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Multimodality robotic systems: Integrated combined legged-aerial mobility for subterranean search-and-rescue
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-3922-1735
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-1046-0305
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-8235-2728
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7334-4523
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2022 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 154, article id 104134Article in journal (Refereed) Published
Abstract [en]

This work presents a field-hardened autonomous multimodal legged-aerial robotic system for subterranean exploration, extending a legged robot to be the carrier of an aerial platform capable of a rapid deployment in search-and-rescue scenarios. The driving force for developing such robotic configurations are the requirements for large-scale and long-term missions, where the payload capacity and long battery life of the legged robot is combined and integrated with the agile motion of the aerial agent. The multimodal robot is structured around the quadruped Boston Dynamics Spot, enhanced with a custom configured autonomy sensor payload as well as a UAV carrier platform, while the aerial agent is a custom built quadcopter. This work presents the novel design and hardware implementation as well as the onboard sensor suites. Moreover it establishes the overall autonomy architecture in a unified supervision approach while respecting each locomotion modality, including guidance, navigation, perception, state estimation, and control capabilities with a focus on rapid deployment and efficient exploration. The robotic system complete architecture is evaluated in real subterranean tunnel areas, in multiple fully autonomous search-and-rescue missions with the goal of identifying and locating objects of interest within the subterranean environment.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 154, article id 104134
Keywords [en]
Field robotics, Search-and-rescue robotics, Unmanned aerial vehicles, Quadruped robots, Multimodality robots, Subterranean exploration
National Category
Robotics and automation
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-90608DOI: 10.1016/j.robot.2022.104134ISI: 000810432800009Scopus ID: 2-s2.0-85130338725OAI: oai:DiVA.org:ltu-90608DiVA, id: diva2:1657561
Funder
EU, Horizon 2020, 869379 illuMINEation; 101003591 NEXGEN-SIMSInterreg Nord, ROBOSOL NYPS 20202891
Note

Validerad;2022;Nivå 2;2022-06-01 (johcin)

Available from: 2022-05-11 Created: 2022-05-11 Last updated: 2025-02-09Bibliographically approved
In thesis
1. Risk Aware Path Planning and Dynamic Obstacle Avoidance towards Enabling Safe Robotic Missions
Open this publication in new window or tab >>Risk Aware Path Planning and Dynamic Obstacle Avoidance towards Enabling Safe Robotic Missions
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This compilation thesis presents two main contributions in path planning and obstacle avoidance, as well as an integration of the proposed modules with other frameworks to enable resilient robotic missions in complex environments.In general, through different types of robotic missions it is important to have a collision tolerant and reliable system, both regarding potential risks from collisions with dynamic and static obstacles, but also to secure the overall mission success.%Particularly, a common trend in the presented work is safety regarding collisions with dynamic and static obstacles, as well as reliable overall systems that are capable of executing missions.

The work included in this thesis presents the risk-aware path planner D$^*_+$ that is capable of planning traversable paths for both ground and aerial robots. D$^*_+$ is developed on top of D$^*$-lite with a risk layer close to occupied space, modeling the unknown areas as a risk, and is implemented with a dynamic map to enable updates and adjustments to a changing environment.

The risk layer aids in solving two common challenges with path planning for real robots: a) it creates a safety margin that gives free space between the path and obstacles so that robots with the corresponding size can follow the path, and b) it masks smaller holes in walls that occur when building maps from real data.

Using a dynamic map makes it possible to use D$^*_+$ for an exploration mission, it also enables for the re-planning of the path if the environment changes for example, if an obstacle suddenly blocks a path, a new path will be planned. D$^*_+$ have been tested in different real-life experiments with both an Unmanned Areal Vehicle (UAV) and a quadruped-legged robot and shown to produce traversable paths in different application scenarios, such as exploration, return to base, and navigation on known maps.

This thesis also presents an obstacle avoidance architecture for velocity objects, structured around an object detection and tracking scheme that is combined with non-linear model predictive controller (NMPC) to plan the avoidance maneuver. %that uses a Convolutional Neural Network to detect obstacles that are tracked so they can be avoided by a non-linear model predictive controller (NMPC).In this case, the detection is done with the Convoluitonal Neural Network (CNN) You Only Lock Once v4 (YOLO) where the most certain human is tracked with a Kalman filter, and the velocity of the human is estimated.The proposed scheme models the object motion as constant velocity, which is utilized from the NMPC to plan control inputs for the robot to avoid the identified obstacle. A merit of the approach is that the avoidance maneuver does not only consider the current identification position, but also considers the motion prediction of the object. This avoidance framework proved to be capable to avoid non-cooperative obstacles, such as humans moving towards it.Due to the fact that the avoidance is starting when a future collision is predicted, the avoidance maneuver is started early enough to avoid obstacles with a higher velocity than a classic ``static obstacle'' radius approach can handle.

An additional aim of this thesis is to showcase that the proposed contributions can be applied in full robotic missions/frameworks. Thus, this thesis presents a search and rescue mission with a quadruped-legged robot and a UAV on a partially known map to find the location of survivors and other objects of related interest. In this mission, the quadruped-legged robot carries the UAV to the edge of the known map from where it launches the UAV that then explores and detects any survival and other relevant objects.Also, an autonomy solution, based on Boston dynamics' quadruped-legged robot Spot, for enabling a map-based navigation in confined environments has been developed and tested. This Spot solution enables the robot to navigate to a user-selected point, rotate in the desired direction, and instruct the UAV, in the combined search and rescue mission, to take off.

Place, publisher, year, edition, pages
Luleå University of Technology, 2023
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Robotic, Path planning, Obstacle avoidanc, Robotic missions, licenti thesis
National Category
Robotics and automation
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-95349 (URN)978-91-8048-248-6 (ISBN)978-91-8048-249-3 (ISBN)
Presentation
2023-03-09, A1545, Luleå tekniska universitet, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2023-01-24 Created: 2023-01-20 Last updated: 2025-02-09Bibliographically approved

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Lindqvist, BjörnKarlsson, SamuelKoval, AntonTevetzidis, IliasHaluska, JakubKanellakis, ChristoforosNikolakopoulos, George

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