Applications and Software Engineering, where the correlations were moderate, than in Industrial & Manufacturing Engineering, where the correlations were low. This means that Google Book citations are valuable indicators for conference papers in engineering fields valuing conferences. Although evidence from correlation tests alone is insufficient to judge the value of alternative indicators, the results suggest that Mendeley readers and Google Books citations may be useful for both journal articles and conference papers in engineering fields that value conferences, but not Wikipedia citations or Google Patent citations.Tetfund, Nigeri