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4 changes: 2 additions & 2 deletions PathPlanning/Dijkstra/dijkstra.py
Original file line number Diff line number Diff line change
Expand Up @@ -16,12 +16,12 @@ class DijkstraPlanner:

def __init__(self, ox, oy, resolution, robot_radius):
"""
Initialize map for a star planning
Initialize grid map for Dijkstra planning

ox: x position list of Obstacles [m]
oy: y position list of Obstacles [m]
resolution: grid resolution [m]
rr: robot radius[m]
robot_radius: robot radius [m]
"""

self.min_x = None
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3 changes: 1 addition & 2 deletions docs/modules/5_path_planning/path_planning_main.rst
Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,7 @@
Path Planning
=============

Path planning is the ability of a robot to search feasible and efficient path to the goal. The path has to satisfy some constraints based on the robot’s motion model and obstacle positions, and optimize some objective functions such as time to goal and distance to obstacle. In path planning, dynamic programming based approaches and sampling based approaches are widely used[22]. Fig.5 shows simulation results of potential field path planning and LQRRRT* path planning[27].

Path planning is the ability of a robot to search for a feasible and efficient path to the goal. The path has to satisfy some constraints based on the robot's motion model and obstacle positions, and optimize some objective functions such as time to goal and distance to obstacle. In path planning, dynamic programming based approaches and sampling based approaches are widely used[22]. Fig.5 shows simulation results of potential field path planning and LQR-RRT* path planning[27].
.. toctree::
:maxdepth: 2
:caption: Contents
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