Multiquery motion planning in uncertain spaces: Incremental adaptive randomized roadmaps

Khaksar, Weria; Uddin, Md Zia; Torresen, Jim

doi:10.2478/amcs-2019-0047

Artykuł - szczegóły

Tytuł artykułu

Multiquery motion planning in uncertain spaces: Incremental adaptive randomized roadmaps

Autorzy

Khaksar Weria , Uddin Md Zia , Torresen Jim

Treść / Zawartość

Pełne teksty:

02_khaksar_uddin_torresen_multiquery_motion_planning_2019_4.pdf

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Identyfikatory

DOI

10.2478/amcs-2019-0047

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

motion planning roadmap sampling based planner obstacle avoidance

planowanie ruchu mapa drogowa unikanie przeszkód

Wydawca

Oficyna Wydawnicza Uniwersytetu Zielonogórskiego

Czasopismo

International Journal of Applied Mathematics and Computer Science

Rocznik

2019

Tom

Vol. 29, no. 4

Strony

641--654

Opis fizyczny

Bibliogr. 39 poz., rys., tab., wykr.

Twórcy

autor

Khaksar Weria

weriak@ifi.uio.no

Robotics and Intelligent Systems Group (ROBIN), Department of Informatics University of Oslo, Ole Johan Dahls hus, Gaustadallen 23 B, N-0373 Oslo, Norway

autor

Uddin Md Zia

mdzu@ifi.uio.no

Robotics and Intelligent Systems Group (ROBIN), Department of Informatics University of Oslo, Ole Johan Dahls hus, Gaustadallen 23 B, N-0373 Oslo, Norway

autor

Torresen Jim

jimtoer@ifi.uio.no

Robotics and Intelligent Systems Group (ROBIN), Department of Informatics University of Oslo, Ole Johan Dahls hus, Gaustadallen 23 B, N-0373 Oslo, Norway

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-b46e7184-2233-4c68-aa87-f0a25e854204