Motion planning of robotic arms in a cluttered environment is a computationally challenging task especially with increased number of Degrees of Freedom (DOF). Path planning and execution are two key aspects of autonomous behavior of robots. The operating environment produces great challenges in the form of obstacles which require collision avoidance between them and robot arms. Additionally, an optimal behavior is always desired in terms of energy spent, path distance or time of travel. The optimal behavior of the robots depends on the kinematics of the arm, the task to be performed, the environment it is operating in and the obstacles that needs to be encountered. Computation efficiency is very critical while operating in dynamic environments.;In this thesis, we present a novel path planning algorithm based on optimal control technique that searches for a path of manipulator in the free operational space that models the kinematics of the world. This path planner takes in the starting and target configuration from the novel real-time Inverse Kinematics (IK) algorithm developed for a general (2n+1) DOF manipulator arm. The IK algorithm uses an optimization procedure based on obstacle avoidance criterion, to produce a joint configuration for a given End Effector (EE) position and orientation defined by the task. The path planner operates on this, producing path points that not only keeps the entire arm free of collision with every obstacle in the workspace but also is optimal in terms of the additional constraints. The results are simulated and implemented on a 9-DOF hyper-redundant manipulator designed for this purpose.
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