This paper presents a new mobile robot design based on hybridization of the mobile platform and manipulator arm as one entity for robot locomotion as well as manipulation. The novel mechanical design is described in detail. To analyse the design, a virtual prototype tool was developed with ADAMS software for multi-body dynamic motion simulations of the complete robotic system. The simulation results were used to study the robot's mobility characteristics through animations of different possible tasks that require various locomotion and manipulation capabilities. The ability to visualize and validate various robot mobility cases and to study its functionality in the early design stages aided in optimizing the design and hence dramatically reduce physical prototype development time and cost. The design optimization process also involved proper components selection. Moreover, the simulations enabled us to define motor torque requirements and maximize end-effector payload capacity for different robot configurations.

Input shaping is a simple but effective method for vibration reduction of linear flexible systems. When systems exhibit nonlinear dynamics, the performance of an input shaper may be degraded. In this paper, a new shaper design scheme is developed for a nonlinear system which consists of a rigid robotic manipulator and a flexible sheet metal payload being grasped at several points by the robot gripper. The new scheme utilizes a parallel shaper structure based on the modal forces of payload vibration. A feedforward/feedback control law is also presented to drive the system to follow its shaped trajectory and to stabilize it at its desired position. Simulation results of a rest-to-rest robot motion control illustrate improved performance of the proposed input shaper, and verify the effectiveness of the control strategy.

The system tackled in the paper consists of a rigid robotic manipulator and a flexible shear metal payload being grasped at several points by the robot gripper. The dynamic model is first developed using the component mode synthesis method, and the vibration controllability of the flexible payload is discussed. It is concluded that some flexible states may become uncontrollable if the grasping points are located in the subspace of the payload modal nodes. In addition, given proper grasping of the payload and sufficient degrees of freedom of the robot, the flexible states are locally controllable in almost all of the robot workspace with the exception of some singular configurations. This result implies that vibration suppression control can be achieved by properly selecting the grasping points and robot moving path.

A new method aiming at reducing peak torques is developed in this paper for kinematically redundant manipulators. The method considers the contributions of both velocity and acceleration to the joint torque. Instead of minimizing the joint torque or kinetic energy at the current station, the method always uses the current torque to approach an optimum velocity of the following station, which, along with joint acceleration, minimizes the torque of the following station, while at the same time keeps the current torque within the limits. Simulations are conducted for three cases and the results show the clear superiority of the proposed method over the conventional torque optimization and energy minimization methods.

A new method aiming at reducing peak torques is developed in this paper for kinematically redundant manipulators. The method considers the contributions of both velocity and acceleration to the joint torque. Instead of minimizing the joint torque or kinetic energy at the current station, the method always uses the current torque to approach an optimum velocity of the following station, which, along with joint acceleration, minimizes the torque of the following station, while at the same time keeps the current torque within the limits. Simulations are conducted and the results show the clear superiority of the proposed method over the torque optimization method (Hollerbach & Suh 1987) and the energy minimization method (Khatib 1983).