Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

Kulkarni, Mihir; Dharmadhikari, Mihir; Tranzatto, Marco; Zimmermann, Samuel; Reijgwart, Victor; Petris, Paolo De; Nguyen, Huan X.; Khedekar, Nikhil; Papachristos, Christos; Ott, Lionel; Siegwart, Roland; Hutter, Marco; Alexis, Kostas

doi:10.48550/arxiv.2111.06482

Cited by 4 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The uncertainty in localization and mapping is taken into account during the planning in (Papachristos et al, 2019b) in such a way that among all trajectories arriving to the reference waypoint, the one that minimizes the expected localization and mapping uncertainty is selected. To unify the exploration framework across both legged and aerial platforms, (Kulkarni et al, 2021) have revised (Dang et al, 2020b) and added a cooperation framework that identifies global frontiers in a global graph built from the sub-maps of individual robots. The unified strategy for subterranean exploration using legged and aerial robots in tunnel and urban circuits is presented in (Tranzatto et al, 2022b).…”

Section: Darpa Subt Approachesmentioning

confidence: 99%

UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT

Petrlík¹,

Petráček²,

Krátký³

et al. 2023

View full text Add to dashboard Cite

This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology. The proposed system was ranked second in the Virtual Track of the DARPA SubT Finals as part of the team CTU-CRAS-NORLAB. In contrast to the winning solution that was developed specifically for the Virtual Track, the proposed solution also proved to be a robust system for deployment onboard physical UAVs flying in the extremely harsh and confined environment of the real-world competition. The proposed approach enables fully autonomous and decentralized deployment of a UAV team with seamless simulation-toworld transfer, and proves its advantage over less mobile UGV teams in the flyable space of diverse environments. The main contributions of the paper are present in the mapping and navigation pipelines. The mapping approach employs novel map representations—SphereMap for efficient risk-aware long-distance planning, FacetMap for surface coverage, and the compressed topologicalvolumetric LTVMap for allowing multirobot cooperation under low-bandwidth communication. These representations are used in navigation together with novel methods for visibility-constrained informed search in a general 3D environment with no assumptions about the environment structure, while balancing deep exploration with sensor-coverage exploitation. The proposed solution also includes a visual-perception pipeline for on-board detection and localization of objects of interest in four RGB stream at 5 Hz each without a dedicated GPU. Apart from participation in the DARPA SubT, the performance of the UAV system is supported by extensive experimental verification in diverse environments with both qualitative and quantitative evaluation.

show abstract

Section: Darpa Subt Approachesmentioning

confidence: 99%

UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT

Petrlík¹,

Petráček²,

Krátký³

et al. 2023

View full text Add to dashboard Cite

show abstract

“…To achieve autonomous exploration, RMF-Owl utilizes a graph-based path planner called GBPlanner2 [37]. The method builds upon our previous work [1] and uses a bifurcated local/global planning architecture.…”

Section: Exploration Path Planningmentioning

confidence: 99%

RMF-Owl: A Collision-Tolerant Flying Robot for Autonomous Subterranean Exploration

Petris¹,

Nguyen²,

Dharmadhikari³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

This work presents the design, hardware realization, autonomous exploration and object detection capabilities of RMF-Owl, a new collision-tolerant aerial robot tailored for resilient autonomous subterranean exploration. The system is custom built for underground exploration with focus on collision tolerance, resilient autonomy with robust localization and mapping, alongside high-performance exploration path planning in confined, obstacle-filled and topologically complex underground environments. Moreover, RMF-Owl offers the ability to search, detect and locate objects of interest which can be particularly useful in search and rescue missions. A series of results from field experiments are presented in order to demonstrate the system's ability to autonomously explore challenging unknown underground environments.

show abstract

“…Smooth local trajectories are constructed by solving the traveling salesman problem (TSP) for random viewpoints, while only rough routes are retained for global trajectories to improve exploration efficiency. GBPlanner2 [5] added multi-robot collaboration and handling of steep slopes to the GBPlanner and won the challenge.…”

Section: Related Workmentioning

confidence: 99%

Graph Gain: A Concave-Hull Based Volumetric Gain for Robotic Exploration

Sun¹,

Liu²,

Xu³

et al. 2022

Preprint

View full text Add to dashboard Cite

The existing volumetric gain for robotic exploration is calculated in the 3D occupancy map, while the sampling-based exploration method is extended in the reachable (free) space. The inconsistency between them makes the existing calculation of volumetric gain inappropriate for a complete exploration of the environment. To address this issue, we propose a concave-hull based volumetric gain in a sampling-based exploration framework. The concave hull is constructed based on the viewpoints generated by Rapidly-exploring Random Tree (RRT) and the nodes that fail to expand. All space outside this concave hull is considered unknown. The volumetric gain is calculated based on the viewpoints configuration rather than using the occupancy map. With the new volumetric gain, robots can avoid inefficient or even erroneous exploration behavior caused by the inappropriateness of existing volumetric gain calculation methods. Our exploration method is evaluated against the existing state-of-theart RRT-based method in a benchmark environment [1]. In the evaluated environment, the average running time of our method is about 38.4% of the existing state-of-the-art method and our method is more robust.

show abstract

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

Cited by 4 publications

References 48 publications

UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT

UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT

RMF-Owl: A Collision-Tolerant Flying Robot for Autonomous Subterranean Exploration

Graph Gain: A Concave-Hull Based Volumetric Gain for Robotic Exploration

Contact Info

Product

Resources

About