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Authors: Diogo Amorim and Rodrigo Ventura

Affiliation: Institute for Systems and Robotics, Instituto Superior Técnico and Universidade de Lisboa, Portugal

ISBN: 978-989-758-123-6

Keyword(s): Path Planning, Fast Marching Method (FMM), Rapidly-exploring Random Trees (RRT), Rough Terrain.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Formal Methods ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Mobile Robots and Autonomous Systems ; Planning and Scheduling ; Robotics and Automation ; Simulation and Modeling ; Symbolic Systems

Abstract: The following paper addresses the problem of applying existing path planning methods targeting rough terrains. Most path planning methods for mobile robots divide the environment in two areas—free and occupied —and restrict the path to lie within the free space. The presented solution addresses the problem of path planning on rough terrains, where the local shape of the environment are used to both constrain and optimize the resulting path. Finding both the feasibility and the cost of the robot crossing the terrain at a given point is cast as an optimization problem. Intuitively, this problem models dropping the robot at a given location (x,y) and determining the minimal potential energy pose (attitude angles and the distance of the centre of mass to the ground). We then applied two path planning methods for computing a feasible path to a given goal: Fast Marching Method (FMM) and Rapidly exploring Random Tree (RRT). Processing the whole mapped area, determining the cost of every cell in the map, we apply a FMM in order to obtain a potential field free of local minima. This field can then be used to either pre-compute a complete trajectory to the goal point or to control, in real time, the locomotion of the robot. Solving the previously stated problem using RRT we need not to process the entire area, but only the coordinates of the nodes generated. This last approach does not require as much computational power or time as the FMM but the resulting path might not be optimal. In the end, the results obtained from the FMM may be used in controlling the vehicle and show optimal paths. The output from the RRT method is a feasible path to the goal position. Finally, we validate the proposed approach on four example environments. (More)

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Paper citation in several formats:
Amorim, D. and Ventura, R. (2015). A Physics-based Optimization Approach for Path Planning on Rough Terrains.In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-123-6, pages 259-266. DOI: 10.5220/0005529302590266

@conference{icinco15,
author={Diogo Amorim. and Rodrigo Ventura.},
title={A Physics-based Optimization Approach for Path Planning on Rough Terrains},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2015},
pages={259-266},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005529302590266},
isbn={978-989-758-123-6},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - A Physics-based Optimization Approach for Path Planning on Rough Terrains
SN - 978-989-758-123-6
AU - Amorim, D.
AU - Ventura, R.
PY - 2015
SP - 259
EP - 266
DO - 10.5220/0005529302590266

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