ritikamotwani

Learning the Goal Seeking Behaviour for Mobile Robots

Abstract

Machine Learning techniques have been widely used for the navigation of mobile robots moving towards a region of interest while avoiding obstacles. Surprisingly, there is a sparingly little literature on the use of machine learning techniques for navigating the robot towards a precisely defined goal configuration amidst static and dynamic obstacles. The need to have the robot reach a precise configuration is needed for applications like placing the robot for charging, robots carrying mobile manipulation, etc. This paper takes the problem of planning motion of an autonomous robot to reach a specific goal configuration in presence of static and dynamic obstacles as a machine learning problem; and compares two approaches, namely supervised learning and

ritikamotwani

Ritika Motwani| ritikamotwani | FOSSASIA

Overview:

The New Frontend’s basic foundations have been laid in previous work during last summer of code and subsequent contributions thereafter, with CRUD functionality working for most of the APIs offered by the open event server. JSON API spec compliance led to robust integration of the API end points on the client side. However, many bugs still remain, and various APIs have not been implemented yet, or there are other mismatches in actual and expected results.

Major Areas Of My Contributions and Goals achieved:

ritikamotwani

FOSSASIA Codeheat

• Name : Ritika Motwani
• Username : ritikamotwani
• Profile link : http://github.com/ritikamotwani/

ritikamotwani

#include <stdio.h>
#include <stdlib.h>
struct listnode
{
	int val;
	struct listnode *next;
};
struct listnode *insert(struct listnode *start, int n)
{
	struct listnode *tmp;