LAVAPilot: Lightweight UAV Trajectory Planner with Situational Awareness for Embedded Autonomy to Track and Locate Radio-tags

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.Comment: Accepted to 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains

Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor-intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real-world conditions remains a challenge. We developed ConservationBots-small aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times -- significant for energy-limited aerial systems while avoiding close encounters with potential, counter-productive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: i) planning to determine both trajectory and measurement actions guided by an information-theoretic objective, which allows the robot to strategically select near-instantaneous range-only measurements to achieve faster localization, and time-consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance; ii) a bearing detector more robust to noise and iii) a tracking algorithm formulation robust to missed and false detections experienced in real-world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high-resolution elevation data on diverse geographical terrains; field testing; studies with wombats (Lasiorhinus latifrons; nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method.Comment: 33 pages, 21 figure

Online localization of radio-tagged wildlife with an autonomous aerial robot system

© 2015, MIT Press Journals. All rights reserved. The application of autonomous robots to efficiently locate small wildlife species has the potential to provide significant ecological insights not previously possible using traditional land-based survey techniques, and a basis for improved conservation policy and management. We present an approach for autonomously localizing radio-tagged wildlife using a small aerial robot. We present a novel two-point phased array antenna system that yields unambiguous bearing measurements and an associated uncertainty measure. Our estimation and information-based planning algorithms incorporate this bearing uncertainty to choose observation points that improve confidence in the location estimate. These algorithms run online in real time and we report experimental results that show successful autonomous localization of stationary radio tags and live radio-tagged birds

Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review

This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190