I noticed that complete_global_plan is not listed on the wiki page, and I only found it when I started to dig around in the code. It is read in as a param in teb_config.cpp. I had an issue where goal_reached_ would not return true
Right now the RRT has much less documentation than I'd like (the RRT is one of the most useful parts of our codebase, since it's modularized). We need to try to make thorough docs for the rrt.
Once we have most of the way done to this, I'll add documentation generation so we can have a RRT docs site we can link to on our README, just like robocup-software and robocup-firmware.
I created this repo for personal reference but since it is being used by quite a number of users now, I am planning to refactor the code and add proper documentation for new users. I am creating this issue to keep track of updates and collect your feedback.
Feel free to share your thoughts and suggestions here.
User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map
Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa
This repository contains the solutions to all the exercises for the MOOC about SLAM and PATH-PLANNING algorithms given by professor Claus Brenner at Leibniz University. This repository also contains my personal notes, most of them in PDF format, and many vector graphics created by myself to illustrate the theoretical concepts. Hope you enjoy it! :)
You should probably give credit to Coursera, not just the University of Toronto. Also, you should mention the source of the pictures and the slides. And have you considered sharing the answer for one of the projects could go against the terms and conditions of coursera? Please consider that.
Apart from that, Well done man! Looks a very good solution to the project! And you also did a great job
After the Man vs. Machine test, a launch file must be added and basic instructions on how to run the setup created. Basically, this instruction set should be designed so that future members will be able to recreate and improve upon the currently tested perception and drive system.
The mpc_local_planner package implements a plugin to the base_local_planner of the 2D navigation stack. It provides a generic and versatile model predictive control implementation with minimum-time and quadratic-form receding-horizon configurations.
This contains all the codes of the Robot Dynamics and Control Labs. The imlementations model various kinds of manipulators and mobile robots for position control, trajectory planning and path planning problems. The codes are written on MATLAB 2017a.
This is a ROS workspace that creates a trajectory for a UAV to follow passing through a set of given waypoints and avoiding a set of given cylindrical obstacles, using a path planning algorithm.
I noticed that
complete_global_planis not listed on the wiki page, and I only found it when I started to dig around in the code. It is read in as a param in teb_config.cpp. I had an issue wheregoal_reached_would not return true