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

Real-time Aerial Detection and Reasoning on Embedded-UAVs

We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the trade-off. This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition for the emerging UAV applications of autonomous surveying and activity reporting. In particular, our pipeline architectures operate in a time-sensitive manner, have high accuracy in detecting pedestrians from various aerial orientations, use a novel attention map for multi-activities recognition, and jointly refine its detection with temporal information. Numerically, we demonstrate our model's accuracy and fast inference speed on embedded systems. We empirically deployed our prototype hardware with full live feeds in a real-world open-field environment.Comment: In TGR

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Aerial Robotics for Inspection and Maintenance

Aerial robots with perception, navigation, and manipulation capabilities are extending the range of applications of drones, allowing the integration of different sensor devices and robotic manipulators to perform inspection and maintenance operations on infrastructures such as power lines, bridges, viaducts, or walls, involving typically physical interactions on flight. New research and technological challenges arise from applications demanding the benefits of aerial robots, particularly in outdoor environments. This book collects eleven papers from different research groups from Spain, Croatia, Italy, Japan, the USA, the Netherlands, and Denmark, focused on the design, development, and experimental validation of methods and technologies for inspection and maintenance using aerial robots

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

A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems

Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008–2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests

Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at \href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.Comment: 32 pages, 18 figure