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.

Control of a CableDriven Parallel Robot via Deep Reinforcement Learning Deep reinforcement learning (DRL) introduces deep neural networks to solve reinforcement learning problems. DRL is suitable for sequential decisionmaking applications and has been used to solve a variety of robot manipulation tasks recently. This paper applies a DRL algorithm named deep deterministic policy gradient (DDPG) to control a cabledriven parallel robot (CDPR) for the first time. Besides the control of a CDPR, this study investigates the ability of DDPG to learn the optimal tension distribution of the CDPR and the robustness of DDPG to certain model uncertainties of the CDPR. Simulation results show that DDPG can not only control the CDPR but also learn the optimal tension distribution of the CDPR.