UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search
and Rescue in DARPA SubT
- URL: http://arxiv.org/abs/2206.08185v1
- Date: Thu, 16 Jun 2022 13:54:33 GMT
- Title: UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search
and Rescue in DARPA SubT
- Authors: Matej Petrlik, Pavel Petracek, Vit Kratky, Tomas Musil, Yurii
Stasinchuk, Matous Vrba, Tomas Baca, Daniel Hert, Martin Pecka, Tomas
Svoboda, Martin Saska
- Abstract summary: 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.
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.
- Score: 5.145696432159643
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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-to-world 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 topological-volumetric LTVMap for allowing multi-robot
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.
Related papers
- SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems [0.0]
Multi-robot visual (RGB-D) mapping and exploration holds immense potential for application in domains such as domestic service and logistics.
There are two primary challenges: (1) the "ghosting trail" effect, which occurs due to overlapping views of robots, and (2) the oversight of visual reconstructions in selecting the most effective frontiers for exploration.
We propose a new semi-distributed framework (SPACE) for spatial cooperation in indoor environments that enables enhanced coverage and 3D mapping.
arXiv Detail & Related papers (2024-11-04T19:04:09Z) - Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue
with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems.
This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
arXiv Detail & Related papers (2023-08-14T16:48:57Z) - FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for
Egocentric multi-robot exploration [2.433860819518925]
This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration.
The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation.
The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments.
arXiv Detail & Related papers (2023-01-22T21:59:38Z) - ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and
Response with AI [55.41644538483948]
Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response operations.
We have developed the free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS.
We demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide timely predictions of catastrophic flooding events.
arXiv Detail & Related papers (2022-01-25T14:51:19Z) - Advanced Algorithms of Collision Free Navigation and Flocking for
Autonomous UAVs [0.0]
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. The complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed.
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.
arXiv Detail & Related papers (2021-10-30T03:51:40Z) - Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense
Forest Canopy [48.51396198176273]
We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments.
We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models.
A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
arXiv Detail & Related papers (2021-09-14T07:24:53Z) - Trajectory Design for UAV-Based Internet-of-Things Data Collection: A
Deep Reinforcement Learning Approach [93.67588414950656]
In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment.
We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm.
Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
arXiv Detail & Related papers (2021-07-23T03:33:29Z) - A Multi-UAV System for Exploration and Target Finding in Cluttered and
GPS-Denied Environments [68.31522961125589]
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.
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.
arXiv Detail & Related papers (2021-07-19T12:54:04Z) - Planetary UAV localization based on Multi-modal Registration with
Pre-existing Digital Terrain Model [0.5156484100374058]
We propose a multi-modal registration based SLAM algorithm, which estimates the location of a planet UAV using a nadir view camera on the UAV.
To overcome the scale and appearance difference between on-board UAV images and pre-installed digital terrain model, a theoretical model is proposed to prove that topographic features of UAV image and DEM can be correlated in frequency domain via cross power spectrum.
To test the robustness and effectiveness of the proposed localization algorithm, a new cross-source drone-based localization dataset for planetary exploration is proposed.
arXiv Detail & Related papers (2021-06-24T02:54:01Z) - OmniSLAM: Omnidirectional Localization and Dense Mapping for
Wide-baseline Multi-camera Systems [88.41004332322788]
We present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras.
For more practical and accurate reconstruction, we first introduce improved and light-weighted deep neural networks for the omnidirectional depth estimation.
We integrate our omnidirectional depth estimates into the visual odometry (VO) and add a loop closing module for global consistency.
arXiv Detail & Related papers (2020-03-18T05:52:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.