LAVAPilot: lightweight UAV trajectory planner with situational awareness for embedded autonomy to track and locate radio-tags

Hoa Van Nguyen, Fei Chen, Joshua Chesser, Hamid Rezatofighi, Damith Ranasinghe

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Tracking and locating radio-tagged wildlife is a labor-intensive and time-consuming task necessary in wildlife conservation. In this article, we focus on the problem of achieving embedded autonomy for a resource-limited aerial robot for the task capable of avoiding undesirable disturbances to wildlife. We employ a lightweight sensor system capable of simultaneous (noisy) measurements of radio signal strength information from multiple tags for estimating object locations. We formulate a new lightweight task-based trajectory planning method - LAVAPilot - with a greedy evaluation strategy and a void functional formulation to achieve situational awareness to maintain a safe distance from objects of interest. Conceptually, we embed our intuition of moving closer to reduce the uncertainty of measurements into LAVAPilot instead of employing a computationally intensive information gain based planning strategy. We employ LAVAPilot and the sensor to build a lightweight aerial robot platform with fully embedded autonomy for jointly tracking and planning to track and locate multiple VHF radio collar tags used by conservation biologists. Using extensive Monte Carlo simulation-based experiments, implementations on a single board compute module, and field experiments using an aerial robot platform with multiple VHF radio collar tags, we evaluate our joint planning and tracking algorithms. Further, we compare our method with other information-based planning methods with and without situational awareness to demonstrate the effectiveness of our robot executing LAVAPilot. Our experiments demonstrate that LAVAPilot significantly reduces (by 98.5%) the computational cost of planning to enable real-time planning decisions whilst achieving similar localization accuracy of objects compared to information gain based planning methods, albeit taking a slightly longer time to complete a mission. To support research in the field, and conservation biology, we also open source the complete project. In particular, to the best of our knowledge, this is the first demonstration of a fully autonomous aerial robot system where trajectory planning and tracking to survey and locate multiple radio-tagged objects are achieved onboard.

Original languageEnglish
Title of host publicationIEEE International Workshop on Intelligent Robots and Systems (IROS)
EditorsHyunglae Lee
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728162126
ISBN (Print)9781728162133
Publication statusPublished - 2020
Externally publishedYes
EventIEEE/RSJ International Conference on Intelligent Robots and Systems 2020 - Virtual, Las Vegas, United States of America
Duration: 24 Jan 202124 Jan 2021 (Website)

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems 2020
Abbreviated titleIROS 2020
CountryUnited States of America
CityLas Vegas
Internet address

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