Modeling of a Mobile Spatial Cable Robot With Flexible Cables and Investigating the Effect of Its Nonlinear Vibrations on the System Dynamics

In this paper the modeling of a novel moving cable robot is conducted considering the vibration of the cables in its nonlinear format. The robot has 6 DOFs while the controlling input number is 12. Considering the fact that the elasticity of the cables is coupled with the dynamic model of the system, their vibration effects on the robot performance and accuracy. The target of this paper is to model the robot considering the cables’ elasticity and study its effect on the robot performance. This study can be considered in designing the controller of tower cranes and decrease the swing of the cables and increasing their stability. In order to cover the mentioned aim, the continuous vibration of the cables are modeled as a nonlinear system and it is added to the moving platform dynamics. Moreover the differences between the nonlinear modeling of the cables’ vibration and estimating them as a linear system is studied and their related results are compared and analyzed. The correctness of modeling is shown by comparing the results with previous research and the superiority of modeling the cables’ vibration in its nonlinear format is verified by the aid of a series of simulation scenarios in MATLAB. Moreover, by conducting some experimental test on the manufactured moving cable robot of IUST, it is illustrated that, modeling the cables in these robots as a nonlinear system results in more accurate results. It is shown that not only considering the cables’ vibration is significant in analyzing the robot dynamic, but also it is shown that promoting the mentioned model into nonlinear one increase the accuracy of the robot modeling which sequentially can provide a stronger controller for stabilizing and controlling the end-effector within a predefined trajectory.

Can Deep Models Help a Robot to Tune Its Controller? A Step Closer to Self-tuning Model Predictive Controllers

Motivated by the difficulty roboticists experience while tuning model predictive controllers (MPCs), we present an automated weight set tuning framework in this work. The enticing feature of the proposed methodology is the active exploration approach that adopts the exploration–exploitation concept at its core. Essentially, it extends the trial-and-error method by benefiting from the retrospective knowledge gained in previous trials, thereby resulting in a faster tuning procedure. Moreover, the tuning framework adopts a deep neural network (DNN)-based robot model to conduct the trials during the simulation tuning phase. Thanks to its high fidelity dynamics representation, a seamless sim-to-real transition is demonstrated. We compare the proposed approach with the customary manual tuning procedure through a user study wherein the users inadvertently apply various tuning methodologies based on their progressive experience with the robot. The results manifest that the proposed methodology provides a safe and time-saving framework over the manual tuning of MPC by resulting in flight-worthy weights in less than half the time. Moreover, this is the first work that presents a complete tuning framework extending from robot modeling to directly obtaining the flight-worthy weight sets to the best of the authors’ knowledge.

Advanced soft robot modeling in ChainQueen

We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

Predictive Human-in-the-Loop Simulations for Assistive Exoskeletons

Design and evaluation of exoskeletons is often a time consuming and costly process that involves prototyping, human testing, and multiple design iterations. For active exoskeletons, the primary challenge is to detect the wearer’s movement intent and provide potent assistance, which often requires sophisticated control algorithms. The goal of this study is to integrate human musculoskeletal models with robot modeling and control for virtual human-in-the-loop evaluation of exoskeleton design and control. We present potential strategies for assisting various human motions such as squatting, lifting, walking, and running. Several exoskeleton designs (for back, upper extremity, and lower extremity) and their control methods are evaluated with an integrated human-in-the-loop simulation approach to study their functionalities and biomechanical effects on the wearer’ musculoskeletal system. We hope this simulation paradigm can be utilized for virtual design and evaluation of exoskeletons and pave the way to build or optimize exoskeletons.

Stable walking of biped robot based on center of mass trajectory control

The effect of center of mass (CoM) trajectory on stable walking of biped robot was studied in this study. The method of predictive control by controlling CoM trajectory to generate stable walking patterns was discussed, which simulated the principle of adjusting center of gravity in advance during human walking to adapt to the change of road conditions. As for uncertainty of robot modeling and the influence of environment, adding variable zero moment point for variable predictive control system can achieve self-adaptive adjustment of CoM trajectory to generate stable walking patterns. The simulation experiment results indicated that walking stability of biped robot was sensitive to the change of CoM trajectory. As long as CoM trajectory was adjusted well, stable walking of biped robot can be controlled even in the presence of disturbances. It is proved that the method of predictive control by controlling CoM motion to generate stable walking pattern is consistent with reality.