Error Analysis and Adaptive-Robust Control of a 6-DoF Parallel Robot with Ball-Screw Drive Actuators

Parallel kinematic machines (PKMs) are commonly used for tasks that require high precision and stiffness. In this sense, the rigidity of the drive system of the robot, which is composed of actuators and transmissions, plays a fundamental role. In this paper, ball-screw drive actuators are considered and a 6-degree of freedom (DoF) parallel robot with prismatic actuated joints is used as application case. A mathematical model of the ball-screw drive is proposed considering the most influencing sources of nonlinearity: sliding-dependent flexibility, backlash, and friction. Using this model, the most critical poses of the robot with respect to the kinematic mapping of the error from the joint- to the task-space are systematically investigated to obtain the workspace positional and rotational resolution, apart from control issues. Finally, a nonlinear adaptive-robust control algorithm for trajectory tracking, based on the minimization of the tracking error, is described and simulated

Sliding Mode Control of Cable-Driven Redundancy Parallel Robot with 6 DOF Based on Cable-Length Sensor Feedback

The sliding mode control of the cable-driven redundancy parallel robot with six degrees of freedom is studied based on the cable-length sensor feedback. Under the control scheme of task space coordinates, the cable length obtained by the cable-length sensor is used to solve the forward kinematics of the cable-driven redundancy parallel robot in real-time, which is treated as the feedback for the control system. First, the method of forward kinematics of the cable-driven redundancy parallel robot is proposed based on the tetrahedron method and Levenberg-Marquardt method. Then, an iterative initial value estimation method for the Levenberg-Marquardt method is proposed. Second, the sliding mode control method based on the exponential approach law is used to control the effector of the robot, and the influence of the sliding mode parameters on control performance is simulated. Finally, a six-degree-of-freedom position tracking experiment is carried out on the principle prototype of the cable-driven redundancy parallel robot. The experimental results show that the robot can accurately track the desired position in six directions, which indicates that the control method based on the cable-length sensor feedback for the cable-driven redundancy parallel robot is effective and feasible

Adaptive Trajectory Tracking Control of a Parallel Ankle Rehabilitation Robot With Joint-Space Force Distribution

This paper proposes an adaptive trajectory tracking control strategy implemented on a parallel ankle rehabilitation robot with joint-space force distribution. This device is redundantly actuated by four pneumatic muscles (PMs) with three rotational degrees of freedom. Accurate trajectory tracking is achieved through a cascade controller with the position feedback in task space and force feedback in joint space, which enhances training safety by controlling each PM to be in tension in an appropriate level. At a high level, an adaptive algorithm is proposed to enable movement intention-directed trajectory adaptation. This can further help to improve training safety and encourage human-robot engagement. The pilot tests were conducted with an injured human ankle. The statistical data show that normalized root mean square deviation (NRMSD) values of trajectory tracking are all less than 2.3% and the PM force tracking being always controlled in tension, demonstrating its potential in assisting ankle therapy

Geometry-aware Manipulability Learning, Tracking and Transfer

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel \emph{manipulability transfer} framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.Comment: Accepted for publication in the Intl. Journal of Robotics Research (IJRR). Website: https://sites.google.com/view/manipulability. Code: https://github.com/NoemieJaquier/Manipulability. 24 pages, 20 figures, 3 tables, 4 appendice

Lightweight design and encoderless control of a miniature direct drive linear delta robot

This paper presents the design, integration and experimental validation of a miniature light-weight delta robot targeted to be used for a variety of applications including the pick-place operations, high speed precise positioning and haptic implementations. The improvements brought by the new design contain; the use of a novel light-weight joint type replacing the conventional and heavy bearing structures and realization of encoderless position measurement algorithm based on hall effect sensor outputs of direct drive linear motors. The description of mechanical, electrical and software based improvements are followed by the derivation of a sliding mode controller to handle tracking of planar closed curves represented by elliptic fourier descriptors (EFDs). The new robot is tested in experiments and the validity of the improvements are verified for practical implementation

Learning Task Priorities from Demonstrations

Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic

Spatial context-aware person-following for a domestic robot

Domestic robots are in the focus of research in terms of service providers in households and even as robotic companion that share the living space with humans. A major capability of mobile domestic robots that is joint exploration of space. One challenge to deal with this task is how could we let the robots move in space in reasonable, socially acceptable ways so that it will support interaction and communication as a part of the joint exploration. As a step towards this challenge, we have developed a context-aware following behav- ior considering these social aspects and applied these together with a multi-modal person-tracking method to switch between three basic following approaches, namely direction-following, path-following and parallel-following. These are derived from the observation of human-human following schemes and are activated depending on the current spatial context (e.g. free space) and the relative position of the interacting human. A combination of the elementary behaviors is performed in real time with our mobile robot in different environments. First experimental results are provided to demonstrate the practicability of the proposed approach

High precision motion control of parallel robots with imperfections and manufacturing tolerances

This work attempts to achieve precise motion control using parallel robots with manufacturing tolerances and inaccuracies by migrating the measurements from their joint space to task space in order to decrease control system’s sensitivity to any kinematical uncertainty rather than calibrating the parallel plant. The problem of dynamical model uncertainties and its effect on the derivation of the control law is also addressed in this work through disturbance estimation and compensation. Eventually, both task space measurement and disturbance estimation are combined to formulate a control framework that is unsensitive to either kinematical and dynamical system uncertainties