The NHL preseason is a thrilling time for fans, as teams gear up for another exciting season. With the puck set to drop on tomorrow's games across the USA, anticipation is at an all-time high. This article will delve into the key matchups, expert betting predictions, and what fans can expect from these preseason contests. Let's dive into the action-packed schedule and uncover some insights to enhance your viewing experience.
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Key Matchups to Watch
Tomorrow's lineup features several must-watch games that promise to deliver high-octane hockey action. Here are the top matchups that every fan should not miss:
New York Rangers vs. Boston Bruins: This clash between two storied franchises is sure to be a highlight of the preseason. Both teams have bolstered their rosters with key acquisitions, making this game a fascinating preview of potential regular-season battles.
Chicago Blackhawks vs. St. Louis Blues: With both teams looking to rebound from challenging seasons, this matchup offers a glimpse into their rebuilding efforts and emerging talent.
Calgary Flames vs. Vancouver Canucks: A battle between Western Conference rivals, this game will feature young stars eager to make their mark and prove their worth in the upcoming season.
Expert Betting Predictions
Betting on NHL preseason games can be both exciting and lucrative. While it's important to approach betting with caution, expert predictions can provide valuable insights. Here are some expert betting tips for tomorrow's games:
New York Rangers vs. Boston Bruins: Experts predict a close game, with the Bruins having a slight edge due to their depth at forward. Consider betting on the Bruins to cover the spread.
Chicago Blackhawks vs. St. Louis Blues: With both teams experimenting with line combinations, this game could go either way. However, experts suggest betting on the total goals over 5.5 due to the high-scoring potential.
Calgary Flames vs. Vancouver Canucks: The Flames are favored in this matchup, thanks to their strong defensive lineup. Betting on the Flames to win outright could be a wise choice.
In-Depth Analysis of Key Teams
To better understand tomorrow's games, let's take a closer look at some of the key teams and players involved:
New York Rangers
The Rangers have made significant offseason moves, adding depth and skill to their roster. Key players to watch include Artemi Panarin and Mika Zibanejad, who are expected to form a formidable duo down the center. The team's defensive core remains solid, with Jacob Trouba leading the charge on the blue line.
Boston Bruins
The Bruins continue to be one of the most formidable teams in the league, thanks to their deep roster and experienced leadership. Patrice Bergeron and Brad Marchand are expected to lead by example, while rookie forward Fabian Lysell is set to make his debut and showcase his scoring prowess.
Chicago Blackhawks
The Blackhawks are in a rebuilding phase, focusing on developing young talent like Kirby Dach and Adam Boqvist. Tomorrow's game against the Blues will be crucial for assessing their progress and identifying areas for improvement.
St. Louis Blues
The Blues are also in a transitional period, with new coach Craig Berube at the helm. Players like Jordan Kyrou and Pavel Buchnevich will be key in determining how quickly the team can adapt to Berube's system.
Calgary Flames
The Flames have emerged as one of the top contenders in the Western Conference last season and are looking to build on that success. Johnny Gaudreau and Elias Lindholm remain central figures, while newcomer Nazem Kadri is expected to add depth up front.
Vancouver Canucks
The Canucks are eager to bounce back after a disappointing season. Bo Horvat will be pivotal in leading the team's offense, while Quinn Hughes continues to shine on defense with his exceptional skating and playmaking abilities.
Player Spotlights
Tomorrow's games will feature several standout players who could make headlines with impressive performances:
Artemi Panarin (New York Rangers): Known for his offensive creativity and playmaking skills, Panarin is always a threat in any game he plays.
Patrice Bergeron (Boston Bruins): A perennial All-Star and two-time Selke Trophy winner, Bergeron's two-way play sets him apart from his peers.
Kirby Dach (Chicago Blackhawks): As one of the youngest players in the league, Dach has already shown flashes of brilliance and is poised for a breakout season.
Jordan Kyrou (St. Louis Blues): Kyrou has quickly established himself as a dynamic forward with his speed and scoring ability.
Nazem Kadri (Calgary Flames): Acquired in a blockbuster trade this offseason, Kadri brings grit and leadership to Calgary's lineup.
Bo Horvat (Vancouver Canucks): As captain of the Canucks, Horvat leads by example both on and off the ice with his hard work ethic and tenacity.
Tactical Insights: What To Expect From Tomorrow’s Games?
Tomorrow's NHL preseason games offer more than just entertainment; they provide valuable insights into team strategies and player development. Here are some tactical aspects fans should pay attention to:
Lineman Experimentation: Coaches will use these games as an opportunity to test different line combinations and find chemistry among players who may not have played together before.
Special Teams: Power plays and penalty kills will be crucial areas of focus as teams look to refine their strategies before heading into regular-season play.
New Additions: Fans should keep an eye on newly acquired players who are looking to make an impact early in their tenure with their new teams.
Rookie Evaluations: Many young prospects will get their first taste of NHL action during these games, providing coaches with an opportunity to assess their readiness for future roles within the organization.
Fan Engagement: How To Make The Most Of Tomorrow’s Games?
Fans attending or watching these preseason games can enhance their experience by engaging in several ways:
Social Media Interaction: Follow your favorite teams' official accounts for live updates, behind-the-scenes content, and exclusive interviews with players and coaches.
In-Stadium Experiences: If attending in person, take advantage of fan zones where you can participate in interactive activities such as shooting pucks at mini-goals or autograph sessions with current players.
Betting Pools: Participate in office or friend group betting pools for added excitement during tomorrow's games—just remember always gamble responsibly!
Predictions For Tomorrow’s Games: Who Will Come Out On Top?
Making predictions for NHL preseason games can be challenging due to varying levels of player participation and experimentation by coaches. However, based on current form and roster strength, here are some predictions for tomorrow's matchups:
New York Rangers vs Boston Bruins: Expect a closely contested battle between these two powerhouses; however, we predict that Boston edges out New York thanks largely due its depth at forward positions.
New York Rangers vs Boston Bruins Prediction: Boston wins by one goal margin.
Rationale: Boston has shown consistent depth across all lines during training camps which gives them an edge over New York despite both having strong rosters overall.
Chicago Blackhawks vs St Louis Blues Prediction: A high-scoring affair ending in overtime victory for Chicago Blackhawks.
Rationale: Both teams have young rosters filled with talented forwards capable of putting up points which could result in many goals being scored throughout regulation time before Chicago clinches victory during overtime.
Calgary Flames vs Vancouver Canucks Prediction: Calgary Flames secure a comfortable win against Vancouver Canucks.
Rationale: Calgary has been dominant during pre-season so far while Vancouver still appears unsettled under new management; combined with Calgary’s superior defensive capabilities makes them favorites here.
jimmygong1990/RoboND-DeepLearningND<|file_sep|>/catkin_ws/src/turtlebot_gazebo/scripts/gazebo_move.py
#!/usr/bin/env python
import rospy
import math
from geometry_msgs.msg import Twist
# Initialize ROS node
rospy.init_node('turtlebot_move')
# Create publisher object
pub = rospy.Publisher('cmd_vel', Twist)
rate = rospy.Rate(10)
# Create Twist message object
twist = Twist()
# Set linear velocity
twist.linear.x = .1
# Set angular velocity
twist.angular.z = .1
while not rospy.is_shutdown():
# Publish twist message
pub.publish(twist)
rospy.loginfo("Moving")
rate.sleep()<|file_sep|># RoboND-DeepLearningND
This repository contains projects done for Udacity Robotics Software Engineer Nanodegree Program - Deep Learning Nanodegree
## Project - Image Segmentation
### Goal
The goal of this project is using deep learning algorithms such as Fully Convolutional Network (FCN) or DeepLabv3+ model architecture implemented using Tensorflow/Keras library or PyTorch framework.
### Environment Setup
* Install Python3 + pip
* Install Anaconda distribution of Python3 + pip
* Install TensorFlow
* Install Keras + PyTorch
### Dataset Preparation
#### Download Cityscapes Dataset
The Cityscapes dataset contains street scenes from 50 different cities collected using seven different camera views under different weather conditions.
* Go through Cityscapes [website](https://www.cityscapes-dataset.com/) first
* Follow steps described below if you want **Train** data only:
* Register an account
* Download [cityscapes_trainvaltest.tar.gz](https://www.cityscapes-dataset.com/downloads/) (11GB)
* Extract it using command:
tar -xzf cityscapes_trainvaltest.tar.gz
* Move **leftImg8bit** folder inside **cityscapes** folder into **datasets** folder
* Follow steps described below if you want **Train**, **Validation** & **Test** data:
* Register an account
* Download [cityscapes_trainvaltest.tar.gz](https://www.cityscapes-dataset.com/downloads/) (11GB)
* Extract it using command:
tar -xzf cityscapes_trainvaltest.tar.gz
* Move **leftImg8bit**, **gtFine** & **gtCoarse** folders inside **cityscapes** folder into **datasets** folder
### Training Models
#### Train FCN Model Using Tensorflow/Keras
python train_fcn.py --dataset datasets/cityscapes --model_dir fcn_model --train_dir train_fcn --epochs_num=20 --batch_size=10 --learning_rate=0.00001 --restore_checkpoint=False --random_crop=True --num_classes=19 --img_height=512 --img_width=1024 --data_augmentation=True --optimizer=tf.keras.optimizers.Adamax(learning_rate=0.001)
#### Train DeepLabv3+ Model Using Tensorflow/Keras
python train_deeplabv3.py --dataset datasets/cityscapes --model_dir deeplabv3_model --train_dir train_deeplabv3 --epochs_num=20 --batch_size=10 --learning_rate=0.00001 --restore_checkpoint=False --random_crop=True --num_classes=19 --img_height=512 --img_width=1024 --data_augmentation=True
#### Train FCN Model Using PyTorch
python train_fcn.py
--dataset_path datasets/cityscapes
--model_dir fcn_model
--train_dir train_fcn
--epochs_num=20
--batch_size=10
--learning_rate=0.00001
--restore_checkpoint=False
--random_crop=True
--num_classes=19
--img_height=512
--img_width=1024
--data_augmentation=True
#### Train DeepLabv3+ Model Using PyTorch
python train_deeplabv3.py
--dataset_path datasets/cityscapes
--model_dir deeplabv3_model
--train_dir train_deeplabv3
--epochs_num=20
--batch_size=10
--learning_rate=0.00001
--restore_checkpoint=False
--random_crop=True
--num_classes=19
--img_height=512
--img_width=1024
### Evaluate Models
#### Evaluate FCN Model Using Tensorflow/Keras
python evaluate_fcn.py --model_dir fcn_model/fcn_model_v1/
#### Evaluate DeepLabv3+ Model Using Tensorflow/Keras
python evaluate_deeplabv3.py --model_dir deeplabv3_model/deeplabv3_model_v1/
#### Evaluate FCN Model Using PyTorch
python evaluate_fcn.py fcn_model/fcn_model_v1.pth
#### Evaluate DeepLabv3+ Model Using PyTorch
python evaluate_deeplabv3.py deeplabv3_model/deeplabv3_model_v1.pth
<|file_sep|># Project - Autonomous Robotic Vehicle using ROS Navigation Stack
## Goal
The goal of this project is building an autonomous robotic vehicle that uses ROS navigation stack along with SLAM package such as gmapping or cartographer.
## Environment Setup
### Install ROS Kinetic + Gazebo
Follow instructions given [here](http://wiki.ros.org/kinetic/Installation/Ubuntu) for installing ROS Kinetic along with Gazebo.
### Install TurtleBot Simulation Package
Follow instructions given [here](http://wiki.ros.org/turtlebot_gazebo/Tutorials) for installing turtlebot simulation package.
### Install Navigation Stack Packages
Follow instructions given [here](http://wiki.ros.org/navigation/Tutorials/Sensor%20Data%20and%20Robot%20Pose) for installing navigation stack packages.
## Build TurtleBot Simulation Environment
### Start TurtleBot Simulation
1) Start Gazebo Server
roslaunch turtlebot_gazebo turtlebot_world.launch world_file:=~/catkin_ws/src/turtlebot_gazebo/worlds/shopping_mall.world gui:=true map_file:=~/catkin_ws/src/turtlebot_navigation/maps/shopping_mall.yaml
2) Start TurtleBot Simulation
roslaunch turtlebot_gazebo turtlebot_world.launch world_file:=~/catkin_ws/src/turtlebot_gazebo/worlds/shopping_mall.world gui:=false map_file:=~/catkin_ws/src/turtlebot_navigation/maps/shopping_mall.yaml
### Start Navigation Stack Nodes
1) Start SLAM Node
roslaunch turtlebot_slam turtlebot_slam.launch slam_methods:=gmapping map_update_interval:=5 use_map_topic:=true publish_tf:=true gui_map_topic:=map publish_all_tf:=true static_map:=false use_sim_time:=false odom_frame:=odom_orig global_frame:=map robot_base_frame:=base_footprint
2) Start AMCL Node
roslaunch turtlebot_navigation amcl_demo.launch map_file:=$HOME/catkin_ws/src/turtlebot_navigation/maps/shopping_mall.yaml gui_map_topic:=map use_map_topic:=true odom_frame:=odom_orig base_frame:=base_footprint global_frame:=map publish_tf_utm:=false laser_likelihood_max_dist:=inf laser_likelihood_min_dist:=-inf laser_likelihood_holistic_coefficient:=-1 laser_likelihood_threshold:=0 laser_max_beams:=-1 max_particles:=-1 update_min_d:.05 update_min_a:.05 resample_threshold:.05 kld_err:.05 kld_z:.99 kld_scale:.01 default_pose_x:-999 default_pose_y:-999 default_pose_a:-999 initial_pose_x:-999 initial_pose_y:-999 initial_pose_a:-999 start_in_simulator:true clear_costmaps:true use_sim_time:false
3) Start Move Base Node
roslaunch turtlebot_navigation move_base_demo.launch map_file:=$HOME/catkin_ws/src/turtlebot_navigation/maps/shopping_mall.yaml planner_frequency:5 controller_frequency:10 recovery_behavior_enabled:true oscillation_reset_dist:.05 oscillation_distance_threshold:.05 sim_time:false static_map:true transform_tolerance:.5 planner_patience:5 recovery_behavior_enabled:true recovery_behavior_name:"conservative_reset" recovery_trigger_distance:.5 recovery_trigger_trans_rotation:.25 recovery_trigger_theta:.25 safe_distance:.06 controller_patience:15 publish_cost_grid:true costmap_converter_plugin:"costmap_converter::CostmapConverterROS" costmap_converter_spin_thread:false costmap_converter_rate:.8 default_obstacle_publisher_plugin:"obstacle_layer::ObstacleLayer" global_costmap_obstacle_publisher_plugin:"pointcloud_map_publisher::PointCloudMapPublisher" local_costmap_obstacle_publisher_plugin:"pointcloud_map_publisher::PointCloudMapPublisher" robot_description:"" base_local_planner_plugin:"dwa_local_planner/DWAPlannerROS" base_global_planner_plugin:"navfn/NavfnROS" base_global_planner_params:"nt.1nt.5