MAV Navigation in Unknown Dark Underground Mines Using Deep Learning

Mansouri, Sina Sharif; Kanellakis, Christoforos; Karvelis, Petros; Kominiak, Dariusz; Nikolakopoulos, George

doi:10.23919/ecc51009.2020.9143842

Cited by 7 publications

(8 citation statements)

References 19 publications

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“…In response, groups around the world have proposed novel methods both for single-and multi-robot exploration. This includes techniques for quadruped robots [28], methods tailored to fast exploration using aerial platforms [27], schemes for both legged and flying systems [21], hierarchical frameworks to exploit dense local and sparse global information [45], multi-robot exploration strategies [34], approaches exploiting underground mine and cave topologies [46,47], and significant field robotics work [48][49][50][51]. Motivated by the importance of autonomous exploration and tailored to the teamed deployment of legged and flying robots inside subterranean settings, this work contributes two methods, namely on teamed exploration coordination and single-robot planning that enable resilient multi-robot teaming and reliable single-robot operation when communication to and from a robot is not possible, ability to negotiate challenging terrain and capacity to map diverse and large-scale geometries.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

Kulkarni¹,

Dharmadhikari²,

Tranzatto³

et al. 2021

Preprint

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This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying robots and enables navigation in environments with steep slopes, and diverse geometries. When a communication link is available, each robot of the team shares submaps to a centralized location where a multi-robot coordination framework identifies global frontiers of the exploration space to inform each system about where it should re-position to best continue its mission. The strategy is verified through a field deployment inside an underground mine in Switzerland using a legged and a flying robot collectively exploring for 45min, as well as a longer simulation study with three systems.

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Section: Related Workmentioning

confidence: 99%

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

Kulkarni¹,

Dharmadhikari²,

Tranzatto³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…A rather different approach based on deep learning was presented in refs. [8,15,13] for navigation in underground mines. These works suggested a low-cost UAV system design which relies only on a single camera with LED light bar.…”

Section: Related Workmentioning

confidence: 99%

“…This problem arises in many industrial applications such as navigation of flying robots through underground mines and connected tunnels, navigating small aerial vehicles in cluttered indoor environments, 3D mapping of cave networks, interior inspection of pipeline networks, search & rescue missions during disaster events in underground rail networks, etc. For example, some variants of these applications that have gained a great interest by researchers recently are dam penstocks inspection and/or mapping [1,2,3,4], chimney inspection [5], hazardous deep tunnels inspection [6,7], mapping and navigation in underground mines/tunnels [8,9,10,11,12,13,14,15,16], search & rescue in underground mines [17,18], inspection of ventilation systems [18], inspection of narrow sewer tunnels [19] and inspection tasks in the oil industry [20]. In all these applications, a UAV should navigate through a tunnel-like unknown environment while avoiding collisions with the tunnel walls.…”

Section: Introductionmentioning

confidence: 99%

A method for autonomous collision-free navigation of a quadrotor UAV in unknown tunnel-like environments

Elmokadem

Savkin

2021

Robotica

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Unmanned aerial vehicles (UAVs) have become essential tools for exploring, mapping and inspection of unknown three-dimensional (3D) tunnel-like environments which is a very challenging problem. A computationally light navigation algorithm is developed in this paper for quadrotor UAVs to autonomously guide the vehicle through such environments. It uses sensors observations to safely guide the UAV along the tunnel axis while avoiding collisions with its walls. The approach is evaluated using several computer simulations with realistic sensing models and practical implementation with a quadrotor UAV. The proposed method is also applicable to other UAV types and autonomous underwater vehicles.

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“…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%

“…A rather different approach based on deep learning was presented in [277,282,283] for navigation in underground mines. These works suggested a low-cost UAV system design which relies only on a single camera with LED light bar.…”

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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MAV Navigation in Unknown Dark Underground Mines Using Deep Learning

Cited by 7 publications

References 19 publications

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

A method for autonomous collision-free navigation of a quadrotor UAV in unknown tunnel-like environments

Advanced Algorithms of Collision Free Navigation and Flocking for Autonomous UAVs

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