Autonomous Search for Underground Mine Rescue Using Aerial Robots

Dang, Tung; Mascarich, Frank; Khattak, Shehryar; Nguyen, Huan; Nguyen, Hai; Hirsh, Satchel; Reinhart, Russell; Papachristos, Christos; Alexis, Kostas

doi:10.1109/aero47225.2020.9172804

Cited by 42 publications

(43 citation statements)

References 23 publications

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“…Examples of UAV-based solutions for SAR applications in different environments and scenarios such as: remote disaster areas and wilderness SAR [ 1 , 230 , 231 , 232 ]; urban SAR (ex. collapsing buildings) [ 233 , 234 , 235 ]; underground tunnels [ 236 , 237 ]; and marine SAR [ 238 , 239 , 240 , 241 ].…”

Section: Research Challengesmentioning

confidence: 99%

Towards Fully Autonomous UAVs: A Survey

Elmokadem

Savkin

2021

Sensors

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Unmanned Aerial Vehicles have undergone rapid developments in recent decades. This has made them very popular for various military and civilian applications allowing us to reach places that were previously hard to reach in addition to saving time and lives. A highly desirable direction when developing unmanned aerial vehicles is towards achieving fully autonomous missions and performing their dedicated tasks with minimum human interaction. Thus, this paper provides a survey of some of the recent developments in the field of unmanned aerial vehicles related to safe autonomous navigation, which is a very critical component in the whole system. A great part of this paper focus on advanced methods capable of producing three-dimensional avoidance maneuvers and safe trajectories. Research challenges related to unmanned aerial vehicle development are also highlighted.

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Section: Research Challengesmentioning

confidence: 99%

Towards Fully Autonomous UAVs: A Survey

Elmokadem

Savkin

2021

Sensors

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“…This competition tests exploration algorithms in a challenging scenario as SAR missions. Among the participant teams, of particular interest are the work of Petrlik et al [21] and Dang et al [22] which decided to use YOLO Convolutional Neural Network (CNN) for the object detection of artifacts. However, considering the low visibility of this typology of SAR environments, the exploration algorithms employed are fully focused on volumetric gain rather than an objectoriented search.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Object-Oriented Exploration Algorithm for Unmanned Aerial Vehicles

Alarcon¹,

Ghavifekr

Baris

et al. 2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

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Autonomous exploration of unknown environments usually focuses on maximizing the volumetric exploration of the surroundings. Object-oriented exploration, on the other hand, tries to minimize the time spent on the localization of some given objects of interest. While the former problem equally considers map growths in any free direction, the latter fosters exploration towards objects of interest partially seen and not yet accurately identified.The proposed work relates to a novel algorithm that focuses on an object-oriented exploration of unknown environments for aerial robots, able to generate volumetric representations of surroundings, semantically enhanced by labels for each object of interest.As a case study, this method is applied both in a simulated environment and in real-life experiments on a small aerial platform.

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“…The same SLAM framework and local planning algorithm was also used in [302]. Similarly, a local planner based on the Rapidly-exploring Random Graph (RRG) algorithm is used in [303] and [284] to guide the UAV maximizing volumetric exploration gain in underground tunnels with multiple branching locations. In these works, data from range, thermal, vision and inertial sensors are fused as a part of their SLAM implementation (sometimes using only a subset of these sensors).…”

Section: Related Workmentioning

confidence: 99%

“…chimney inspection [275], hazardous deep tunnels inspection [43,276], mapping and navigation in underground mines/tunnels [12,[277][278][279][280][281][282][283], search & rescue in underground mines [284,285],…”

Section: Introductionmentioning

confidence: 99%

Advanced Algorithms of Collision Free Navigation and Flocking for Autonomous UAVs

Elmokadem¹

2021

Preprint

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Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this report contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this report deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this report addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes.

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Autonomous Search for Underground Mine Rescue Using Aerial Robots

Cited by 42 publications

References 23 publications

Towards Fully Autonomous UAVs: A Survey

Towards Fully Autonomous UAVs: A Survey

An Efficient Object-Oriented Exploration Algorithm for Unmanned Aerial Vehicles

Advanced Algorithms of Collision Free Navigation and Flocking for Autonomous UAVs

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