TY - GEN
T1 - DOMA
T2 - 32nd International Conference on Automated Planning and Scheduling, ICAPS 2022
AU - Yu, Jin
AU - Piao, Haiyin
AU - Hou, Yaqing
AU - Mo, Li
AU - Yang, Xin
AU - Zhou, Deyun
N1 - Publisher Copyright:
© 2022, Association for the Advancement of Artificial Intelligence.
PY - 2022/6/13
Y1 - 2022/6/13
N2 - In this paper, we present a Deep Reinforcement Learning (DRL) based real-time smooth UAV motion planning method for solving catastrophic flight trajectory oscillation issues. By formalizing the original problem as a linear mixture of dual-objective optimization, a novel Deep smOoth Motion plAnning (DOMA) algorithm is proposed, which adopts an alternative layer-by-layer gradient descending optimization approach with the major gradient and the DOMA gradient applied separately. Afterwards, the mix weight coefficient between the two objectives is also optimized adaptively. Experimental result reveals that the proposed DOMA algorithm outperforms baseline DRL-based UAV motion planning algorithms in terms of both learning efficiency and flight motion smoothness. Furthermore, the UAV safety issue induced by trajectory oscillation is also addressed.
AB - In this paper, we present a Deep Reinforcement Learning (DRL) based real-time smooth UAV motion planning method for solving catastrophic flight trajectory oscillation issues. By formalizing the original problem as a linear mixture of dual-objective optimization, a novel Deep smOoth Motion plAnning (DOMA) algorithm is proposed, which adopts an alternative layer-by-layer gradient descending optimization approach with the major gradient and the DOMA gradient applied separately. Afterwards, the mix weight coefficient between the two objectives is also optimized adaptively. Experimental result reveals that the proposed DOMA algorithm outperforms baseline DRL-based UAV motion planning algorithms in terms of both learning efficiency and flight motion smoothness. Furthermore, the UAV safety issue induced by trajectory oscillation is also addressed.
UR - http://www.scopus.com/inward/record.url?scp=85142633206&partnerID=8YFLogxK
U2 - 10.1609/icaps.v32i1.19855
DO - 10.1609/icaps.v32i1.19855
M3 - Conference contribution
AN - SCOPUS:85142633206
T3 - Proceedings International Conference on Automated Planning and Scheduling, ICAPS
SP - 662
EP - 666
BT - Proceedings of the 32nd International Conference on Automated Planning and Scheduling, ICAPS 2022
A2 - Kumar, Akshat
A2 - Thiebaux, Sylvie
A2 - Varakantham, Pradeep
A2 - Yeoh, William
PB - Association for the Advancement of Artificial Intelligence
Y2 - 13 June 2022 through 24 June 2022
ER -