TY - JOUR
T1 - Autonomous UAV path-planning optimization using metaheuristic approach for predisaster assessment
AU - Qadir, Z.
AU - Zafar, M.H.
AU - Moosavi, S.K.R.
AU - Le, K.N.
AU - Mahmud, M.A.P.
PY - 2022
Y1 - 2022
N2 - In this article, different state-of-the-art metaheuristic algorithms are analyzed to incorporate the collision-free path-planning approach for UAVs in a disaster situation. The efficient path planning is used to identify the main cause of disaster like bushfires that are badly affecting the forest ecosystem throughout the globe. This novel approach is a first step toward a predisaster assessment and possibilities to save the survivors in minimal time. Different metaheuristic algorithms, such as PSO, GWO, WOA, BMO, and DGBCO, are compared for UAV path optimization capability. In order to test the robustness of our proposed model, four different scenarios are presented which include general environment, condense environment, maze environment, and dynamic environment. In each scenario, the obstacles are placed in such a way to increase the overall path complexity for a UAV to reach the destination. The exploration and exploitation groups working simultaneously in a dynamic environment make it effective for UAV path planning using the DGBCO algorithm. Based on the parameters selected, the DGBCO algorithm outperforms other algorithms and achieves 24.5% less transportation cost and 13.3% less computational time. Hence, DGBCO can be efficiently applied for UAV path-planning optimization in any of the aforementioned environments.
AB - In this article, different state-of-the-art metaheuristic algorithms are analyzed to incorporate the collision-free path-planning approach for UAVs in a disaster situation. The efficient path planning is used to identify the main cause of disaster like bushfires that are badly affecting the forest ecosystem throughout the globe. This novel approach is a first step toward a predisaster assessment and possibilities to save the survivors in minimal time. Different metaheuristic algorithms, such as PSO, GWO, WOA, BMO, and DGBCO, are compared for UAV path optimization capability. In order to test the robustness of our proposed model, four different scenarios are presented which include general environment, condense environment, maze environment, and dynamic environment. In each scenario, the obstacles are placed in such a way to increase the overall path complexity for a UAV to reach the destination. The exploration and exploitation groups working simultaneously in a dynamic environment make it effective for UAV path planning using the DGBCO algorithm. Based on the parameters selected, the DGBCO algorithm outperforms other algorithms and achieves 24.5% less transportation cost and 13.3% less computational time. Hence, DGBCO can be efficiently applied for UAV path-planning optimization in any of the aforementioned environments.
UR - http://hdl.handle.net/1959.7/uws:66672
U2 - 10.1109/JIOT.2021.3137331
DO - 10.1109/JIOT.2021.3137331
M3 - Article
SN - 2327-4662
VL - 9
SP - 12505
EP - 12514
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 14
ER -