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fix README

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Atsushi Sakai
2018-02-10 20:31:43 -08:00
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README.md
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@@ -86,7 +86,7 @@ This is a sensor fusion localization with Extended Kalman Filter(EKF).
The blue line is true trajectory, the black line is dead reckoning trajectory,
the gren point is positioning observation (ex. GPS), and the red line is estimated trajectory with EKF.
the green point is positioning observation (ex. GPS), and the red line is estimated trajectory with EKF.
The red ellipse is estimated covariance ellipse with EKF.
@@ -112,7 +112,7 @@ The blue line is true trajectory, the black line is dead reckoning trajectory,
and the red line is estimated trajectory with PF.
It is assumued that the robot can measure a distance from landmarks (RFID).
It is assumed that the robot can measure a distance from landmarks (RFID).
This measurements are used for PF localization.
@@ -126,7 +126,6 @@ This is a 2D navigation sample code with Dynamic Window Approach.
![2](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/DynamicWindowApproach/animation.gif)
## Grid based search
@@ -161,7 +160,6 @@ Ref:
- [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf)
## Model Predictive Trajectory Generator
This is a path optimization sample on model predictive trajectory generator.
@@ -180,7 +178,6 @@ Ref:
- [Optimal rough terrain trajectory generation for wheeled mobile robots](http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
　
## State Lattice Planning
@@ -259,7 +256,7 @@ Ref:
- [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416)
- [Sampling-based Algorithms for Optimal Motion Planningj](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf)
- [Sampling-based Algorithms for Optimal Motion Planning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf)
### RRT with dubins path
@@ -344,10 +341,9 @@ You can get different Beizer course:
![Bezier2](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/BezierPath/Figure_2.png?raw=True)
Ref:
- [Continuous Curvature Path Generation Based on B ́ezier Curves for Autonomous Vehicles](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf)
- [Continuous Curvature Path Generation Based on Bezier Curves for Autonomous Vehicles](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf)
## Quintic polynomials planning
@@ -477,7 +473,7 @@ Path tracking simulation with iterative linear model predictive speed and steeri
<img src="https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640">
This code uses cvxpy as an optimization modeling tool,
This code uses cvxpy as an optimization modeling tool.
- [Welcome to CVXPY 1\.0 — CVXPY 1\.0\.0 documentation](https://cvxgrp.github.io/cvxpy/index.html)
@@ -491,7 +487,3 @@ MIT
Atsushi Sakai ([@Atsushi_twi](https://twitter.com/Atsushi_twi))