Long-Duration Autonomy for Small Rotorcraft UAS including Recharging

Brommer, Christian; Malyuta, Danylo; Hentzen, Daniel

doi:10.48550/arxiv.1810.05683

Cited by 4 publications

(4 citation statements)

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“…In addition, it allows the human supervisor and robots to share global coordinates for artifact locations relative to the DARPA-provided world frame. In order to align with the DARPA frame, Apriltags (Malyuta et al, 2019;Brommer et al, 2018;Wang and Olson, 2016), retro-reflective targets, and Leica Total Station (LTS) reflectors are attached to a gate with relative transforms to the DARPA origin frame. The global frame was assumed to be aligned with gravity, but each team was responsible for aligning yaw, and XYZ-translation from their robots to the common DARPA frame.…”

Section: Common Reference Frame Alignmentmentioning

confidence: 99%

Flexible Supervised Autonomy for Exploration in Subterranean Environments

Biggie¹,

R.²,

Riley³

et al. 2023

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While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in realworld underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission, and make high-impact strategic decisions.We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and reconfigurable communications.

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Section: Common Reference Frame Alignmentmentioning

confidence: 99%

Flexible Supervised Autonomy for Exploration in Subterranean Environments

Biggie¹,

R.²,

Riley³

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Sharing information is often more beneficial if the information shares a common reference frame. DARPA provides several options for localizing against a global reference frame including Apriltags (Malyuta et al, 2019;Brommer et 2018;Wang and Olson, 2016), generic retroreflectors, and survey reflectors. Transforms from the targets to the origin are provided by DARPA.…”

Section: Localizationmentioning

confidence: 99%

Multi-Agent Autonomy: Advancements and Challenges in Subterranean Exploration

Ohradzansky

Riley

et al. 2022

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Artificial intelligence has undergone immense growth and maturation in recent years, though autonomous systems have traditionally struggled when fielded in diverse and previously unknown environments. DARPA is seeking to change that with the Subterranean Challenge, by providing roboticists the opportunity to support civilian and military first responders in complex and high-risk underground scenarios. The subterranean domain presents a handful of challenges, such as limited communication, diverse topology and terrain, and degraded sensing. Team MARBLE proposes a solution for autonomous exploration of unknown subterranean environments in which coordinated agents search for artifacts of interest. The team presents two navigation algorithms in the form of a metric-topological graph-based planner and a continuous frontier-based planner. To facilitate multi-agent coordination, agents share and merge new map information and candidate goal points. Agents deploy communication beacons at different points in the environment, extending the range at which maps and other information can be shared. Onboard autonomy reduces the load on human supervisors, allowing agents to detect and localize artifacts and explore autonomously outside established communication networks. Given the scale, complexity, and tempo of this challenge, a range of lessons was learned, most importantly, that frequent and comprehensive field testing in representative environments is key to rapidly refining system performance.

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“…For instance, in the context of the DARPA Subterranean Challenge, the objects of interest are either artifacts (known objects of interest), or fiducial markers at known locations (only present at the entrance of the tunnel, for calibration purposes). We use YOLO [29] to detect the artifacts and the AprilTags library [30], [31], [32] to detect the fiducial markers. YOLO produces a 2D bounding box around a detected artifact; we then estimate the position of the artifact by using the bearing corresponding to the center of the bounding box and the range obtained from the depth channel of the RGB-D camera.…”

Section: Vision Front-end and Artifact Positioningmentioning

confidence: 99%

LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

Ebadi

Chang

Palieri

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

148

102

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Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and complex subterranean environments is a challenging problem. Sensors must operate in off-nominal conditions; uneven and slippery terrains make wheel odometry inaccurate, while long corridors without salient features make exteroceptive sensing ambiguous and prone to drift; finally, spurious loop closures that are frequent in environments with repetitive appearance, such as tunnels and mines, could result in a significant distortion of the entire map. These challenges are in stark contrast with the need to build highly-accurate 3D maps to support a wide variety of applications, ranging from disaster response to the exploration of underground extraterrestrial worlds. This paper reports on the implementation and testing of a lidar-based multirobot SLAM system developed in the context of the DARPA Subterranean Challenge. We present a system architecture to enhance subterranean operation, including an accurate lidarbased front-end, and a flexible and robust back-end that automatically rejects outlying loop closures. We present an extensive evaluation in large-scale, challenging subterranean environments, including the results obtained in the Tunnel Circuit of the DARPA Subterranean Challenge. Finally, we discuss potential improvements, limitations of the state of the art, and future research directions.

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Long-Duration Autonomy for Small Rotorcraft UAS including Recharging

Cited by 4 publications

References 0 publications

Flexible Supervised Autonomy for Exploration in Subterranean Environments

Flexible Supervised Autonomy for Exploration in Subterranean Environments

Multi-Agent Autonomy: Advancements and Challenges in Subterranean Exploration

LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

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