The code performance of industrial robots is typically analyzed through CPU metrics, which overlook the physical impact of code on robot behavior. This study introduces a novel framework for assessing robot program performance from an embodiment perspective by analyzing the robot’s electrical power profile. Our approach diverges from conventional CPU-based evaluations and instead leverages a suite of normalized metrics, namely, the energy utilization coefficient (fU), the energy conversion metric (fC), and the reliability coefficient (fR), to capture how efficiently and reliably energy is used during task execution. Complementing these metrics, the established robot wear metric (α) provides further insight into long-term reliability. Our approach is demonstrated through an experimental case study in machine tending, comparing four programs with diverse strategies using a UR5e robot. The proposed metrics directly compare and categorize different robot programs, regardless of the specific task, by linking code performance to its physical manifestation through power consumption patterns. Our results reveal the strengths and weaknesses of each strategy, offering actionable insights for optimizing robot programming practices. Enhancing energy efficiency and reliability through this embodiment-centric approach not only improves individual robot performance but also supports broader industrial objectives such as sustainable manufacturing and cost reduction.

Industrial robots have been widely used in diverse activities and industries for more than six decades. However, these robots were initially designed to operate autonomously without human interaction. The emergence of a new generation of manipulators, namely lightweight robots such as collaborative robots, has revolutionized the industry by enabling robots to work alongside humans. In this paper, we qualitatively compare the energy consumption of these two types of robots. First, we propose experimental setups to investigate how specific variables, such as standstill position, motion commands, velocity and acceleration limits, time scaling, and joint temperatures, influence the energy consumption of a Cobot, namely, UR3e. Then, UR3e results are compared to IR experimental results which are mainly based on existing literature. The comparison reveals that the energy signature graph, which depicts the energy consumption versus execution time, differs between these two robots. Furthermore, industrial robots consume a considerably larger amount of mechanical energy compared to their electronic components’ energy, while UR3e consumes a higher proportion of energy in their electronic components. Energy optimization strategies for UR3e should focus on efficient electronic design, such as the distribution of computation tasks among system assets, rather than reducing energy consumption through motion planning.

Compliance with global guidelines for sustainable and responsible production in modern industry requires a comparative analysis of consumer devices' energy consumption (EC). This also holds true for the newly established generation of lightweight industrial robots (LIRs). To identify potential strategies for energy optimization, standardized benchmarking procedures are required. However, to the best of the authors' knowledge, there is currently no standardized method for benchmarking the EC of manipulators. In response to this need, we have developed a comprehensive benchmarking framework to evaluate the EC of various LIR designs, delving into the theoretical power consumption under both static and dynamic conditions. Our analysis has led to the proposal of seven proposed metrics—three static and four dynamic. The static metrics—controller consumption, joint electronics consumption, and mechanical brakes' consumption—evaluate the maintenance EC of the robot. Meanwhile, we suggest three dynamic metrics that gauge the system's energy efficiency during motion, with or without payload. We extend this metrics selection by introducing the cost of transportation map for manipulators. For each of the metrics, we suggest a standardized measurement procedure based on state-of-the-art norms and literature. The metric set and experimental procedures are demonstrated using five manipulators (UR3e, UR5e, FR3, M0609, Gen3). Among the results, we can see interesting trends for future optimization of the electronic components and their architecture, e.g., reducing the robot's EC by decentralizing computation via low-consumption onboard controllers for basic tasks and external servers for complex ones.

The concept of a Digital Twin has proved its worth over the past two decades, establishing itself as a cornerstone of contemporary industry. Augmented Reality, an emerging technology, enhances the interaction between humans and machines, including computers and robots. Today, numerous examples exist of the union of these two technologies to create real-augmented digital-twin models of collaborative robots. However, these models often lack data on motor currents and power consumption. In this study, we propose a real-augmented digital-twin model that accurately estimates energy consumption. This additional energy information equips the tool for various applications such as robot optimization, commissioning, and troubleshooting. We employ our real-augmented digital-twin model to test methods for reducing Cobots’ energy consumption, using the tool to demonstrate and train Cobot practitioners on these techniques’ applications. The model is also useful for anomaly detection (troubleshooting) when the robot’s consumption statistically deviates from the ideal model. Moreover, the model can anticipate the robot’s power consumption during the commissioning phase, prior to its installation. Through a series of experiments and a practical demonstration at a robot fair for practitioners, we illustrate the benefits and training capabilities of our approach.

Human-Robot Interaction (HRI) for collaborative robots has not changed since the introduction of the first cobot. The main interface to communicate with the robot remains a wired display - teach pendant (TP). While attempts are made to make the programming experience better - more intuitive touch-screen displays, it generally remains the same. With the recent rapid development of Augmented Reality (AR), the HRI of the cobot could drastically change. This paper explores AR-based implementations in robotics and categorizes them based on the type of the used device, with the main focus on the least explored category - mobile AR. Furthermore, two experiments are conducted to determine the user’s experience in robot programming using TP with a mobile-based AR interface. For this reason, an AR application prototype is developed as a co-interface to a TP. The results of the experiments are presented: the first examines the user’s needs that are missing in current solutions, while the second one analyses the user’s experience in using the robot with the AR interface. The obtained results suggest that users could benefit from mobile-based AR solutions in the commissioning and troubleshooting phase of the lifetime of the robot. However, at the same time, this solution is not advanced and accurate enough (yet) to encourage users to switch to the new platform and abandon the classical TP, while programming the robot.

Haptic devices have been employed to immerse users in VR environments. In particular, hand and finger haptic devices have been deeply developed. However, this type of devices occludes hand detection for some tracking systems, or, for some other tracking systems, it is uncomfortable for the users to wear two different devices (haptic and tracking device) on both hands. We introduce RecyGlide, a novel wearable multimodal display located at the forearm. The RecyGlide is composed of inverted five-bar linkages with 2 degrees of freedom (DoF) and vibration motors (see Fig. 1.(a). The device provides multimodal tactile feedback such as slippage, force vector, pressure, and vibration. We tested the discrimination ability of monomodal and multimodal stimuli patterns on the forearm and confirmed that the multimodal patterns have higher recognition rate. This haptic device was used in VR applications, and we proved that it enhances VR experience and makes it more interactive.