The purpose of this thesis is to develop methods to generate minimum-energy consumption trajectories for a point-to-point motion under pre-defined kinematic and dynamic constraints for robotic manipulators. With respect to other trajectory optimization methods, the work presented in this thesis provides two new methods to the scientific literature. The proposed methods improve the handling of the constraints in trajectory optimization methods as well as reducing the computational complexity of redundant/hyper-redundant manipulator systems.

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The hyper redundant manipulators (HRMs) have excessively large degrees of freedom. As a special but practicable subset, the binary HRMs use binary (on-off) actuators with only two stable states such as pneumatic cylinders and solenoids. Such actuators are simple, cheap, and easy to control. Therefore, a binary HRM has been studied in this thesis. The thesis work covers the conceptual design of a spatial binary HRM together with its controlled motion simulations. The manipulator consists of many modules, each of which has the same constructive characteristics and consists of three submodules which are two cascaded variable geometry truss structures working in mutually orthogonal planes and a discrete twister. The manipulator is assumed to be powered with pneumatic on-off actuators. Because of the discrete nature of the binary actuators, a small but continuously actuated manipulator with six degrees of freedom is installed as the last module of the HRM in order to compensate the discretization errors. To solve the inverse kinematics problem of the HRM, three methods have been presented. These are the spline fitting, the extended spline fitting, and the workspace filling methods. The spline fitting method is based on forcing the spine (i.e. the center line) of the manipulator to approximate a spatial reference spline which is specified as a desired curve. In the extended spline fitting method, the result found in the first method is improved by using a genetic algorithm. In the work space filling method, the workspace of the manipulator is filled randomly with a sufficiently large finite number of discrete configurational samples. If it is desired to have concentration on a particular region of the work space, then that region is filled by using a genetic algorithm. After the filling stage, the sample closest to the desired configuration is determined by a suitable search algorithm. Finally, in order to simulate the motion of the HRM between two successive configurational steps, the equations of motions of the HRM are obtained in terms of the pressure forces generated by the binary pneumatic actuators. Then, the necessary simulations are carried out to demonstrate the performance of the HRM in some typical applications.

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