Exploration of 3D terrains using potential fields with elevation-based local distortions

Maffei, Renan; Souza, Marcos P.; Mantelli, Mathias; Pittol, Diego; Kolberg, Mariana; Jorge, Vitor A. M.

doi:10.1109/icra40945.2020.9197577

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Due to their intuitive formulation and low computational requirements, geometrybased methods continue to be widely employed in active perception. Recent works include semantics-assisted indoor exploration [13], active 3D coverage and reconstruction [14], and Laplace potential fields for safe outdoor exploration [15].…”

Section: Related Workmentioning

confidence: 99%

Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation

Asgharivaskasi,

Atanasov

2021

Preprint

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Many robot applications call for autonomous exploration and mapping of unknown and unstructured environments. Information-based exploration techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and fast Shannon mutual information (FSMI), have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful objects, it is necessary to capture semantic categories in the measurements, map representation, and exploration objective. This work develops a Bayesian multi-class mapping algorithm utilizing rangecategory measurements. We derive a closed-form efficiently computable lower bound for the Shannon mutual information between the multi-class map and the measurements. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. We compare our method against frontier-based and FSMI exploration and apply it in a 3-D photo-realistic simulation environment.

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Section: Related Workmentioning

confidence: 99%

Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation

Asgharivaskasi,

Atanasov

2021

Preprint

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“…In [27,28], well-known closed-form solutions of the uncompressed fluid flow around simple geometries was used in order to safely drive a robot among moving obstacles. Harmonic potential fields have also been used in [29,30] to address the Simultaneous Localization and Mapping problem (SLAM) by coordinating the robot motion in unknown environments. Moreover, a methodology based on the evaluation of the harmonic potential field's streamlines was used in [31,32] for mapping a multiply connected workspace to a disk, collapsing inner obstacles to line segments or arcs.…”

Section: Introductionmentioning

confidence: 99%

Robot Navigation in Complex Workspaces Employing Harmonic Maps and Adaptive Artificial Potential Fields

Vlantis

Bechlioulis

Kyriakopoulos

2023

Sensors

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In this work, we address the single robot navigation problem within a planar and arbitrarily connected workspace. In particular, we present an algorithm that transforms any static, compact, planar workspace of arbitrary connectedness and shape to a disk, where the navigation problem can be easily solved. Our solution benefits from the fact that it only requires a fine representation of the workspace boundary (i.e., a set of points), which is easily obtained in practice via SLAM. The proposed transformation, combined with a workspace decomposition strategy that reduces the computational complexity, has been exhaustively tested and has shown excellent performance in complex workspaces. A motion control scheme is also provided for the class of non-holonomic robots with unicycle kinematics, which are commonly used in most industrial applications. Moreover, the tuning of the underlying control parameters is rather straightforward as it affects only the shape of the resulted trajectories and not the critical specifications of collision avoidance and convergence to the goal position. Finally, we validate the efficacy of the proposed navigation strategy via extensive simulations and experimental studies.

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Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observations

Asgharivaskasi

Atanasov

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Exploration of 3D terrains using potential fields with elevation-based local distortions

Cited by 5 publications

References 22 publications

Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation

Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation

Robot Navigation in Complex Workspaces Employing Harmonic Maps and Adaptive Artificial Potential Fields

Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observations

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