Hi, I want to use FASTER on the ground robot. And I run the "roslaunches" according to the exact procedure.
When I first click the "start" and give the goal by "2D Nave Goal", it runs well and reached the destination.
But for the next goal, it always fails and the car is swaying in the simulation environment.
Do you know the possible causes of this problem? Thanks a lot.
What's more, I'd lik
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.
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
RPDC : This contains all my MATLAB codes for the Robotics, Planning, Dynamics and Control . The implementations model various kinds of manipulators and mobile robots for position control, trajectory planning and path planning problems.
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
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! :)
Hi, I want to use FASTER on the ground robot. And I run the "roslaunches" according to the exact procedure.
When I first click the "start" and give the goal by "2D Nave Goal", it runs well and reached the destination.
But for the next goal, it always fails and the car is swaying in the simulation environment.
Do you know the possible causes of this problem? Thanks a lot.
What's more, I'd lik