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summercamp2018

summercamp2018-1

智能系统实验室-2018新人暑期培训计划

summercamp2018homework

Homework Statistics of SummerCamp2018

teb_local_planner

An optimal trajectory planner considering distinctive topologies for mobile robots based on Timed-Elastic-Bands (ROS Package)

temporal_difference_learning_path_planning

When born, animals and humans are thrown into an unknown world forced to use their sensory inputs for survival. As they begin to understand and develop their senses they are able to navigate and interact with their environment. The process in which we learn to do this is called reinforcement learning. This is the idea that learning comes from a series of trial and error where there exists rewards and punishments for every action. The brain naturally logs these events as experiences, and decides new actions based on past experience. An action resulting in a reward will then be higher favored than an action resulting in a punishment. Using this concept, autonomous systems, such as robots, can learn about their environment in the same way. Using simulated sensory data from ultrasonic sensors, moisture sensors, encoders, shock sensors, pressure sensors, and steepness sensors, a robotic system will be able to make decisions on how to navigate through its environment to reach a goal. The robotic system will not know the source of the data or the terrain it is navigating. Given a map of an open environment simulating an area after a natural disaster, the robot will use model-free temporal difference learning with exploration to find the best path to a goal in terms of distance, safety, and terrain navigation. Two forms of temporal difference learning will be tested; off-policy (Q-Learning) and onpolicy (Sarsa). Through experimentation with several world map sizes, it is found that the off-policy algorithm, Q-Learning, is the most reliable and efficient in terms of navigating a known map with unequal states.

timed_path_follower

tld_msgs

tld_turtlebot_follower

This is a ROS package, which can make the turtlebot follow an pre-selected object. The visual tracking algorithm is TLD.

tld_turtlebot_follower-release

The release package for the ROS package tld_turtlebot_follower

toolkit-matlab

Official MATLAB toolkit for generic object tracking benchmark GOT-10k

tracker_kcf_ros

基于ros下应用深度相机的kcf追踪算法实现

tractor-trailer-trajectory-planning

traffic-sign-recognition-for-autonomous-cars

Traffic Sign Recognition for autonomous vehicles applications using ROS to control a turtlebot.

tsinghuadatastructoj

mooc-清华数据结构与算法(邓俊辉) OJ习题

turtlebot

Turtle Bot SLAM Project with Obstacle Avoidance

turtlebot-autonomous-slam-and-feature-tracking-on-ros

Turtlebot autonomous SLAM, AR tag tracking, color track, voice control

turtlebot-color-follower

Turtlebot follows color pink and if it detects an obstacle it moves around the obstacle

turtlebot-follow-person

This repo documents our work for the ECEN 631 - Robotic Vision final project at BYU. Here we use person tracking from a mono camera feed to get a TurtleBot following a person.

turtlebot-follower

Project Repo for Turtlebot Project for ECE 6562

turtlebot-navigation

turtlebot-object-follow

Customisable Turtlebot tracking behaviour, using ROS and OpenCV.

turtlebot_blob_follower

turtlebot_follower

EE 631 Project

turtlebot_follower-1

This is a ROS package for a turtlebot follower

turtlebot_guide

Meta-package for the turtlebot guide aims at developing a robot that is able to guide the follower to the destination safe.

turtlebot_lqr_student

turtlebot_nav

Turtlebot navigation and obstacle avoidance

turtlebot_navigation

This project was completed on May 15, 2015. The goal of the project was to implement software system for frontier based exploration and navigation for turtlebot-like robots.

turtlebot_parking_leap

Turtlebot for self-parking while using LeapMotion for manual control when desired and gesture inputs

turtlebotmatlab

Waypoint following with obstacle avoidance using ROS in matlab