Dynamic path planning for a mobile automaton with limited information on the environment

Lumelsky, V.; Stepanov, Alexander A.

doi:10.1109/tac.1986.1104175

Cited by 319 publications

(151 citation statements)

References 6 publications

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“…However, it is weakly dependent on the complexity of the environment since the associated relaxation algorithm depends on the number of cells in the grid and not on the specific placement of obstacles on it. Bug like approaches (Lumelsky and Skewis, 1990;Lumelsky and Stepanov, 1986), for example, focus on completeness rather than optimality and tend to deteriorate as the intricacy of the environment mounts. They guarantee to find a path if it exists but pay little attention to minimizing its length.…”

Section: Discussionmentioning

confidence: 99%

Autonomous Learning Architecture for Environmental Mapping

Silva

Idiart

Trevisan

et al. 2004

Journal of Intelligent and Robotic Systems

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Abstract.Here we propose an architecture for an autonomous mobile agent that explores while mapping a two-dimensional environment. The map is a discretized model for the localization of obstacles, on top of which a harmonic potential field is computed. The potential field serves as a fundamental link between the modeled (discrete) space and the real (continuous) space where the agent operates. It indicates safe paths towards non-explored regions. Harmonic functions were originally used as global path planners in mobile robotics. In this paper, we extend its functionality to environment exploration. We demonstrate our idea through experimental results obtained using a Nomad 200 robot platform.

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Section: Discussionmentioning

confidence: 99%

Autonomous Learning Architecture for Environmental Mapping

Silva

Idiart

Trevisan

et al. 2004

Journal of Intelligent and Robotic Systems

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“…If the environment is totally unknown, then a direct path towards the goal will be generated as if there was no obstacle. On the other hand, Euclidian DT (EDT) algorithm [9] is implemented to the partially or fully known map in order to achieve initial steering costs of the grids on the map. The static trajectory is then calculated based on the current EDT costs.…”

Section: Path-planning Phasementioning

confidence: 99%

A dynamic path planning method for wheeled mobile robots (Dyna-bug).

Karakaya

Küçükyıldız

Ocak

2017

GJCS

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In this study, a hybrid path-planning scheme is presented. The main contribution of this paper is merging the static grid costs of the global map and the immediate environmental structure of the local map. The stationary condition of the map and the instant local goal is weighted by certain coefficients in order to determine the next move of the wheeled mobile robot (WMR). Thus, the cost function is defined in terms of the grid costs and the dynamic parameters. The main assumption is that the WMR on which this scheme is executed must be equipped with a field scanning sensor. The sensor readings in each processing cycle are pre-processed before plugging in the cost function. The passages in the local map are extracted from the sensor data, then the optimal collision-free point lying on the passages is obtained via the cost function.

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“…The initial block of material is cut into individual planes, and duplication proceeds semi-independently in each plane. In each plane, the obstacle perimeter identification problem uses the bug algorithm [13]. Any module on the perimeter of the obstacle (as determined by a missing neighbor), attempts to route a message to the unoccupied lattice location.…”

Section: Formulation Of Distributed Duplicationmentioning

confidence: 99%

“…The 2D duplication algorithm uses the bug algorithm [13] for all message routing. In particular, its ability to route SENse and DUPlication messages along the face of the obstacle is crucial to the algorithm's success.…”

Section: D Duplicationmentioning

confidence: 99%

What's in the Bag: A Distributed Approach to 3D Shape Duplication with Modular Robots

Gilpin¹,

Rus²

2012

Robotics: Science and Systems VIII

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Abstract-Our goal is to develop an automated digital fabrication process that can make any object out of smart materials. In this paper, we present an algorithm for creating shapes by the process of duplication, using modules we have termed smart sand. The object to be duplicated is dipped into a bag of smart sand; the particles exchange messages to sense the object's shape; and then the particles selectively form mechanical bonds with their neighbors to form a duplicate of the original.Our algorithm is capable of duplicating convex and concave 3D objects in a completely distributed manner. It uses O(1) storage space and O(n) inter-module messages per module. We perform close to 500 experiments using a realistic simulator with over 1400 modules. These experiments confirm the functionality and messaging demands of our distributed duplication algorithm while demonstrating that the algorithm can be used to form interesting and useful shapes.

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Dynamic path planning for a mobile automaton with limited information on the environment

Cited by 319 publications

References 6 publications

Autonomous Learning Architecture for Environmental Mapping

Autonomous Learning Architecture for Environmental Mapping

A dynamic path planning method for wheeled mobile robots (Dyna-bug).

What's in the Bag: A Distributed Approach to 3D Shape Duplication with Modular Robots

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