The purpose of this thesis is to design, model and control of a personal assistant robot used for domestic tasks. In order to make the robot’s design more efficient, a virtual simulation system is built using dynamic simulation software. The kinematic model is set up based on modified D-H principle. The dynamic model is built using the Lagrange theorem and elaborated in Matlab. We also employ an energy-based approach for modeling and its bond graph notation ensures encapsulation of functionality, extendibility and reusability of each element of the model. A hybrid algorithm of combining the Jacobian pseudoinverse algorithm with Rapidly-Exploring Random Tree method is presented for collision-free path planning of a redundant manipulator. An intelligent robust controller based on neural network is introduced for the coordinated control of a mobile manipulator. This method does not require an accurate model of the robot. Unknown dynamic parameters of the mobile platform and the manipulator are identified and compensated in closed-loop control using RBF neural network. A similar control algorithm is presented for coordinated force/motion control of a mobile manipulator suffering both holonomic and nonholonomic constraints. Kinematics and dynamics of a dexterous hand manipulating an object with known shape by rolling contacts are derived. A computed torque control algorithm is presented to ensure firm grip, avoid slippage and well track a given motion imposed to the object. The validation of models and different control laws were made by the co-simulation Matlab / virtual model
