The kinematic analysis of redundant serial manipulators with$2n+1$revolute joints (integer$n \geq 3$), which we call circular manipulators, is presented in this article. The structure of the kinematic chain of circular manipulators has special properties that can be seen in the Denavit–Hartenberg parameters: all orthogonal distances are zero, all even-numbered offsets are zeros, but odd-numbered offsets are not. Typical manipulators that fulfill these properties are redundant 7R serial chains ($n=3$) that mimic the human arm, e.g., the lightweight robot arm KUKA LBR iiwa. This 7R circular manipulator has self-motion as rotation around an axis that goes through two fixed points for a fixed pose. First, radical reparametrization is presented based on the swivel angle of the closed-form inverse kinematics solution for the 7R circular manipulator. Second, for a six-dimensional task, the inverse kinematics solution for redundant serial manipulators with$2n+1$revolute joints ($n\geq 3$) is reparametrized by the swivel angle and other$2n-6$rotation parameters. From a geometric point of view, for a circular manipulator with$2n+1$revolute joints, one can have${n(n-1)}/{2}$choices of such circular rotations. Third, we conjecture numerical kinematic singularities for circular manipulators in a recursive formula, confirming$n=5,6,7$.

During the co-presence of human workers and robots, measures are required to avoid injuries from undesired contacts. Capacitive Proximity Sensors (CPSs) offer a cost-effective solution to cover the entire robot manipulator with fast close-range perception for HRC tasks, closing the perception gap between tactile detection and mid-range perception. CPSs do not suffer from occlusion and compared to pure tactile or force sensing, they react earlier and allow increasing the operating speed of Collaborative Robots (Cobots) while still maintaining safety. However, since capacitive coupling to obstacles varies with their distance, shape and material properties, the projection from capacitance to actual distances is a general problem. In this work, we propose an universal benchmark test procedure for fellow researchers to evaluate their CPSs. Considering ISO/TS 15066 for Power and Force Limiting (PFL) as a reference, we derive the requirements for the specified body regions and propose a method for determining the operation speed to comply with PFL based on a pre-defined detection threshold. Finally, the benchmark test procedure is evaluated on three different concepts of CPSs from the contributed researchers, demonstrating the general applicability.

Awareness of its surroundings is a crucial capability for a robot meant to be working alongside other robots or human operators. When considering safety norms and modalities, in particular the Speed and Separation Monitoring (SSM), proper proximity information can make the difference in the overall efficiency of a use case, for example avoiding unnecessary penalizations in the cycle-time. This paper presents a method to exploit the proximity perception capabilities of radar sensors to construct a continuous speed control algorithm for a UR10 robot. With respect to standard implementations of the SSM in industrial and collaborative environments, the proposed speed control is enhanced by the addition of direct human’s velocity measurement, full direction of travel and target classification. The results are evalauted according to the SSM metrics for safety and productivity, showing an overall increase in efficiency while still maintaining safety level requirements.

Formal design verification receives increased importance since the complexity of safety-critical technical applications steadily increases. In this paper, we describe an approach for symbolic model checking of collaborative robotic systems against safety properties. In particular, the methodology presented enables verification even under system modifications and adaptive behavior generally represented as variability. Architectural, behavioral and environmental models are correspondingly abstracted to reach a balance of generality and verifiability with reasonable effort. To demonstrate the proposed method, we use the model of a collaborative assembly use case and formally verify the existence of mechanical clamping hazards between the robot’s moving end effector and static environment objects.

Using the effects of quantum mechanics for computing challenges has been an often discussed topic for decades. The frequent successes and early products in this area, which we have seen in recent years, indicate that we are currently entering a new era of computing. This paradigm shift will also impact the work of robotic scientists and the applications of robotics. New possibilities as well as new approaches to known problems will enable the creation of even more powerful and intelligent robots that make use of quantum computing cloud services or co-processors. In this position paper, we discuss potential application areas and also point out open research topics in quantum computing for robotics. We go into detail on the impact of quantum computing in artificial intelligence and machine learning, sensing and perception, kinematics as well as system diagnosis. For each topic we point out where quantum computing could be applied based on results from current research.

Human attention processes play a major role in the optimization of human-robot interaction (HRI) systems. This work describes a novel methodology to measure and predict situation awareness and from this overall performance from gaze features in real-time. The awareness about scene objects of interest is described by 3D gaze analysis using data from wearable eye tracking glasses and a precise optical tracking system. A probabilistic framework of uncertainty considers coping with measurement errors in eye and position estimation. Comprehensive experiments on HRI were conducted with typical tasks including handover in a lab based prototypical manufacturing environment. The methodology is proven to predict standard measures of situation awareness (SAGAT, SART) as well as performance in the HRI task in real-time and will open new opportunities for human factors based performance optimization in HRI applications.

The control system for a mobile robot typically assumes fixed kinematics according to the drive's geometry and functionality. Faults in the system, for example a blocked steering actuator, will then lead to an undesired behaviour, unless one takes care of specific single and/or multiple faults explicitly. We present a novel model-programmed procedure for on-line kinematics reasoning that allows a robot to deduce the (inverse)-kinematics of the drive and also its kinematic abilities for the specific modes of operation and some falt modes during operation. As a consequence, we can reconfigure a robot drive to compensate for some faults and also inform a higher level control system about changed mobility capabilities of a robot. Being fault tolerant is, however, only one advantage of our approach that derives the kinematics control strategy from a geometric and functional model of the drive. We can easily adapt the controller for various robot drives, handle drives that change their geometry and functionality during run-time and also provide the basis for a flexible control scheme for self-configuring multi-robot systems.

Modern drives of mobile robots are complex machines. Because of this complexity, as well as of wear and aging of components, faults occurs in such systems quite frequently at runtime. In order to use such drives in truly autonomous robots it is desirable that the robot is able to automatically react to such faults. Therefore, the robot needs reasoning and reconfiguration capabilities in order to be able to detect, localize and repair such faults on-line. In this paper we propose a model-based diagnosis and reconfiguration framework which allows an autonomous robot to detect and compensate faults in its drive. Moreover, we present an implementation for a real robot platform. Finally, we report experimental results which shows that the proposed framework is able to correctly cope with injected faults in the drive hardware, like broken motors.

Modern automated systems evolve both continuously and discretely, and hence require estimation techniques that go well beyond the capability of a typical Kalman Filter. Multiple model (MM) estimation schemes track these system evolutions by applying a bank of filters, one for each discrete system mode. Modern systems, however, are often composed of many interconnected components that exhibit rich behaviors, due to complex, system-wide interactions. Modeling these systems leads to complex stochastic hybrid models that capture the large number of operational and failure modes. This large number of modes makes a typical MM estimation approach infeasible for online estimation. This paper analyzes the shortcomings of MM estimation, and then introduces an alternative hybrid estimation scheme that can efficiently estimate complex systems with large number of modes. It utilizes search techniques from the toolkit of model-based reasoning in order to focus the estimation on the set of most likely modes, without missing symptoms that might be hidden amongst the system noise. In addition, we present a novel approach to hybrid estimation in the presence of unknown behavioral modes. This leads to an overall hybrid estimation scheme for complex systems that robustly copes with unforeseen situations in a degraded, but fail-safe manner.

Semiquantitative simulation is an approach for the analysis of uncertain dynamic systems that performs a comprehensive simulation study based on automated reasoning methods. Semiquantitative simulation of complex models is, however, hindered by the limited automated reasoning capabilities of the currently available semiquantitative simulation techniques. The paper describes the extension of semiquantitative simulation techniques on the basis of Lyapunov methods. This extension improves automated reasoning by utilizing generalized energy functions, called Lyapunov functions. Automated reasoning based on Lyapunov functions can be seen as a generalization of the energy considerations employed by engineers. It has the advantage that it can be used to analyze systems where it does not make sense to speak about energy in the physical sense. The difficult task of deducing a Lyapunov function for the semiquantitatively modeled dynamic system is solved by reformulating methods from nonlinear control theory. A procedure for an automatic deduction of a Lyapunov function and Lyapunov-based reasoning methods using this deduced Lyapunov function are given. The improved automated reasoning capabilities of our extended SQSIM simulation platform are demonstrated by example.