482 research outputs found
Noise Aware Path Planning and Power Management of Hybrid Fuel UAVs
Hybrid fuel Unmanned Aerial Vehicles (UAV), through their combination of
multiple energy sources, offer several advantages over the standard single fuel
source configuration, the primary one being increased range and efficiency.
Multiple power or fuel sources also allow the distinct pitfalls of each source
to be mitigated while exploiting the advantages within the mission or path
planning. We consider here a UAV equipped with a combustion engine-generator
and battery pack as energy sources. We consider the path planning and
power-management of this platform in a noise-aware manner. To solve the path
planning problem, we first present the Mixed Integer Linear Program (MILP)
formulation of the problem. We then present and analyze a label-correcting
algorithm, for which a pseudo-polynomial running time is proven. Results of
extensive numerical testing are presented which analyze the performance and
scalability of the labeling algorithm for various graph structures, problem
parameters, and search heuristics. It is shown that the algorithm can solve
instances on graphs as large as twenty thousand nodes in only a few seconds.Comment: 11 pages, 12 figure
Linkless octree using multi-level perfect hashing
The standard C/C++ implementation of a spatial partitioning data structure, such as octree and quadtree, is often inefficient in terms of storage requirements particularly when the memory overhead for maintaining parent-to-child pointers is significant with respect to the amount of actual data in each tree node. In this work, we present a novel data structure that implements uniform spatial partitioning without storing explicit parent-to-child pointer links. Our linkless tree encodes the storage locations of subdivided nodes using perfect hashing while retaining important properties of uniform spatial partitioning trees, such as coarse-to-fine hierarchical representation, efficient storage usage, and efficient random accessibility. We demonstrate the performance of our linkless trees using image compression and path planning examples.postprin
A Global Path Planning Algorithm Based on Bidirectional SVGA
For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎). This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning
Mapping and Localization in Urban Environments Using Cameras
In this work we present a system to fully automatically create a highly accurate visual feature map from image data aquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving
Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning
Quality colormaps can help communicate important data patterns. However,
finding an aesthetically pleasing colormap that looks "just right" for a given
scenario requires significant design and technical expertise. We introduce
Cieran, a tool that allows any data analyst to rapidly find quality colormaps
while designing charts within Jupyter Notebooks. Our system employs an active
preference learning paradigm to rank expert-designed colormaps and create new
ones from pairwise comparisons, allowing analysts who are novices in color
design to tailor colormaps to their data context. We accomplish this by
treating colormap design as a path planning problem through the CIELAB
colorspace with a context-specific reward model. In an evaluation with twelve
scientists, we found that Cieran effectively modeled user preferences to rank
colormaps and leveraged this model to create new quality designs. Our work
shows the potential of active preference learning for supporting efficient
visualization design optimization.Comment: CHI 2024. 12 pages/9 figure
ADAPTIVE PROBABILISTIC ROADMAP CONSTRUCTION WITH MULTI-HEURISTIC LOCAL PLANNING
The motion planning problem means the computation of a collision-free motion for a movable object among obstacles from the given initial placement to the given end placement. Efficient motion planning methods have many applications in many fields, such as robotics, computer aided design, and pharmacology. The problem is known to be PSPACE-hard. Because of the computational complexity, practical applications often use heuristic or incomplete algorithms. Probabilistic roadmap is a probabilistically complete motion planning method that has been an object of intensive study over the past years. The method is known to be susceptible to the problem of “narrow passages”: Finding a motion that passes a narrow, winding tunnel can be very expensive. This thesis presents a probabilistic roadmap method that addresses the narrow passage problem with a local planner based on heuristic search. The algorithm is suitable for planning motions for rigid bodies and articulated robots including multirobot systems with many degrees-of-freedom. Variants of the algorithm are describe
Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques
L'abstract è presente nell'allegato / the abstract is in the attachmen
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