From dd47c2dcd405893448f61ad3fb332fae34632e26 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 10 Feb 2018 20:31:43 -0800 Subject: [PATCH] fix README --- README.md | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 6e98ca8d..36c07141 100644 --- a/README.md +++ b/README.md @@ -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 -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)) - - - -