Exploration and Mapping with Groups of Robots: Recent Trends

Li, Alberto Quattrini

doi:10.1007/s43154-020-00030-5

Cited by 27 publications

(10 citation statements)

References 135 publications

(123 reference statements)

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“…Classic robot exploration is posed as the problem of finding a path that results in building a map as quickly as possible of an initially unknown world (Yamauchi 1997;Quattrini Li 2020).…”

Section: Explorationmentioning

confidence: 99%

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Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy

Best,

Garg,

Keller

et al. 2023

The International Journal of Robotics Research

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We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard teams of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a generalized frontier-based exploration algorithm and an OpenVDB-based map processing pipeline. Our local planner utilizes a dynamically feasible trajectory library and a GPU-based Euclidean distance transform map to allow fast and safe navigation through both tight doorways and expansive spaces. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to three robots that fly up to 3 m/s and distances exceeding 1 km in confined spaces. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. Experiments are also detailed from the DARPA Subterranean Challenge, where our proposed autonomy pipeline contributed to us winning the “Most Sectors Explored” award. We provide an extended discussion of lessons learned, release software as open source, and present a video that illustrates our extensive field trials.

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“…Classic robot exploration is posed as the problem of finding a path that results in building a map as quickly as possible of an initially unknown world (Yamauchi 1997;Quattrini Li 2020).…”

Section: Explorationmentioning

confidence: 99%

“…Classic robot exploration is posed as the problem of finding a path that results in building a map as quickly as possible of an initially unknown world (Yamauchi 1997; Quattrini Li 2020). Decision-making algorithms for exploration are loosely characterized as information-theoretic, frontier-based, or graph search–based.…”

Section: Related Workmentioning

confidence: 99%

Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy

Best,

Garg,

Keller

et al. 2023

The International Journal of Robotics Research

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“…Relevant applications. Mobile robots can be considered as autonomous data-gathering tools, enabling scientific research in remote and hazardous environments (Li 2020;Bai et al, 2021). RIG has been successfully applied to environmental mapping and monitoring (Dunbabin and Marques 2012).…”

Section: Robotic Information Gatheringmentioning

confidence: 99%

Adaptive Robotic Information Gathering via non-stationary Gaussian processes

Chen

Khardon

Liu

2023

The International Journal of Robotics Research

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Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many applications, including but not limited to autonomous exploration and mapping, 3D reconstruction or inspection, search and rescue, and environmental monitoring. A RIG system relies on a probabilistic model’s prediction uncertainty to identify critical areas for informative data collection. Gaussian processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data is typically non-stationary—different locations do not have the same degree of variability. As a result, the prediction uncertainty does not accurately reveal prediction error, limiting the success of RIG algorithms. We propose a family of non-stationary kernels named Attentive Kernel (AK), which is simple and robust and can extend any existing kernel to a non-stationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used stationary kernels and the leading non-stationary kernels. The improved uncertainty quantification guides the downstream informative planner to collect more valuable data around the high-error area, further increasing prediction accuracy. A field experiment demonstrates that the proposed method can guide an Autonomous Surface Vehicle (ASV) to prioritize data collection in locations with significant spatial variations, enabling the model to characterize salient environmental features.

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“…The robot must traverse the environment to perceive the unknown space from different perspectives, accumulating the acquired sensor knowledge in a spatial map. OSCP is a prerequisite problem for a wide range of applications involving operation in an unknown environment, such as structural modeling and inspection, surveying, search and rescue, and many others [1].…”

Section: Introductionmentioning

confidence: 99%

“…As each action is executed, the incremental objective is recursively solved using feedback from the added environment knowledge until the global exploration objective has been achieved. 1 David Vutetakis is with the Department of Computer Science, University of North Carolina, Charlotte, NC 28223, USA.dvutetak@uncc.edu 2 Jing Xiao is with the Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA. jxiao2@wpi.edu…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Loop-Closure Approach for Simultaneous Exploration and Coverage of Unknown Infrastructure Using MAVs

Vutetakis

Xiao

2019

2019 International Conference on Robotics and Automation (ICRA)

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This work addresses the problem of online exploration and visual sensor coverage of unknown environments. We introduce a novel perception roadmap we refer to as the Active Perception Network (APN) that serves as a hierarchical topological graph describing how to traverse and perceive an incrementally built spatial map of the environment. The APN state is incrementally updated to expand a connected configuration space that extends throughout as much of the known space as possible, using efficient difference-awareness techniques that track the discrete changes of the spatial map to inform the updates. A frontier-guided approach is presented for efficient evaluation of information gain and covisible information, which guides view sampling and refinement to ensure maximum coverage of the unmapped space is maintained within the APN. The updated roadmap is hierarchically decomposed into subgraph regions which we use to facilitate a non-myopic global view sequence planner. A comparative analysis to several state-of-the-art approaches was conducted, showing significant performance improvements in terms of total exploration time and surface coverage, and demonstrating high computational efficiency that is scalable to large and complex environments.

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Exploration and Mapping with Groups of Robots: Recent Trends

Cited by 27 publications

References 135 publications

Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy

Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy

Adaptive Robotic Information Gathering via non-stationary Gaussian processes

An Autonomous Loop-Closure Approach for Simultaneous Exploration and Coverage of Unknown Infrastructure Using MAVs

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