Digital Library of Zielona Góra contains 9 272 digital objects
Object
Title: Multiquery motion planning in uncertain spaces: Incremental adaptive randomized roadmaps
Contributor:
Clempner, Julio B. - ed. ; Ikonen, Enso - ed. ; Kurdyukov, Alexander P. - ed.
Subtitle:
Group publication title:
Abstract:
Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms. As its application domains grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise. ; In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and graph adjustment strategy to handle diverse types of planning uncertainty such as sensing noise, unknown static and dynamic obstacles and an inaccurate environment map in a discrete-time system. ; The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulting grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision, which gives the planner more time to react to uncertainty before getting too close to an obstacle. ; The simulation results for problems with various sources of uncertainty show a significant improvement compared with similar algorithms in terms of the failure rate, the processing time and the minimum distance from obstacles. The planner is also successfully implemented and tested on a TurtleBot in four different scenarios with uncertainty.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 29, number 4 (2019) ; click here to follow the link
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2019)
Last modified:
Jul 14, 2025
In our library since:
Jul 14, 2025
Number of object content hits:
20
All available object's versions:
https://zbc.uz.zgora.pl/repozytorium/publication/101029
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 29, number 1 (2019)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 29, number 2 (2019)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 29, number 3 (2019)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 29, number 4 (2019)
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| Edition name | Date |
|---|---|
| Multiquery motion planning in uncertain spaces: Incremental adaptive randomized roadmaps | Jul 14, 2025 |
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