Ouroboros: Using potential field in unexplored regions to close loops

Jorge, Vitor A. M.; Maffei, Renan; Franco, Gabriel Silveira; Daltrozo, Jessica; Giambastiani, Mariane; Kolberg, Mariana; Prestes, Edson

doi:10.1109/icra.2015.7139479

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…Another recent continuous-domain avenue for active SLAM algorithms is the use of potential fields. Some examples are [249], which uses convolution techniques to compute entropy and select the robot's actions, and [122], which resorts to the solution of a boundary value problem.…”

Section: Active Slammentioning

confidence: 99%

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cadena¹,

Carlone²,

Carrillo³

et al. 2016

IEEE Trans. Robot.

3,141

1,742

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Abstract-Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

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Section: Active Slammentioning

confidence: 99%

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cadena¹,

Carlone²,

Carrillo³

et al. 2016

IEEE Trans. Robot.

3,141

1,742

View full text Add to dashboard Cite

show abstract

“…However, constructing such harmonic potential fields often presents numerical and computational challenges and are especially susceptible to the curse of dimensionality, scaling poorly as the size and dimensions of the workspace increase. In order to tackle the above problems, several approaches have been developed, such as restricting the necessary relevant computations of the APF on a local window around each robot [ 15 ] or multi-grid methods [ 16 ]. Nevertheless, notably, in the aforementioned HPF-based frameworks, the APF was computed by applying finite differences, thus requiring the discretization of the entire exploration domain, which results in a significant increase in the computational complexity proportional to the grid density.…”

Section: Related Workmentioning

confidence: 99%

Indoor Visual Exploration with Multi-Rotor Aerial Robotic Vehicles

Rousseas

Karras

Bechlioulis

et al. 2022

Sensors

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In this work, we develop a reactive algorithm for autonomous exploration of indoor, unknown environments for multiple autonomous multi-rotor robots. The novelty of our approach rests on a two-level control architecture comprised of an Artificial-Harmonic Potential Field (AHPF) for navigation and a low-level tracking controller. Owing to the AHPF properties, the field is provably safe while guaranteeing workspace exploration. At the same time, the low-level controller ensures safe tracking of the field through velocity commands to the drone’s attitude controller, which handles the challenging non-linear dynamics. This architecture leads to a robust framework for autonomous exploration, which is extended to a multi-agent approach for collaborative navigation. The integration of approximate techniques for AHPF acquisition further improves the computational complexity of the proposed solution. The control scheme and the technical results are validated through high-fidelity simulations, where all aspects, from sensing and dynamics to control, are incorporated, demonstrating the capacity of our method in successfully tackling the multi-agent exploration task.

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“…The proposed method applies a potential field method, which is a general method in the robotics field, for assessing the interaction between drivers [12][13][14]. A potential field method considers two types of potential energies: attractive and repulsive.…”

Section: Feature Extraction For the Interactionmentioning

confidence: 99%

Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers

et al. 2020

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In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and to decrease accident rates. For example, a warning system to alert a lane change performed by surrounding vehicles to the front space of the host vehicle can be considered. If it is possible to forecast the intention of the interrupting vehicle in advance, the host driver can easily respond to the lane change with sufficient reaction time. This paper assumes a mandatory situation where two lanes are merged. The proposed method assesses the interaction between the lane-changing vehicle and the host vehicle on the mainstream lane. Then, the lane-change goal is estimated based on the interaction under the assumption that the lane-changing driver decides to minimize the collision risk. The proposed method applies the dynamic potential field method, which changes the distribution according to the relative speed and distance between two subject vehicles, to assess the interaction. The performance of goal estimation is evaluated using real traffic data, and it is demonstrated that the estimation can be successfully performed by the proposed method.

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Ouroboros: Using potential field in unexplored regions to close loops

Cited by 11 publications

References 20 publications

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Indoor Visual Exploration with Multi-Rotor Aerial Robotic Vehicles

Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers

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