TY - GEN
T1 - Complete Coverage Path Planning for Omnidirectional Expand and Collapse Robot Panthera
AU - Yi, Lim
AU - Sang, Ash Wan Yaw
AU - Hayat, Abdullah Aamir
AU - Tang, Qinrui
AU - Le, Anh Vu
AU - Elara, Mohan Rajesh
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Autonomous mobile robots (AMRs) face challenges in efficiently covering complex environments. To navigate narrow and expansive areas, AMRs must have two essential attributes: compact size for confined spaces and larger size with omnidirectional locomotion for broader spaces. This study utilizes omnidirectional expand and collapse robots (OECRs) to demonstrate efficient area coverage. OECRs can collapse to navigate through confined spaces and expand for efficient coverage in broad spaces. However, current complete coverage path planning (CCPP) methods do not account for the expanded and collapsed states of OECRs. To address this, a depth-first search (DFS) approach is proposed for OECRs' CCPP, which can adjust the robotic footprint along the CCPP path to reduce path length. The proposed DFS outperforms the state-of-the-art CCPP in terms of increased area coverage and reduced distance traveled on a selected map.
AB - Autonomous mobile robots (AMRs) face challenges in efficiently covering complex environments. To navigate narrow and expansive areas, AMRs must have two essential attributes: compact size for confined spaces and larger size with omnidirectional locomotion for broader spaces. This study utilizes omnidirectional expand and collapse robots (OECRs) to demonstrate efficient area coverage. OECRs can collapse to navigate through confined spaces and expand for efficient coverage in broad spaces. However, current complete coverage path planning (CCPP) methods do not account for the expanded and collapsed states of OECRs. To address this, a depth-first search (DFS) approach is proposed for OECRs' CCPP, which can adjust the robotic footprint along the CCPP path to reduce path length. The proposed DFS outperforms the state-of-the-art CCPP in terms of increased area coverage and reduced distance traveled on a selected map.
UR - http://www.scopus.com/inward/record.url?scp=85182524655&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182524655&partnerID=8YFLogxK
U2 - 10.1109/IROS55552.2023.10342525
DO - 10.1109/IROS55552.2023.10342525
M3 - Conference contribution
AN - SCOPUS:85182524655
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 8249
EP - 8254
BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Y2 - 1 October 2023 through 5 October 2023
ER -