Several industrial robotic applications that require high speed or high stiffness-to-inertia ratios use parallel kinematic robots. In the cases where the critical point of the application is the speed, the compliance of the main mechanical transmissions placed between the actuators and the parallel kinematic structure can be significantly higher than that of the parallel kinematic structure itself. This paper deals with this kind of system, where the overall performance depends on the maximum speed and on the dynamic behavior. Our research proposes a new approach for the investigation of the modes of vibration of the end-effector placed on the robot structure for a system where the transmission’s compliance is not negligible in relation to the flexibility of the parallel kinematic structure. The approach considers the kinematic and dynamic coupling due to the parallel kinematic structure, the system’s mass distribution and the transmission’s stiffness. In the literature, several papers deal with the dynamic vibration analysis of parallel robots. Some of these also consider the transmissions between the motors and the actuated joints. However, these works mainly deal with the modal analysis of the robot’s mechanical structure or the displacement analysis of the transmission’s effects on the positioning error of the end-effector. The discussion of the proposed approach takes into consideration a linear delta robot. The results show that the system’s natural frequencies and the directions of the end-effector’s modal displacements strongly depend on its position in the working space.

High-speed pick-and-place industrial applications often use parallel kinematic robots due to their high stiffness and dynamic performance; furthermore, the latter not only depends on the mechanical characteristics of the robots but also on the control algorithm. The literature shows several theoretical contributions to such controllers, mainly tested at the simulation level or on simple proof-of-concept laboratory equipment that execute low-speed and simple trajectories. This paper presents an experimental investigation of the dynamic performance of an industrial high-speed 4-DOF 5R parallel robot designed for pick-and-place applications on moving objects. The inverse dynamics control in the task space is used as a control algorithm. The results show the contribution of all the components of the control algorithm to the motor torque, and the inverse dynamics controller performances are discussed also in comparison to those achievable with simpler PD or PID controllers in a joint space. Moreover, the paper shows the controller synthesis from a modern mechatronic point of view, and the effectiveness of the proposed solution for the tracking of complex high-speed trajectories in an industrial application.

The ability to predict the maximal performance of an industrial robot executing non-deterministic tasks can improve process productivity through time-based planning and scheduling strategies. These strategies require the configuration and the comparison of a large number of tasks in real time for making a decision; therefore, an efficient task execution time estimation method is required. In this work, we propose the use of neural network models to approximate the task time function of a generic multi-DOF robot; the models are trained using data obtained from sophisticated motion planning algorithms that optimize the shape of the trajectory and the executed motion law, taking into account the kinematic and dynamic model of the robot. For scheduling purposes, we propose to evaluate only the neural network models, thus confining the online use of the motion planning software to the full definition of the actually scheduled task. The proposed neural network model presents a uniform interface and an implementation procedure that is easily adaptable to generic robots and tasks. The paper’s results show that the models are accurate and more efficient than the full planning pipeline, having evaluation times compatible with real-time process optimization.

The motion performances of multi-axis machinery are strongly related to the proper design of the servo axes driving the actuated joints, which must fulfil motion requests in terms of speed and torque for the motion design task. Multi-degree-of-freedom machinery highlights, at the joint level, mixed-load conditions due to high dynamic movements and external working forces. Moreover, such systems have a dynamic coupling between the axes due to their mechanical configuration. This mixed-load condition makes the servo-axes design, whose result is the selection of the couple power drive system and transmission, not a trivial procedure. This paper proposes an extensive theoretical discussion that leads to a general procedure for the direct design of the servo axes of multi-degree-of-freedom machinery subject to a mixed-load condition and dynamic coupling between the mechanical joints. The proposed procedure starts with identifying the loads required on the actuated joints without distinguishing between the different contributions. Then, in a few steps, the methodology leads to the direct choice of the PDS/transmission units through a single graphical representation where the load’s peak, thermal and speed conditions are highlighted, along with the possible PDSs and transmissions. The graphical representation proposed also allows the easy and efficient analysis of the effect of transmission selection on the torque and speed margins. Finally, the paper presents the effectiveness of the proposed procedure, applied to the design of the servo axes of a linear delta robot.

The mechatronic design approach to robotics deploys, inter alia, widely available mechanical design engineering tools that, together with standard production techniques, allow the accurate quantification of the system’s mass properties. While this enables the synthesis of model-based centralized controllers, friction still limits the achievable dynamic performances, as its prediction at the design stage is hampered by complex dependencies on loads, temperature, wear, and lubrication. Further uncertainties affecting mechatronic devices stem from the actuation systems, whose parameters are specified by the manufacturer with relatively loose accuracy. These challenges are addressed here through a method based on MEMS IMUs for the real-time estimation of both friction effects and uncertain actuator parameters. The resulting model, inclusive of the frictionless dynamics, is applied in a closed loop to improve the control performance. An experimental comparison with decentralized and non-adaptive regulators highlights severalfold reductions in tracking errors; the ability to track temperature-dependent friction variations is also shown. From this work, it may be concluded that the use of MEMS sensors, together with identification and adaptive control algorithms, sensibly increases the dynamic performance of robotic systems. The real-time properties of the method also enable future investigations into topics such as MEMS-based diagnostics and predictive maintenance.