Optimization Design of Redundant Cable Driven Parallel Robots Based on Constant Stiffness Space Cable drive parallel robots (CDPRs) have a flexible structure, highly modular, and are easy to achieve rapid reorganization. To quickly recombine a highperformance CDPRs, the dimension and shape of fixed platform and endeffector is optimized with the maximum constant stiffness space (CSS) volume as the performance index in this paper. Firstly, the static model of CDPRs is established by utilizing the static equilibrium equation, and then the stiffness model and cable tension feasible region previously studied by the author are reviewed. On this basis, the concept and calculation method of the CSS of CDPRs are proposed. Then, the mutual coupling relationship between multiple optimization parameters and their influence on the optimization objective are considered comprehensively. The response surface optimization model is constructed for establishing the relationship between various optimization parameters and optimization objective, and it is solved using sample points selected by utilizing Latin hypercube design method. Finally, the influence of each optimization parameter on the CSS volume is analyzed through the response surface model, and the final optimization result is given.

Optimization based Trajectory Planning of Mobile CableDriven Parallel Robots A Mobile CableDriven Parallel Robot (MCDPR) is composed of a classical CableDriven Parallel Robot (CDPR) carried by multiple mobile bases. The additional mobilities due the motion of the mobile bases allow such systems to autonomously modify their geometric architecture, and thus make them suitable for multiple manipulation tasks in constrained environments. Moreover, these additional mobilities mean MCDPRs are kinematically redundant and may use this redundancy to optimize secondary task criteria. However, the high dimensional state space and closed chain constraints add complexity to the motion planning problem. To overcome this, we propose a method for trajectory planning for MCDPRs performing pick and place operations in cluttered environments by using direct transcription optimization. Two different scenarios have been considered and their results are validated using a dynamic simulation software (VREP) and experimentally.