823 research outputs found
System Development of an Unmanned Ground Vehicle and Implementation of an Autonomous Navigation Module in a Mine Environment
There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine the structural integrity of the roof and pillars through the generation of high-fidelity 3D maps. The generation of the maps will allow the miners to rapidly respond to any increasing hazards with proactive measures such as: sending workers to build/rebuild support structure to prevent accidents. The objective of this research is the development, implementation and testing of a robust unmanned ground vehicle (UGV) that will operate in mine environments for extended periods of time. To achieve this, a custom skid-steer four-wheeled UGV is designed to operate in these challenging underground mine environments. To autonomously navigate these environments, the UGV employs the use of a Light Detection and Ranging (LiDAR) and tactical grade inertial measurement unit (IMU) for the localization and mapping through a tightly-coupled LiDAR Inertial Odometry via Smoothing and Mapping framework (LIO-SAM). The autonomous navigation module was implemented based upon the Fast likelihood-based collision avoidance with an extension to human-guided navigation and a terrain traversability analysis framework. In order to successfully operate and generate high-fidelity 3D maps, the system was rigorously tested in different environments and terrain to verify its robustness. To assess the capabilities, several localization, mapping and autonomous navigation missions were carried out in a coal mine environment. These tests allowed for the verification and tuning of the system to be able to successfully autonomously navigate and generate high-fidelity maps
Aerial Field Robotics
Aerial field robotics research represents the domain of study that aims to
equip unmanned aerial vehicles - and as it pertains to this chapter,
specifically Micro Aerial Vehicles (MAVs)- with the ability to operate in
real-life environments that present challenges to safe navigation. We present
the key elements of autonomy for MAVs that are resilient to collisions and
sensing degradation, while operating under constrained computational resources.
We overview aspects of the state of the art, outline bottlenecks to resilient
navigation autonomy, and overview the field-readiness of MAVs. We conclude with
notable contributions and discuss considerations for future research that are
essential for resilience in aerial robotics.Comment: Accepted in the Encyclopedia of Robotics, Springe
Flexible Supervised Autonomy for Exploration in Subterranean Environments
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 real-world 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
re-configurable communications.Comment: Field Robotics special issue: DARPA Subterranean Challenge,
Advancement and Lessons Learned from the Final
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
This article proposes a novel unsupervised learning framework for detecting
the number of tunnel junctions in subterranean environments based on acquired
2D point clouds. The implementation of the framework provides valuable
information for high level mission planners to navigate an aerial platform in
unknown areas or robot homing missions. The framework utilizes spectral
clustering, which is capable of uncovering hidden structures from connected
data points lying on non-linear manifolds. The spectral clustering algorithm
computes a spectral embedding of the original 2D point cloud by utilizing the
eigen decomposition of a matrix that is derived from the pairwise similarities
of these points. We validate the developed framework using multiple data-sets,
collected from multiple realistic simulations, as well as from real flights in
underground environments, demonstrating the performance and merits of the
proposed methodology
NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology,
under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and
Defense Advanced Research Projects Agency (DARPA)
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