8,925 research outputs found
Path planning for complex 3D multilevel environments
The continuous development of graphics hardware is
contributing to the creation of 3D virtual worlds with
high level of detail, from models of large urban areas, to
complete infrastructures, such as residential buildings,
stadiums, industrial settings or archaeological sites, to
name just a few. Adding virtual humans or avatars adds
an extra touch to the visualization providing an enhanced
perception of the spaces, namely adding a sense of scale,
and enabling simulations of crowds. Path planning for
crowds in a meaningful way is still an open research
field, particularly when it involves an unknown polygonal
3D world. Extracting the potential paths for navigation in
a non automated fashion is no longer a feasible option
due to the dimension and complexity of the virtual
environments available nowadays. This implies that we
must be able to automatically extract information from
the geometry of the unknown virtual world to define
potential paths, determine accessibilities, and prepare a
navigation structure for real time path planning and path
finding. A new image based method is proposed that
deals with arbitrarily a priori unknown complex virtual
worlds, namely those consisting of multilevel passages
(e.g. over and below a bridge). The algorithm is capable
of extracting all the information required for the actual
navigation of avatars, creating a hierarchical data
structure to help both high level path planning and low
level path finding decisions. The algorithm is image
based, hence it is tessellation independent, i.e. the
algorithm does not rely on the underlying polygonal
structure of the 3D world. Therefore, the number of
polygons does not have a significant impact on the
performance, and the topology has no weight on the
results.Fundação para a Ciência e a Tecnologi
Path planning for complex 3D multilevel environments
The continuous development of graphics hardware is
contributing to the creation of 3D virtual worlds with
high level of detail, from models of large urban areas, to
complete infrastructures, such as residential buildings,
stadiums, industrial settings or archaeological sites, to
name just a few. Adding virtual humans or avatars adds
an extra touch to the visualization providing an enhanced
perception of the spaces, namely adding a sense of scale,
and enabling simulations of crowds. Path planning for
crowds in a meaningful way is still an open research
field, particularly when it involves an unknown polygonal
3D world. Extracting the potential paths for navigation in
a non automated fashion is no longer a feasible option
due to the dimension and complexity of the virtual
environments available nowadays. This implies that we
must be able to automatically extract information from
the geometry of the unknown virtual world to define
potential paths, determine accessibilities, and prepare a
navigation structure for real time path planning and path
finding. A new image based method is proposed that
deals with arbitrarily a priori unknown complex virtual
worlds, namely those consisting of multilevel passages
(e.g. over and below a bridge). The algorithm is capable
of extracting all the information required for the actual
navigation of avatars, creating a hierarchical data
structure to help both high level path planning and low
level path finding decisions. The algorithm is image
based, hence it is tessellation independent, i.e. the
algorithm does not use the underlying polygonal structure
of the 3D world. Therefore, the number of polygons as
well as the topology, do not affect the performance
Hierarchical path-finding for Navigation Meshes (HNA*)
Path-finding can become an important bottleneck as both the size of the virtual environments and the number of agents navigating them increase. It is important to develop techniques that can be efficiently applied to any environment independently of its abstract representation. In this paper we present a hierarchical NavMesh representation to speed up path-finding. Hierarchical path-finding (HPA*) has been successfully applied to regular grids, but there is a need to extend the benefits of this method to polygonal navigation meshes. As opposed to regular grids, navigation meshes offer representations with higher accuracy regarding the underlying geometry, while containing a smaller number of cells. Therefore, we present a bottom-up method to create a hierarchical representation based on a multilevel k-way partitioning algorithm (MLkP), annotated with sub-paths that can be accessed online by our Hierarchical NavMesh Path-finding algorithm (HNA*). The algorithm benefits from searching in graphs with a much smaller number of cells, thus performing up to 7.7 times faster than traditional A¿ over the initial NavMesh. We present results of HNA* over a variety of scenarios and discuss the benefits of the algorithm together with areas for improvement.Peer ReviewedPostprint (author's final draft
Value Iteration Networks on Multiple Levels of Abstraction
Learning-based methods are promising to plan robot motion without performing
extensive search, which is needed by many non-learning approaches. Recently,
Value Iteration Networks (VINs) received much interest since---in contrast to
standard CNN-based architectures---they learn goal-directed behaviors which
generalize well to unseen domains. However, VINs are restricted to small and
low-dimensional domains, limiting their applicability to real-world planning
problems.
To address this issue, we propose to extend VINs to representations with
multiple levels of abstraction. While the vicinity of the robot is represented
in sufficient detail, the representation gets spatially coarser with increasing
distance from the robot. The information loss caused by the decreasing
resolution is compensated by increasing the number of features representing a
cell. We show that our approach is capable of solving significantly larger 2D
grid world planning tasks than the original VIN implementation. In contrast to
a multiresolution coarse-to-fine VIN implementation which does not employ
additional descriptive features, our approach is capable of solving challenging
environments, which demonstrates that the proposed method learns to encode
useful information in the additional features. As an application for solving
real-world planning tasks, we successfully employ our method to plan
omnidirectional driving for a search-and-rescue robot in cluttered terrain
Walking in the cities without ground, how 3D complex network volumetrics improve analysis
Pedestrian route choice, wayfinding behaviour and movement pattern research rely on objective spatial configuration model and analysis. In 3D indoor and outdoor multi-level buildings and urban built environments (IO-ML-BE), spatial configuration analysis allows to quantify and control for route choice and wayfinding complexity/difficulty. Our contribution is to compare the interaction of the level of definition (LOD) of indoor and outdoor multi-level pedestrian network spatial models and complexity metric analyses. Most studies are indoor or outdoor and oversimplify multi-level vertical connections. Using a novel open data set of a large-scale 3D centreline pedestrian network which implement transport geography 2D data model principles in 3D, nine spatial models and twelve spatial complexity analyses of a large-scale 3D IO-ML-BE are empirically tested with observed pedestrian movement patterns (N = 17,307). Bivariate regression analyses show that the association with movement pattern increases steadily from R2 ≈ 0.29 to 0.56 (space syntax, 2.5D) and from R2 ≈ 0.54 to 0.72 (3D sDNA) as the 3D transport geography spatial model LOD and completeness increases. A multivariate stepwise regression analysis tests the bi-variate findings. A novel 3D hybrid angular-Euclidean analysis was tested for the objective description of 3D multi-level IO-ML-BE route choice and wayfinding complexity. The results suggest that pedestrian route choice, wayfinding and movement pattern analysis and prediction research in a multi-level IO-ML-BE should use high-definition 3D transport geography network spatial model and include interdependent outdoor and indoor spaces with detailed vertical transitions
Hierarchical Path Finding to Speed Up Crowd Simulation
Path finding is a common problem in computer games. Most videogames require to simulate thousands or millions of agents who interact and navigate in a 3D world showing capabilities such as chasing, seeking or intercepting other agents. A new hierarchical path finding solution is proposed for large environments. Thus, a navigation mesh as abstract data structure is used in order to divide the 3D world. Then, a hierarchy of graphs is built to perform faster path finding calculations than a common A*. The benefits of this new approach are demonstrated on large world models
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