D*+: A Risk Aware Platform Agnostic Heterogeneous Path Planner

Karlsson, Samuel; Koval, Anton; Kanellakis, Christoforos; Nikolakopoulos, George

doi:10.2139/ssrn.4137561

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Problems with these methods can arise when you desire to have large safety marginals to promote as safe as possible navigation, but there still are occasions where the robot must violate those outer margins to reach its destination. In this work, we are relying on our own developed algorithm denoted as D * + (Karlsson et al, 2022). D * + is an extension of D * -lite (Koenig and Likhachev, 2002), where we add proximity risk costs to occupied voxels and treat unknown space (e.g.…”

Section: Path Planning and Navigationmentioning

confidence: 99%

“…The pose xinit initializes the FAST-LIO (Xu and Zhang, 2021) LiDAR-Intertial Odometry in the global map frame. The GUI operator sends the inspection waypoint list W P to a mission planner, which queries the D * + (Karlsson et al, 2022) path planner to plan a risk-aware path P to the next inspection point W Pi and P is segmented into pose references as P = [P1, P2, . .…”

Section: Routine Inspection Autonomy (Ria)mentioning

confidence: 99%

See 1 more Smart Citation

Safety Inspections and Gas Monitoring in Hazardous Mining Areas Shortly After Blasting Using Autonomous UAVs

Nordström,

Stathoulopoulos,

Dahlquist

et al. 2024

Preprint

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This article presents the first ever fully autonomous UAV (Unmanned Aerial Vehicle) mission to perform gas measurements after a real blast in an underground mine. The demonstration mission was deployed around 40 minutes after the blast took place, and as such realistic gas levels were measured. We also present multiple field robotics experiments in different mines detailing the development process. The presented novel autonomy stack, denoted as the Routine Inspection Autonomy (RIA) framework, combines a risk-aware 3D path planning D + ∗ , with 3D LiDAR-based global relocalization on a known map, and it is integrated on a custom hardware and a sensing stack with an onboard gas sensing device. In the presented framework, the autonomous UAV can be deployed in incredibly harsh conditions (dust, significant deformations of the map) shortly after blasting to perform inspections of lingering gases that present a significant safety risk to workers. We also present a change detection framework that can extract and visualize the areas that were changed in the blasting procedure, a critical parameter for planning the extraction of materials, and for updating existing mine maps. As will be demonstrated, the RIA stack can enable robust autonomy in harsh conditions, and provides reliable and safe navigation behavior for autonomous Routine Inspection missions.

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Section: Path Planning and Navigationmentioning

confidence: 99%

Section: Routine Inspection Autonomy (Ria)mentioning

confidence: 99%

Safety Inspections and Gas Monitoring in Hazardous Mining Areas Shortly After Blasting Using Autonomous UAVs

Nordström,

Stathoulopoulos,

Dahlquist

et al. 2024

Preprint

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show abstract

REF: A Rapid Exploration Framework for Deploying Autonomous MAVs in Unknown Environments

Patel

Lindqvist

Kanellakis

et al. 2023

J Intell Robot Syst

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Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present a complete framework for deploying Micro Aerial Vehicles (MAVs) in autonomous exploration missions in unknown subterranean areas. The main motive of exploration algorithms is to depict the next best frontier for the MAV such that new ground can be covered in a fast, safe yet efficient manner. The proposed framework uses a novel frontier selection method that also contributes to the safe navigation of autonomous MAVs in obstructed areas such as subterranean caves, mines, and urban areas. The framework presented in this work bifurcates the exploration problem in local and global exploration. The proposed exploration framework is also adaptable according to computational resources available onboard the MAV which means the trade-off between the speed of exploration and the quality of the map can be made. Such capability allows the proposed framework to be deployed in subterranean exploration and mapping as well as in fast search and rescue scenarios. The performance of the proposed framework is evaluated in detailed simulation studies with comparisons made against a high-level exploration-planning framework developed for the DARPA Sub-T challenge as it will be presented in this article.

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D*+: A Risk Aware Platform Agnostic Heterogeneous Path Planner

Cited by 2 publications

References 32 publications

Safety Inspections and Gas Monitoring in Hazardous Mining Areas Shortly After Blasting Using Autonomous UAVs

Safety Inspections and Gas Monitoring in Hazardous Mining Areas Shortly After Blasting Using Autonomous UAVs

REF: A Rapid Exploration Framework for Deploying Autonomous MAVs in Unknown Environments

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