Building an autonomous and intelligent robot, which can plan its motion in a dynamic environment,has become a thrust area in robotic research. A successful use of an autonomous mobile robot depends on its motion planning scheme and controller. The control action of a manipulator (a-priori known, carefully engineered and highly predictable workspace) is comparatively easier and more stable too. However, it may be difficult to control a wheeled robot (particularly, a car-like robot) due to the reason that an exact analytical modeling is a complicated task. Car-like mobile robots are subjected to non-holonomic (non-integrable) kinematic constraints involving the time derivatives of configuration variables and dynamic constraints,which force the robot to move either in forward or backward direction only. Moreover, in a dynamic environment, the path of the robot is constrained by the partially-known movement of moving obstacles. Thus, in order to generate a collision-free path of a car-like robot during its navigation among several moving obstacles, it should have proper motion planning and obstacle avoidance schemes. It is also to be noted that in a partially-known environment, motion planning depends on the sensory information, which might be associated with imprecision and uncertainty. Therefore, in order to navigate in an uncluttered environment,the robot should have an adaptive motion planner. Both analytical as well as graph-based techniques have been used to solve the navigation problems of robots involving the static and/or moving obstacles. However, all such methods may not be suitable for on-line implementations due to their inherent computational complexity and limitations.
NIRMAL BARAN HUI,GUIDE:Dr. Dilip Kumar Pratihar Department of Mechanical Engineering Indian Iinstitute of Technology, Kharagpur 2007