27,830 research outputs found
Landmark Guided Probabilistic Roadmap Queries
A landmark based heuristic is investigated for reducing query phase run-time
of the probabilistic roadmap (\PRM) motion planning method. The heuristic is
generated by storing minimum spanning trees from a small number of vertices
within the \PRM graph and using these trees to approximate the cost of a
shortest path between any two vertices of the graph. The intermediate step of
preprocessing the graph increases the time and memory requirements of the
classical motion planning technique in exchange for speeding up individual
queries making the method advantageous in multi-query applications. This paper
investigates these trade-offs on \PRM graphs constructed in randomized
environments as well as a practical manipulator simulation.We conclude that the
method is preferable to Dijkstra's algorithm or the algorithm with
conventional heuristics in multi-query applications.Comment: 7 Page
SkiMap: An Efficient Mapping Framework for Robot Navigation
We present a novel mapping framework for robot navigation which features a
multi-level querying system capable to obtain rapidly representations as
diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These
are inherently embedded into a memory and time efficient core data structure
organized as a Tree of SkipLists. Compared to the well-known Octree
representation, our approach exhibits a better time efficiency, thanks to its
simple and highly parallelizable computational structure, and a similar memory
footprint when mapping large workspaces. Peculiarly within the realm of mapping
for robot navigation, our framework supports realtime erosion and
re-integration of measurements upon reception of optimized poses from the
sensor tracker, so as to improve continuously the accuracy of the map.Comment: Accepted by International Conference on Robotics and Automation
(ICRA) 2017. This is the submitted version. The final published version may
be slightly differen
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Visual robot navigation within large-scale, semi-structured environments
deals with various challenges such as computation intensive path planning
algorithms or insufficient knowledge about traversable spaces. Moreover, many
state-of-the-art navigation approaches only operate locally instead of gaining
a more conceptual understanding of the planning objective. This limits the
complexity of tasks a robot can accomplish and makes it harder to deal with
uncertainties that are present in the context of real-time robotics
applications. In this work, we present Topomap, a framework which simplifies
the navigation task by providing a map to the robot which is tailored for path
planning use. This novel approach transforms a sparse feature-based map from a
visual Simultaneous Localization And Mapping (SLAM) system into a
three-dimensional topological map. This is done in two steps. First, we extract
occupancy information directly from the noisy sparse point cloud. Then, we
create a set of convex free-space clusters, which are the vertices of the
topological map. We show that this representation improves the efficiency of
global planning, and we provide a complete derivation of our algorithm.
Planning experiments on real world datasets demonstrate that we achieve similar
performance as RRT* with significantly lower computation times and storage
requirements. Finally, we test our algorithm on a mobile robotic platform to
prove its advantages.Comment: 8 page
Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing
Recent research in multi-robot exploration and mapping has focused on
sampling environmental fields, which are typically modeled using the Gaussian
process (GP). Existing information-theoretic exploration strategies for
learning GP-based environmental field maps adopt the non-Markovian problem
structure and consequently scale poorly with the length of history of
observations. Hence, it becomes computationally impractical to use these
strategies for in situ, real-time active sampling. To ease this computational
burden, this paper presents a Markov-based approach to efficient
information-theoretic path planning for active sampling of GP-based fields. We
analyze the time complexity of solving the Markov-based path planning problem,
and demonstrate analytically that it scales better than that of deriving the
non-Markovian strategies with increasing length of planning horizon. For a
class of exploration tasks called the transect sampling task, we provide
theoretical guarantees on the active sampling performance of our Markov-based
policy, from which ideal environmental field conditions and sampling task
settings can be established to limit its performance degradation due to
violation of the Markov assumption. Empirical evaluation on real-world
temperature and plankton density field data shows that our Markov-based policy
can generally achieve active sampling performance comparable to that of the
widely-used non-Markovian greedy policies under less favorable realistic field
conditions and task settings while enjoying significant computational gain over
them.Comment: 10th International Conference on Autonomous Agents and Multiagent
Systems (AAMAS 2011), Extended version with proofs, 11 page
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