Search and rescue under the forest canopy using multiple UAVs

Tian, Yulun; Liu, Katherine; Ok, Kyel; Tran, Loc; Allen, Danette; Roy, Nicholas; How, Jonathan P.

doi:10.1177/0278364920929398

Cited by 80 publications

(38 citation statements)

References 79 publications

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“…There is prior work that addresses under-canopy data collection but mostly with human pilots. 3,4 Only a few under-canopy autonomous flights have been demonstrated so far, 5,6 but the environments are much sparser than those shown in Figure 1. Moreover, those flights are only performed at a relatively small scale.…”

Section: In This Article We Will Describe the State Of The Art In Uav...mentioning

confidence: 99%

“…A conservative local planning strategy was used with unobserved space regarded as nonfree space, which potentially limits the flight speeds as well as the optimality of local planning. A system of multiple UAVs collaboratively surveying and mapping under forest canopy was presented by Tian et al 6 A 2-D LiDAR is used for state estimation and mapping. A collaborative simultaneous localization and mapping (SLAM) scheme with loop closure capabilities was proposed where object-based maps were built by detecting trees and using them as landmarks.…”

Section: Uav Platforms and Autonomymentioning

confidence: 99%

“…Some recent work use methods such as clustering, filtering, circle fitting, and arc extraction to detect trees. 6 However, their performances are parameter-sensitive, which require expert knowledge to tune. Therefore, a data-driven approach is preferred for large-scale complicated agriculture environments.…”

Section: Lidar-based (3-d)mentioning

confidence: 99%

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Challenges and Opportunities for Autonomous Micro-UAVs in Precision Agriculture

Chen

Nardari³

et al. 2022

IEEE Micro

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Mobile robots such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) are increasingly used for precision agriculture. While UGVs have larger payload capabilities and longer operation time, they are limited to 2-D space. This makes UAVs better suited for tasks that require fast coverage, harsh terrain traversal, and high altitude or multilevel operation. However, it remains a challenging task to develop a reliable yet fully autonomous UAV system that can actively extract actionable information in large-scale cluttered agricultural environments. Such a system will have to estimate its own poses, build a map of the environment, navigate through obstacles, and act to gather information with limited onboard computation and battery life. In this survey, we first review recent advances in UAV hardware and software, ranging from novel platforms and sensors to state-of-the-art autonomous navigation, object detection and segmentation, robot localization, and mapping algorithms related to agriculture. We then provide a list of challenges in each field and potential opportunities for the broader adoption of UAVs in precision agriculture.

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Section: In This Article We Will Describe the State Of The Art In Uav...mentioning

confidence: 99%

Section: Uav Platforms and Autonomymentioning

confidence: 99%

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Challenges and Opportunities for Autonomous Micro-UAVs in Precision Agriculture

Chen

Nardari³

et al. 2022

IEEE Micro

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“…Research work focusing on multiple UAVs for SAR missions can be broadly divided into two main areas: (i) swarm systems, which consist of a large number of smaller drones with lower quality sensors and a lower level of autonomy [11]- [13], and (ii) cooperative systems, which contain only several larger UAV platforms with more sensors and more powerful on-board processors to enable more complex onboard decisions than smaller drones [2], [14], [15]. There is considerable amount of research on SAR missions in GPS-denied environments with a single UAV [16]- [20].…”

Section: Introductionmentioning

confidence: 99%

A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments

Xiaolong

Vanegas

Gonzalez

et al. 2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

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The use of multi-rotor Unmanned Aerial Vehicles (UAVs) for search and rescue as well as remote sensing is rapidly increasing. Multi-rotor UAVs, however, have limited endurance. The range of UAV applications can be widened if teams of multiple UAVs are used. We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Examples of such environments include indoor scenarios, urban or natural canyons, caves, and tunnels, where the GPS signal is limited or blocked. The framework is based on a probabilistic decentralised Partially Observable Markov Decision Process which accounts for the uncertainties in sensing and the environment. The team can cooperate efficiently, with each UAV sharing only limited processed observations and their locations during the mission. The system is simulated using the Robotic Operating System and Gazebo. Performance of the system with an increasing number of UAVs in several indoor scenarios with obstacles is tested. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.

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“…Collaborative perception has been widely applied in a variety of real-world applications including human-robot collaborative assembly [18,20], multi-robot search and rescue [1,45], and connected autonomous driving [19,49]. Correspondence identification is defined as a problem to identify the same objects observed by multiple agents in their own fields of view, which is considered an essential component to enable collaborative perception [14,17,43]. For example, as illustrated by Figure 1, when a collaborative robot assists a human worker who wears an augmented reality (AR) headset to assemble a chair, they need to identify the correspondence of the chair parts in order to ensure that both the robot and the human correctly refer to the same object.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Deep Graph Matching for Correspondence Identification in Collaborative Perception

Gao¹,

Zhang²

2021

Robotics: Science and Systems XVII

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Correspondence identification is essential for multirobot collaborative perception, which aims to identify the same objects in order to ensure consistent references of the objects by a group of robots/agents in their own fields of view. Although recent deep learning methods have shown encouraging performance on correspondence identification, they suffer from two shortcomings, including the inability to address non-covisibility in collaborative perception that is caused by occlusion and limited fields of view of the agents, and the inability to quantify and reduce uncertainty to improve correspondence identification. To address both issues, we propose a novel uncertainty-aware deep graph matching method for correspondence identification in collaborative perception. Our new approach formulates correspondence identification as a deep graph matching problem, which identifies correspondences based upon graph representations that are constructed from the agents' observations. We introduce a novel deep graph matching network under the Bayesian framework to explicitly quantify uncertainty in the identified correspondences. In addition, we design a novel loss function that explicitly reduces correspondence uncertainty and perceptual non-covisibility during learning. We evaluate our approach in the robotics applications of collaborative assembly and multi-robot coordination using high-fidelity simulations and physical robots. Experiments have shown that, through addressing both uncertainty and non-covisibility, our approach achieves the state-of-the-art performance of correspondence identification.

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Search and rescue under the forest canopy using multiple UAVs

Cited by 80 publications

References 79 publications

Challenges and Opportunities for Autonomous Micro-UAVs in Precision Agriculture

Challenges and Opportunities for Autonomous Micro-UAVs in Precision Agriculture

A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments

Bayesian Deep Graph Matching for Correspondence Identification in Collaborative Perception

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