TY - GEN
T1 - Autonomous waypoints planning and trajectory generation for multi-rotor UAVs
AU - Li, Yilan
AU - Eslamiat, Hossein
AU - Wang, Ningshan
AU - Zhao, Ziyi
AU - Sanyal, Amit K.
AU - Qiu, Qinru
N1 - Publisher Copyright:
© 2019 ACM.
PY - 2019/4/15
Y1 - 2019/4/15
N2 - Autonomous trajectory generation in a complex environment is a challenging task for multi-rotor unmanned aerial vehicles (UAVs), which have high maneuverability in three-dimensional motion. Safe and effective operations for these UAVs demand obstacle avoidance strategies and advanced trajectory planning and control schemes for stability and energy efficiency. To solve those problems in one framework analytically is extremely challenging when the UAV needs to fly large distance in a complex environment. To address this challenge, a two-level optimization strategy is adopted. At the higher-level, a sequence of waypoints is selected that lead the UAV from its current position to the destination. At the lower-level, an optimal trajectory is generated between each pair of adjacent waypoints analytically. While the goal of trajectory generation is to maintain the stability of the UAV, the goal of the waypoints planning is to select waypoints with the lowest control thrust consumption throughout the entire trip while avoiding collisions with obstacles. The entire framework is implemented using deep reinforcement learning, which learns the highly complicated and non-linear interaction between those two levels, and the impact from the environment. A progressive learning strategy is investigated that not only reduces convergence time but also improves result quality. We further investigate and provide results regarding the tuning of gains in the optimal trajectory scheme using genetic algorithm. The experimental results demonstrate that our proposed approach is able to generate a list of obstacle-free waypoints with minimum control energy and develop an optimal trajectory with optimized platform velocity, acceleration, jerk and control thrust.
AB - Autonomous trajectory generation in a complex environment is a challenging task for multi-rotor unmanned aerial vehicles (UAVs), which have high maneuverability in three-dimensional motion. Safe and effective operations for these UAVs demand obstacle avoidance strategies and advanced trajectory planning and control schemes for stability and energy efficiency. To solve those problems in one framework analytically is extremely challenging when the UAV needs to fly large distance in a complex environment. To address this challenge, a two-level optimization strategy is adopted. At the higher-level, a sequence of waypoints is selected that lead the UAV from its current position to the destination. At the lower-level, an optimal trajectory is generated between each pair of adjacent waypoints analytically. While the goal of trajectory generation is to maintain the stability of the UAV, the goal of the waypoints planning is to select waypoints with the lowest control thrust consumption throughout the entire trip while avoiding collisions with obstacles. The entire framework is implemented using deep reinforcement learning, which learns the highly complicated and non-linear interaction between those two levels, and the impact from the environment. A progressive learning strategy is investigated that not only reduces convergence time but also improves result quality. We further investigate and provide results regarding the tuning of gains in the optimal trajectory scheme using genetic algorithm. The experimental results demonstrate that our proposed approach is able to generate a list of obstacle-free waypoints with minimum control energy and develop an optimal trajectory with optimized platform velocity, acceleration, jerk and control thrust.
KW - Deep reinforcement learning
KW - Multi-rotor UAV
KW - Optimal trajectory generation
KW - Waypoints planning
UR - http://www.scopus.com/inward/record.url?scp=85066042108&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066042108&partnerID=8YFLogxK
U2 - 10.1145/3313151.3313163
DO - 10.1145/3313151.3313163
M3 - Conference contribution
AN - SCOPUS:85066042108
T3 - DESTION 2019 - Proceedings of the Workshop on Design Automation for CPS and IoT
SP - 31
EP - 40
BT - DESTION 2019 - Proceedings of the Workshop on Design Automation for CPS and IoT
A2 - Ramachandran, Gowri Sankar
A2 - Ortiz, Jorge
PB - Association for Computing Machinery, Inc
T2 - 2019 Workshop on Design Automation for CPS and IoT, DESTION 2019
Y2 - 15 April 2019
ER -