Design of an Autonomous Robot for Mapping, Navigation, and Manipulation in Underground Mines

Robert, Losch; Grehl, Steve; Donner, Marc; Buhl, C; Jung, Bernhard

doi:10.1109/iros.2018.8594190

Cited by 36 publications

(28 citation statements)

References 7 publications

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“…Motion Distortion Correction: We assume information from one or more 360 degree lidar sensors, such as the Velodyne Puck or Ouster lidars. The raw information coming from the lidar is fed into a motion distortion correction (MDC) unit which corrects the Cartesian position of each point to account for the motion of the robot while a single scan of the lidar is completed 1 . This correction is particularly important for points at large range, when high-rate rotations are experienced by the robot, and is a commonly employed step [17], [20].…”

Section: A Point Cloud Preprocessormentioning

confidence: 99%

“…Robotic systems are rapidly entering in all aspects of human life. In particular, robots are being deployed in increasingly complex environments for a broad spectrum of applications ranging from mining [1] and search-and-rescue [2], to industrial monitoring [3] and planetary exploration [4]. In these scenarios, darkness, presence of obscurants (e.g.…”

Section: Introductionmentioning

confidence: 99%

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LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time

Palieri

Morrell

Thakur

et al. 2021

IEEE Robot. Autom. Lett.

108

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A reliable odometry source is a prerequisite to enable complex autonomy behaviour in next-generation robots operating in extreme environments. In this work, we present a high-precision lidar odometry system to achieve robust and real-time operation under challenging perceptual conditions. LOCUS (Lidar Odometry for Consistent operation in Uncertain Settings), provides an accurate multi-stage scan matching unit equipped with an health-aware sensor integration module for seamless fusion of additional sensing modalities. We evaluate the performance of the proposed system against state-ofthe-art techniques in perceptually challenging environments, and demonstrate top-class localization accuracy along with substantial improvements in robustness to sensor failures. We then demonstrate real-time performance of LOCUS on various types of robotic mobility platforms involved in the autonomous exploration of the Satsop power plant in Elma, WA where the proposed system was a key element of the CoSTAR team's solution that won first place in the Urban Circuit of the DARPA Subterranean Challenge.

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Section: A Point Cloud Preprocessormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time

Palieri

Morrell

Thakur

et al. 2021

IEEE Robot. Autom. Lett.

108

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show abstract

“…From a system's perspective, the works in Morris et al (2006) amd Novák et al (2015) overview innovations in ground and submersible subterranean robotics, while the paper in Tardioli et al (2019) provides an overview on the use of ground robots underground. Furthermore, the methods in Bakambu and Polotski (2007) and Lösch et al (2018) outline navigation solutions for robotic rovers underground.…”

Section: Related Workmentioning

confidence: 99%

Graph‐based subterranean exploration path planning using aerial and legged robots

Dang

Tranzatto

Khattak

et al. 2020

Journal of Field Robotics

148

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Autonomous exploration of subterranean environments remains a major challenge for robotic systems. In response, this paper contributes a novel graph-based subterranean exploration path planning method that is attuned to key topological properties of subterranean settings, such as large-scale tunnel-like networks and complex multibranched topologies. Designed both for aerial and legged robots, the proposed method is structured around a bifurcated local-and global-planner architecture. The local planner utilizes a rapidly exploring random graph to reliably and efficiently identify paths that optimize an exploration gain within a local subspace, while simultaneously avoiding obstacles, respecting applicable traversability constraints and honoring dynamic limitations of the robots. Reflecting the fact that multibranched and tunnel-like networks of underground environments can often lead to dead-ends and accounting for the robot endurance, the global planning layer works in conjunction with the local planner to incrementally build a sparse global graph and is engaged when the system must be repositioned to a previously identified frontier of the exploration space, or commanded to return-to-home. The designed planner is detailed with respect to its computational complexity and compared against state-of-the-art approaches. Emphasizing field experimentation, the method is evaluated within multiple real-life deployments using aerial robots and the ANYmal legged system inside both long-wall and room-and-pillar underground mines in the United States and in Switzerland, as well as inside an underground bunker. The presented results further include missions conducted within the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, a relevant competition on underground exploration.

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“…External disturbances, parametric uncertainty and un-modelled dynamics are other challenges which should be taken into consideration. To solve these problems, model-free control approaches have been presented (Karayiannidis et al, 2016; Lösch et al, 2018; Meng et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Observer-based adaptive control of robot manipulators using reinforcement learning and the Fourier series expansion

Khodamipour

Khorashadizadeh

Farshad

2021

Transactions of the Institute of Measurement and Control

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Designing observer-controller structures for nonlinear system with unknown dynamics such as robotic systems is among popular research fields in control engineering. The novelty of this paper is in presenting an observer-based model-free controller for robot manipulators using reinforcement learning (RL). The proposed controller calculates the desired motor voltages that fulfil a satisfactory tracking performance. Moreover, the uncertainties and nonlinearities in the observer model and RL controller are estimated and compensated for by using the Fourier series expansion. Simulation results and comparison with the previous related works (extended state observer and radial basis function neural networks) indicate the satisfactory performance of the proposed method.

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Design of an Autonomous Robot for Mapping, Navigation, and Manipulation in Underground Mines

Cited by 36 publications

References 7 publications

LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time

LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time

Graph‐based subterranean exploration path planning using aerial and legged robots

Observer-based adaptive control of robot manipulators using reinforcement learning and the Fourier series expansion

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