TY - GEN
T1 - Complete Coverage Path Planning Using Adaptive GBNN for Omnidirectional Sweeping Robot
AU - Yi, Lim
AU - Wan, Ash Yaw Sang
AU - Hayat, A. A.
AU - Vu Le, Anh
AU - Tang, Q. R.
AU - Balakrishnan, R.
AU - Elara, M. R.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Outdoor cleaning while maximizing the area coverage is a complex task because of confined spaces, precision motion, and planning challenges. This paper proposes a novel design of an omnidirectional self-reconfigurable robot named PantheraV3. The novelty here is in terms of the ability of the sweeping brushes to rotate and brush along its length and width, resulting mainly in two states of the robot during the cleaning. This paper proposes the Complete Coverage Path Planning (CCPP) method by adapting Glasius Bio-inspired Neural Network (GBNN) to the robot form factor for more efficient area coverage. The algorithm is made generic which can support any form factor or footprint of robots. Real-world experiments were conducted with PantheraV3. Using the proposed aGBNN and PantheraV3 was able to complete area coverage with lesser path length, i.e., 40% reduction in distance traveled than with GBNN in a selected environment.
AB - Outdoor cleaning while maximizing the area coverage is a complex task because of confined spaces, precision motion, and planning challenges. This paper proposes a novel design of an omnidirectional self-reconfigurable robot named PantheraV3. The novelty here is in terms of the ability of the sweeping brushes to rotate and brush along its length and width, resulting mainly in two states of the robot during the cleaning. This paper proposes the Complete Coverage Path Planning (CCPP) method by adapting Glasius Bio-inspired Neural Network (GBNN) to the robot form factor for more efficient area coverage. The algorithm is made generic which can support any form factor or footprint of robots. Real-world experiments were conducted with PantheraV3. Using the proposed aGBNN and PantheraV3 was able to complete area coverage with lesser path length, i.e., 40% reduction in distance traveled than with GBNN in a selected environment.
UR - http://www.scopus.com/inward/record.url?scp=85174392841&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174392841&partnerID=8YFLogxK
U2 - 10.1109/CASE56687.2023.10260630
DO - 10.1109/CASE56687.2023.10260630
M3 - Conference contribution
AN - SCOPUS:85174392841
T3 - IEEE International Conference on Automation Science and Engineering
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Y2 - 26 August 2023 through 30 August 2023
ER -