Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory

Kabir, Rabiul Hasan; Lee, Kooktae

doi:10.23919/acc50511.2021.9483227

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…The optimal control (5) will drive the robot to head toward a location where the highest intensity of gas plume is expected. This optimal control will be calculated in a receding-horizon fashion [8][9][10][11][12][13] with a given horizon length , meaning in each time step, the robot measures the gas plume using an onboard sensor, constructing a function , followed by as shown in (5).…”

Section: Resultsmentioning

confidence: 99%

Optimal Gas Leak Localization and Detection using an Autonomous Mobile Robot

Macias¹,

Lee²

2022

International Conference of Control, Dynamic Systems, and Robotics

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Section: Resultsmentioning

confidence: 99%

Optimal Gas Leak Localization and Detection using an Autonomous Mobile Robot

Macias¹,

Lee²

2022

International Conference of Control, Dynamic Systems, and Robotics

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“…Decentralized coordination strategies for multi-robot exploration are classed as explicit or implicit. Explicit coordination involves jointly optimizing plans by, for example, an auction mechanism (Burgard et al 2005; Dias et al 2006; Masaba and Quattrini Li 2021) or communicating intentions (Best et al 2019; Corah and Michael 2019; Kabir and Lee 2021). Implicit coordination involves planning locally while only communicating previous observations and odometry (Anderson and Papanikolopoulos 2008; Bautin et al 2011; Colares and Chaimowicz 2016; Petrlik et al 2023).…”

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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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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“…However, this approach is inefficient, as robots may move to the same frontier. Kabir and Lee [19] mitigate this limitation by basing their approach on optimal transport theory, while Yu et al [20] utilize a multi-robot multi-target potential field to assign robots to different frontiers. To this end, [21] and [22] coordinate the agents using a distributed assignment of single targets, while Corah and Michael [7] assign robots to groups of frontiers.…”

Section: Related Workmentioning

confidence: 99%

Fast Multi-UAV Decentralized Exploration of Forests

Bartolomei

Teixeira

Chli

2023

IEEE Robot. Autom. Lett.

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Efficient exploration strategies are vital in tasks such as search-and-rescue missions and disaster surveying. Unmanned Aerial Vehicles (UAVs) have become particularly popular in such applications, promising to cover large areas at high speeds. Moreover, with the increasing maturity of onboard UAV perception, research focus has been shifting toward higher-level reasoning for multi-robot missions. However, autonomous navigation and exploration of previously unknown large spaces still constitute an open challenge, especially when the environment is cluttered and exhibits large and frequent occlusions due to high obstacle density, as is the case of forests. Moreover, the problem of longdistance wireless communication in such scenes can become a limiting factor, especially when automating the navigation of a UAV fleet. In this spirit, this work proposes an exploration strategy that enables multiple UAVs to quickly explore complex scenes in a decentralized fashion. By providing the decisionmaking capabilities to each UAV to switch between different execution modes, the proposed strategy is shown to strike a great balance between cautious exploration of yet completely unknown regions and more aggressive exploration of smaller areas of unknown space. This results in full coverage of forest areas in multi-UAV setups up to 30% faster than the state of the art.

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Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory

Cited by 7 publications

References 15 publications

Optimal Gas Leak Localization and Detection using an Autonomous Mobile Robot

Optimal Gas Leak Localization and Detection using an Autonomous Mobile Robot

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

Fast Multi-UAV Decentralized Exploration of Forests

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