Abstract
The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.
Original language | English |
---|---|
Title of host publication | 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665434201 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Event | 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 - San Antonio, United States Duration: 3 Oct 2021 → 7 Oct 2021 |
Publication series
Name | AIAA/IEEE Digital Avionics Systems Conference - Proceedings |
---|---|
Volume | 2021-October |
ISSN (Print) | 2155-7195 |
ISSN (Electronic) | 2155-7209 |
Conference
Conference | 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021 |
---|---|
Country/Territory | United States |
City | San Antonio |
Period | 3/10/21 → 7/10/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Global Mission Planning
- Precision Agriculture
- Reinforcement Learning
- Temporal Difference Learning
- UAV