Dynamic modeling and tracking control of a nonholonomic wheeled mobile manipulator with dual arms

This paper presents methodologies for dynamic modeling and trajectory tracking of a nonholonomic wheeled mobile manipulator (WMM) with dual arms. The complete dynamic model of such a manipulator is easily established using the Lagrange's equation and MATHEMATICA. The structural properties of the overall system along with its subsystems are also well investigated and then exploited in further controller synthesis. The derived model is shown valid by reducing it to agree well with the mobile platform model. In order to solve the path tracking control problem of the wheeled mobile manipulator, a novel kinematic control scheme is proposed to deal with the nonholonomic constraints. With the backstepping technique and the filtered-error method, the nonlinear tracking control laws for the mobile manipulator system are constructed based on the Lyapunov stability theory. The proposed control scheme not only achieves simultaneous trajectory and velocity tracking, but also compensates for the dynamic interactions caused by the motions of the mobile platform and the two onboard manipulators. Simulation results are performed to illustrate the efficacy of the proposed control strategy

Hierarchical Task Planning for Multiarm Robot with Multiconstraint

Multiarm systems become the trends of space robots, for the on-orbit servicing missions are becoming more complex and various. A hierarchical task planning method with multiconstraint for multiarm space robot is presented in this paper. The process of task planning is separated into two hierarchies: mission profile analysis and task node planning. In mission profile analysis, several kinds of primitive tasks and operators are defined. Then, a complex task can be decomposed into a sequence of primitive tasks by using hierarchical task network (HTN) with those primitive tasks and operators. In task node planning, A⁎ algorithm is improved to adapt the continuous motion of manipulator. Then, some of the primitive tasks which cannot be executed directly because of constraints are further decomposed into several task nodes by using improved A⁎ algorithm. Finally, manipulators execute the task by moving from one node to another with a simple path plan algorithm. The feasibility and effectiveness of the proposed task planning method are verified by simulation

Fuzzy optimisation based symbolic grounding for service robots

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophySymbolic grounding is a bridge between task level planning and actual robot sensing and actuation. Uncertainties raised by unstructured environments make a bottleneck for integrating traditional artificial intelligence with service robotics. In this research, a fuzzy optimisation based symbolic grounding approach is presented. This approach can handle uncertainties and helps service robots to determine the most comfortable base region for grasping objects in a fetch and carry task. Novel techniques are applied to establish fuzzy objective function, to model fuzzy constraints and to perform fuzzy optimisation. The approach does not have the short comings of others’ work and the computation time is dramatically reduced in compare with other methods. The advantages of the proposed fuzzy optimisation based approach are evidenced by experiments that were undertaken in Care-O-bot 3 (COB 3) and Robot Operating System (ROS) platforms