In this work the features of the robot environment such as lane markers and obstacles are divided in to two classes using k-NN algorithm and a maximum margin hyperplane is obtained using SVM that optimally divides both the classes. This hyperplane represents a collision free path. In situations that demand both lane compliance and obstacle avoidance, the use of this technique would simplify the problem and eliminate any conflicts that may arise due to the usage of separate algorithms.
Mobile robot navigation in hilly and uneven terrain is a challenging problem. However, humans possess an uncanny knack to identify paths even in difficult terrains. Therefore, it is beneficial to use this human expert knowledge in identifying paths from start to goal. These paths can be considered as additional information to the robot to help them move towards the goal. Objective of this research was to develop a framework where the 1) Robot can make use of human expert knowledge to easily reach the goal from any start position; and 2) The robot can autonomously navigate through the terrains even in the absence of expert assistance. This research made use of reinforcement learning techniques to learn from human experts who provided rough trajectories as inputs. Fuzzy logic was further used to obtain detailed trajectory which was safe for the robot.