Authors:
Diogo Amorim
and
Rodrigo Ventura
Affiliation:
Institute for Systems and Robotics, Instituto Superior Técnico and Universidade de Lisboa, Portugal
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 cel
l 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.
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