Shyba Zaheer & Tauseef Gulrez International Journal of Robotics and Automation (IJRA), Volume (6) : Issue (1) : 2015 14 A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuration Eigenspaces Shyba Zaheer s.shyba@gmail.com Department of Electrical & Electronics Engineering T.K.M. College of Engineering Kerala, India Tauseef Gulrez gtauseef@ieee.org Virtual and Simulations of Reality (ViSOR) Lab, Department of Computing, Macqaurie University 2109 NSW, Sydney, Australia. Abstract This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software. Keywords: Free-configuration Space, Eigenvector, Motion Planning, Trajectory Planning. . 1. INTRODUCTION Motion planning is one of the most important tasks in intelligent control of an autonomous mobile robot (AMR). It is often decomposed into path-planning and trajectory planning. Path planning is referred as to generate a collision free path in an environment with obstacles. Whereas, trajectory planning schedule the movement of a mobile robot along the planned path. Based on the availability of information about environment, the path-planning algorithms are divided into two categories, namely offline and online. Offline path planning of robots in environments uses complete information about stationary obstacles and trajectory of moving obstacles, which are known in advance. This method is also known as global path planning. When complete information about environment is not available in advance, the mobile robot gets information through sensors, as it moves through the environment. This is known as online or local path planning. Essentially, online path planning begins its initial path offline but switches to online mode when it discovers new changes in obstacle scenario. Classical approaches used in online path planning are Potential Filed approach (PF), collision–cone approach, and vector field histogram (VFH) method. Khatib [1] proposed the Artificial Potential Field (APF) approach which is popular in mobile robotics. This approach is known for its mathematical elegance and simplicity