Autonomous UAV with Learned Trajectory Generation and Control

Yilan Li, Mingyang Li, Amit Sanyal, Yanzhi Wang, Qinru Qiu

Research output: Chapter in Book/Entry/PoemConference contribution

2 Scopus citations


Unmanned aerial vehicle (UAV) technology is a rapidly growing field with tremendous opportunities for research and applications. To achieve true autonomy for UAVs in the absence of remote control, external navigation aids like global navigation satellite systems and radar systems, a minimum energy trajectory planning that considers obstacle avoidance and stability control will be the key. Although this can be formulated as a constrained optimization problem, due to the complicated non-linear relationships between UAV trajectory and thrust control, it is almost impossible to be solved analytically. While deep reinforcement learning is known for its ability to provide model free optimization for complex system through learning, its state space, actions and reward functions must be designed carefully. This paper presents our vision of different layers of autonomy in a UAV system, and our effort in generating and tracking the trajectory both using deep reinforcement learning (DRL). The experimental results show that compared to conventional approaches, the learned trajectory will need 20% less control thrust and 18% less time to reach the target. Furthermore, using the control policy learning by DRL, the UAV will achieve 58.14% less position error and 21.77% less system power.

Original languageEnglish (US)
Title of host publication2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728119274
StatePublished - Oct 2019
Event33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019 - Nanjing, China
Duration: Oct 20 2019Oct 23 2019

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130


Conference33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019


  • Deep reinforcement learning
  • actor-critic algorithm
  • continuous trajectory tracking
  • unmanned aerial vehicles

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture


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