Adaptive Motion Control Middleware for Teleoperation Based on Pose Tracking and Trajectory Planning

Concurrent with autonomous robots, teleoperation gains importance in industrial applications. This includes human–robot cooperation during complex or harmful operations and remote intervention. A key role in teleoperation is the ability to translate operator inputs to robot movements. Therefore, providing different motion control types is a decisive aspect due to the variety of tasks to be expected. For a wide range of use-cases, a high degree of interoperability to a variety of robot systems is required. In addition, the control input should support up-to-date Human Machine Interfaces. To address the existing challenges, we present a middleware for teleoperation of industrial robots, which is adaptive regarding motion control types. Thereby the middleware relies on an open-source, robot meta-operating system and a standardized communication. Evaluation is performed within defined tasks utilizing different articulated robots, whereby performance and determinacy are quantified. An implementation sample of the method is available on: https://github.com/FAU-FAPS/adaptive_motion_control.

Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots

Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robots to prevent fatal accidents. This paper proposes a deep learning method for identifying external collisions in 6-DoF articulated robots. The proposed method expands the idea of CollisionNet, which was previously proposed for collision detection, to identify the locations of external forces. The key contribution of this paper is uncertainty-aware knowledge distillation for improving the accuracy of a deep neural network. Sample-level uncertainties are estimated from a teacher network, and larger penalties are imposed for uncertain samples during the training of a student network. Experiments demonstrate that the proposed method is effective for improving the performance of collision identification.

Micrometer Positioning Accuracy With a Planar Parallel Continuum Robot

Parallel Continuum Robots (PCR) have several advantages over classical articulated robots, notably a large workspace, miniaturization capabilities and safe human-robot interactions. However, their low accuracy is still a serious drawback. Indeed, several conditions have to be met for PCR to reach a high accuracy, namely: a repeatable mechanical structure, a correct kinematic model, and a proper estimation of the model’s parameters. In this article, we propose a methodology that allows reaching a micrometer accuracy with a PCR. This approach emphasizes the importance of using a repeatable continuum mechanism, identifying the most influential parameters of an accurate kinematic model of the robot and precisely measuring them. The experimental results show that the proposed approach allows to reach an accuracy of 3.3 µm in position and 0.5 mrad in orientation over a 10 mm long circular path. These results push the current limits of PCR accuracy and make them good potential candidates for high accuracy automatic positioning tasks.

FPGA-Based Implementation of Torque Controller for 6-DOF Articulated Robots

This paper aims to design an impedance position-based proportional-integral-derivative (PID) controller based on forward and inverse kinematics of HIWIN RA605 articulated robot, and Field-Programmable Gate Array (FPGA) implementation for a PID torque controller. In order to control the robot, the FPGA needs to output commands to communicate with the AC servo motor drivers. An FPGA-based controller for a 6-degree-of-freedom (DOF) articulated robot that implements several tasks such as hardware implementation, encoder counters, noise cancellation algorithm, analog generators, PID controller, and communication are included, the processing time of the FPGA is 16 μs. Meanwhile, the whole process of the system only takes 82 μs to complete.

A Configurable Architecture for Two Degree-of-Freedom Variable Stiffness Actuators to Match the Compliant Behavior of Human Joints

Living beings modulate the impedance of their joints to interact proficiently, robustly, and safely with the environment. These observations inspired the design of soft articulated robots with the development of Variable Impedance and Variable Stiffness Actuators. However, designing them remains a challenging task due to their mechanical complexity, encumbrance, and weight, but also due to the different specifications that the wide range of applications requires. For instance, as prostheses or parts of humanoid systems, there is currently a need for multi-degree-of-freedom joints that have abilities similar to those of human articulations. Toward this goal, we propose a new compact and configurable design for a two-degree-of-freedom variable stiffness joint that can match the passive behavior of a human wrist and ankle. Using only three motors, this joint can control its equilibrium orientation around two perpendicular axes and its overall stiffness as a one-dimensional parameter, like the co-contraction of human muscles. The kinematic architecture builds upon a state-of-the-art rigid parallel mechanism with the addition of nonlinear elastic elements to allow the control of the stiffness. The mechanical parameters of the proposed system can be optimized to match desired passive compliant behaviors and to fit various applications (e.g., prosthetic wrists or ankles, artificial wrists, etc.). After describing the joint structure, we detail the kinetostatic analysis to derive the compliant behavior as a function of the design parameters and to prove the variable stiffness ability of the system. Besides, we provide sets of design parameters to match the passive compliance of either a human wrist or ankle. Moreover, to show the versatility of the proposed joint architecture and as guidelines for the future designer, we describe the influence of the main design parameters on the system stiffness characteristic and show the potential of the design for more complex applications.

Semi-infinite programming for trajectory optimization with non-convex obstacles

This article presents a novel optimization method that handles collision constraints with complex, non-convex 3D geometries. The optimization problem is cast as a semi-infinite program in which each collision constraint is implicitly treated as an infinite number of numeric constraints. The approach progressively generates some of these constraints for inclusion in a finite nonlinear program. Constraint generation uses an oracle to detect points of deepest penetration, and this oracle is implemented efficiently via signed distance field (SDF) versus point cloud collision detection. This approach is applied to pose optimization and trajectory optimization for both free-flying rigid bodies and articulated robots. Experiments demonstrate performance improvements compared with optimizers that handle only convex polyhedra, and demonstrate efficient collision avoidance between non-convex CAD models and point clouds in a variety of pose and trajectory optimization settings.