Recent times are witnessing the emergence of indoor sites with extenuating circumstances that place a strict time constraint on mobile robots to reach a target while covering a given area. This has created a global demand to equip mobile robots with the ability to autonomously plan a coverage path to reach the static target effectively and efficiently. The current approaches to achieve such tasks, however, are either time-consuming or human-operator dependent. To this end, an offline-online strategy is proposed to meet the speeding-up challenge by efficiently modelling the environment using a priori information. In the 'offline' stage of the strategy, the layout of the environment is segmented into a set of regions. The corners and dead-ends are identified based on the spatial mobility of the regions. The global path is then computed by deriving a graph-structured, road map using the segmented regions. In the 'online' stage, the global path is traversed by selecting frontiers which concurrently minimizes the covered area and time. In case the path is obstructed, a re-planning strategy is deployed. The proposed strategy is evaluated by various experiments against two baseline search approaches in three simulated environments. The results manifest a significant reduction in time to reach the goal and coverage area which caters to the strict time constraint for mobile robots.

Khanam, Z., Saha, S., Ognibene, D., McDonald-Maier, K., & Ehsan, S. (2021). An offline-online strategy for goal-oriented coverage path planning using a priori information. In 2021 14th IEEE International Conference on Industry Applications, INDUSCON 2021 - Proceedings (pp.874-881). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/INDUSCON51756.2021.9529583].

An offline-online strategy for goal-oriented coverage path planning using a priori information

Ognibene D.;
2021

Abstract

Recent times are witnessing the emergence of indoor sites with extenuating circumstances that place a strict time constraint on mobile robots to reach a target while covering a given area. This has created a global demand to equip mobile robots with the ability to autonomously plan a coverage path to reach the static target effectively and efficiently. The current approaches to achieve such tasks, however, are either time-consuming or human-operator dependent. To this end, an offline-online strategy is proposed to meet the speeding-up challenge by efficiently modelling the environment using a priori information. In the 'offline' stage of the strategy, the layout of the environment is segmented into a set of regions. The corners and dead-ends are identified based on the spatial mobility of the regions. The global path is then computed by deriving a graph-structured, road map using the segmented regions. In the 'online' stage, the global path is traversed by selecting frontiers which concurrently minimizes the covered area and time. In case the path is obstructed, a re-planning strategy is deployed. The proposed strategy is evaluated by various experiments against two baseline search approaches in three simulated environments. The results manifest a significant reduction in time to reach the goal and coverage area which caters to the strict time constraint for mobile robots.
Si
poster + paper
Scientifica
A priori information; Coverage path planning; Frontier detection; Goal-oriented sensor based coverage; Static target;
English
IEEE International Conference on Industry Applications, INDUSCON 2021
978-1-6654-4118-6
https://ieeexplore.ieee.org/document/9529583/authors#authors
Khanam, Z., Saha, S., Ognibene, D., McDonald-Maier, K., & Ehsan, S. (2021). An offline-online strategy for goal-oriented coverage path planning using a priori information. In 2021 14th IEEE International Conference on Industry Applications, INDUSCON 2021 - Proceedings (pp.874-881). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/INDUSCON51756.2021.9529583].
Khanam, Z; Saha, S; Ognibene, D; McDonald-Maier, K; Ehsan, S
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/362660
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