Reducing the energy consumption of industrial robots performing pick-and-place tasks is required to increase profitability while reducing carbon footprint. Natural motion stands out as a mixed-energy-reduction strategy, especially useful for cyclical tasks. An optimization approach is proposed for calculating the elastic parameters, namely the stiffness and equilibrium position, of constant-stiffness springs parallel to the actuators of parallel robots. Three typical trajectory-dependent methods for calculating these parameters are presented: free-vibration response, optimized, and predefined trajectory. As the set of springs and the task specification are strongly coupled, deviations from the nominal task would require replacing or removing the springs. Therefore, two adjustment strategies, one based on trajectory optimization and the other on equilibrium position update, are proposed to further exploit the natural motion. All optimization problems are solved and compared in a case study of a five-bar linkage performing a nominal pick-and-place task. Then, a palletizing pick-and-place scenario is introduced to perform the proposed trajectory and equilibrium adjustments. It is shown that using nominal springs reduces energy consumption near the nominal task, and implementing the proposed adjustments reduces energy over a wider region.

For manufacturing processes in industries such as aerospace, automotive and electronics, it is essential for robots to perform pick-and-place tasks with high efficiency and accuracy. To this end, we propose a data-driven framework to generate a pick-and-place trajectory that ensures high-accuracy tracking while simultaneously reducing residual vibration, which is particularly valuable for commercial industrial robots with unchangeable control systems. The proposed approach includes both the trajectory generation and the trajectory compensation phases. In the first phase, we plan a pick-and-place trajectory that effectively attenuate residual vibration by minimizing the acceleration energy within a specific frequency spectrum, where the frequency parameters and the time ratio are tuned by Bayesian optimization. In the second phase, we focus on improving the tracking accuracy by incorporating a trajectory compensation term. More precisely, we first learn a Koopman operator-based linear predictor, where a model-agnostic meta-learning framework is introduced to mitigate the demand for massive data from the target system. Then, we calculate the trajectory compensation term using an iterative learning control-based method. The proposed methodology is entirely data driven, enabling its application in various robotic systems and has potential in other manufacturing applications. We demonstrate the approach through high-fidelity simulations on Delta robots-a representative parallel robot, where trajectory generation effectively removes vibrations, and through physical experiments on UR5 robots-a typical serial robot. The results of the experiment show that the positioning accuracy of the three joints of the UR5 robot improved by 94%, 43%, and 96%.

This paper presents a design methodology for a multi-directional quasi-zero-stiffness (QZS) system that can achieve vibration isolation of large bodies with high masses across a broad frequency range in all six degrees of freedom in space. The system is based on a QZS design combined with an elastic bedding approach utilizing helical compression springs. Unlike many existing QZS approaches, the proposed system satisfies practical application constraints as well as requirements common in ultra-precision manufacturing. Specifically, it eliminates conventional mechanical joints to prevent particle abrasion and avoids critical high natural frequencies within the isolation system itself. In a two-stage design process, the system is first designed for vibration isolation in all three translational directions and then extended to all six directions through the spatial arrangement of the springs. By analytically deriving and evaluating the system’s stiffness matrix, the mutual dependencies between the positions of the springs and the resulting natural behavior are investigated to determine a suitable system configuration. The findings demonstrate the feasibility of designing a QZS system capable of stable vibration isolation for a body with a mass of 2500 kg that can be tuned to achieve target natural frequencies of about 2 Hz in all six degrees of freedom, while providing internal natural frequencies of the isolation system above a target value of 1000 Hz. In comparison to a conventional vibration isolation system, reductions in the natural frequency of up to 80% were achieved.

Advanced digital manufacturing technologies are currently under development in laboratories or specific fabrication environments. In the construction sector, numerous robotized processes are being experimented with, but their on-site implementation remains challenging. A potential solution is to decentralize production through modular laboratories or portable enclosures that can be brought to the execution site, enabling resource-savings and efficiency. Transformable enclosures allow flexibility in usage by providing shielding and security as well as accommodating design demands such as weatherproofing and noise, visual and splinter protection when needed. This project presents a concept of a hemispherical origami-based transformable and lightweight hybrid enclosure that can be applicable to robotic fabrication applications. Automated design processes implemented origami-based patterning techniques, identifying and adapting the origami pattern to gain full transformability. Static and dynamic analyses, as well as Multi-Body simulations, were carried out, to verify foldability and stability. The outcome is a realized construction of 2.3 m height, 4.15 m diameter that houses a robot with a 1.65 m range and a 15 kg payload demonstrating the feasibility of an origami-based enclosure system for architectural scale.

The growing importance of virtual vehicle development and subjective assessments of driving characteristics on driving simulators pushes the demand for real-time vehicle dynamics simulation. Real-time capable high-fidelity multibody simulation models are required that capture the influence of the kinematics and elastokinematics of wheel suspensions on driving dynamics accurately. Elastic bushings, commonly used in wheel suspensions to enhance ride comfort and handling, introduce high numerical stiffness of the equations of motion and pose significant challenges for real-time simulation. Non-iterative implicit integration methods offer an attractive solution by combining stability, accuracy and deterministic computational effort. These methods avoid iterative solutions of nonlinear equation systems by exploiting the Jacobians of the equations of motion, making them well suited for real-time applications. To reduce their computational effort, this study simplifies several non-iterative implicit integration methods with a linearization-based equations of motion approximation that is updated every few integration steps. The performance of the methods is evaluated and compared for real-time multibody simulations of a double wishbone suspension with elastic bushings. The influence of the integration step size and the update frequency of the equations of motion evaluation and linearization on computation times and accuracy is analyzed using dynamic suspension tests.

Speed and Separation Monitoring (SSM) according to ISO 10218 requires continuous distance monitoring, bounded reaction times, and maintenance of a protective separation distance under worst-case conditions. While certified SSM implementations predominantly rely on exo-centric safety scanners or external vision systems, robot-mounted ego-centric sensing architectures remain under-investigated with respect to normative SSM requirements. A feasibility study of ego-centric Time-of-Flight sensor rings for robot-mounted workspace monitoring is presented. An 18-sensor VL53L7CX ring system mounted on a cobot is analyzed through a structured decomposition of the reaction-time chain and an estimation of the resulting protective separation distance based on a conservative normative framework. The evaluation yields a protective separation distance of approximately 2.4 m under conservative ISO 10218-2 assumptions. For the evaluated architecture, this indicates that standalone SSM is not suitable for close-range collaborative operation. This result should not be interpreted as a fundamental limitation of ego-centric perception itself. Rather, the findings suggest that ego-centric sensing is more appropriately understood as a complementary sensing component within combined collaborative safety concepts. In this role, it can support localized near-workspace monitoring and the detection of human approach in the immediate vicinity of the robot, while overall interaction safety remains governed by additional protective measures.

Learning from Demonstrations(LfD) enables robots to acquire complex skills by observing human behavior, significantly reducing the need for explicit programming. However, applying LfD in industrial settings remains challenging due to limited demonstrations, variability in task executions, and the need to generalize across diverse scenarios. To address these issues, this paper presents a learning based hierarchical task and motion planning framework that integrates Behavior Trees (BT) for high-level task sequencing and Dynamic Motion Primitives (DMP) for low-level motion generation. Demonstration trajectories are segmented and actions are generated using an agentcentric, state-augmented segmentation strategy. Subsequently, relevant features are automatically extracted to define the pre-and post-conditions for each action for the construction of a modular BT. For motion execution, DMP are enhanced with a recovery mechanism for adaptive, obstacle-aware reproduction. A backchaining mechanism is also introduced for BT extension. Validation was performed through simulation and real-world experiments on multiple tasks. Comparative results demonstrate that the proposed method outperforms existing LfD and planning baselines in task success rate, efficiency, and motion smoothness, highlighting its potential for flexible and scalable automation.

This paper proposes an optimal control approach to reduce frame vibrations in robotic pick and place tasks, caused by rapid acceleration and deceleration of the robot. The objective of the proposed optimal control approach is to determine time-optimal trajectories that cancel out residual frame vibrations after trajectory execution. The control problem is defined for a delta robot but can be adapted to any type of robot used for pick and place tasks. To solve the optimal control problem numerically, it is transformed into a nonlinear programming problem using the Legendre-Gauss-Lobatto collocation method. To validate the approach, experiments are conducted to compare the residual frame vibrations of optimized trajectories with typical pick and place trajectories. The novelty of the paper is the vibration reduction with an optimization-based approach on a complex multi-degree-of-freedom robot system whose dynamic parameters are identified with experimental data. In addition, a mathematical description of all constraints required for a robotic pick and place task is proposed for the optimization.

Ultrasonic metal welding is a widely used process for joining metals to create electrical connections using high-frequency mechanical vibrations. Despite its industrial relevance, achieving consistent joint quality remains challenging and requires a deeper understanding of the relationship between process parameters and joint quality. The challenge here is identifying the process signals that significantly correlate with the joint quality. Due to the nature of the process, manual evaluation of such signals is ineffective, leading to an increased focus on implementing statistical or machine learning-based models for predicting joint quality. This study proposes a method to improve the accuracy and interpretability of predicting joint quality using machine learning models. Welding experiments involving various machine internal and external sensors are conducted. The sensors’ measurement data are recorded synchronously as time series. Since most machine learning models require scalar input, informative features must be extracted from the time series data. Various statistical and signal-based features can be extracted from the entire time series. To distinguish between the influences of the different process phases and their overall behavior, the signals are divided into segments. Although equal-length segmentation is simple, it overlooks signal characteristics such as steep transitions or prolonged steady states. Change point detection enables segmentation based on signal behavior, allowing for more relevant feature extraction. Moreover, the detected segments can be associated with known process phases, linking data-driven insights with domain knowledge. As a result, both the predictive performance and interpretability of the ML models improve, allowing for clearer associations between process phases and quality outcomes.

In Robot-Assisted Multidirectional Additive Manufacturing (RAM-AM), the travel paths (non-printing transitions) between separate printing segments are rarely planned explicitly. This often results in discontinuities, inefficient connections, and severe collision risks, particularly in moving-bed configurations where the entire part moves relative to the nozzle. We propose a decoupled planning pipeline that first computes a safe, smooth geometric path in the platform frame and subsequently generates an executable joint-space trajectory under kinematic limits. The geometric planning utilizes an A* search on an OctoMap-based voxel grid with an asymmetric layered cost map to actively steer the end-effector away from the printed structure. This raw path is smoothed using sparse B-splines, and orientations are optimized by blending Spherical Linear Interpolation (Slerp) with Artificial Potential Fields (APF) to maximize clearance. For trajectory generation, we resolve kinematic redundancy by sampling pose tolerances and selecting a globally consistent joint sequence via second-order Dynamic Programming (DP), explicitly penalizing joint jerks and singularities. Validated on a 6-DoF KUKA KR6 R900 sixx, the method is demonstrated in scenarios involving complex obstacles like holes. Results show that our approach guarantees safe obstacle avoidance where standard baselines fail, yields significantly smoother motions, and that exploiting the redundant rotation about the extrusion axis drastically reduces the inverse kinematics search effort.

Collaborative robots (Cobots) are increasingly being used as assistance systems to relieve humans in private and commercial environments. Current systems are designed to stop in the event of a collision, which means that robots only stop after unwanted contact. For completely safe physical collaboration, collisions should be avoided as far as practicable, and distance measurement should take place instead. The internal sensors of Cobots typically do not provide exteroceptive distance measurement, which is why additional sensors must be added. To ensure safe collision avoidance, low-latency systems are required to monitor the work area and avoid collisions. This work investigates how different integration methods for distance-sensors in ROS2 affect end-to-end latency when running on a resource-constrained microcontroller. A Raspberry Pi Pico is used to connect two low-cost sensors that are representative of typical HRC cells: an ultrasonic-sensor and a time-of-flight-sensor. Both sensors use different communication standards. Two integration strategies are compared. In the standard ROS2 integration, the sensor firmware runs on the microcontroller, which streams the data via a serial connection to a ROS2 node. In the micro-ROS-based integration, the firmware and ROS2 publisher are combined in a single application. For each sensor, the latency between measurement acquisition and publication in a ROS2 topic is quantified over multiple trials. The results show that micro-ROS reduces average latency and suppresses extreme outliers, although the overall update rate remains limited by the sensor’s intrinsic sampling frequency. The study derives practical design guidelines for latency-aware integration in ROS2 on low-cost microcontrollers and discusses their implications for safe HRC.

Articulated mobile platforms and closed-loop passive limbs have become two prominent features in parallel robots for pick-and-place operations, enabling large rotation and efficient constraint, respectively. However, the influence of structural parameters inside closed-loop limbs on the performance of such mechanisms remains insufficiently understood. To address this issue, this paper proposes an evaluation approach for the motion-force interaction of articulated-platform parallel mechanisms with closed-loop passive limbs. The investigated mechanisms feature an 8degree-of-freedom (DOF) articulated mobile platform constrained by eight wrenches from passive limbs, also referred to as an 8-wrench-8-DOF system. The power coefficient is adopted to quantify the relationship between each distal wrench and its corresponding virtual distal twist, as well as that between each proximal wrench and its corresponding actual proximal twist. Representative mechanisms, such as the Heli4 and H4 variants, are analyzed. The results show that the approach effectively reveals how closed-loop limb parameters influence the distal motion-force interaction performance of the parallel mechanism. The methodology offers a new perspective for evaluating articulated-platform parallel mechanisms and provides a theoretical foundation for future work, including dimension optimization and type selection.

Interactive mechanism design benefits from continuous parameter variation with immediate kinematic and kinetostatic feedback. However, existing interactive workflows often couple variables ad hoc and lack robust dependency management, which can yield ambiguous update order and inconsistent model states under circular relations. It is hypothesized that making dependencies explicit and validating updates transactionally can preserve a consistent model state while maintaining interactive latency. This paper extends Mechanism Developer (MechDev) with a unified workflow that combines slider-based parametrization for real-time exploration with a graph-based phrasing interface for defining algebraic relations among variables. Updates are compiled into a dependency graph and must pass a transactional validation pipeline (syntax, binding, safety, cycle detection to enforce a directed acyclic graph (DAG)) before commitment. For accepted updates, dependent variables are propagated in deterministic topological order and applied to the live mechanism model and views. In two planar linkage case studies (four-bar and five-bar), the system deterministically propagates parameter changes and rejects invalid or cyclic definitions without corrupting the model state. These results indicate that dependency-graph-based phrasing enables robust, real-time parameter coupling for interactive mechanism design within MechDev.

I2C is widely used in mechatronic systems due to its low wiring effort and broad component interoperability. When identical sensors provide fixed or only weakly configurable addresses, shared-bus integration is limited by address conflicts, cumulative capacitive loading, and sequential transaction timing. These constraints limit the scalability and predictability of multi-sensor architectures. This work presents a segmented I2C architecture that enables the deterministic replication of identical sensor modules through controlled bus visibility. Modular piggyboards implement electrically isolated downstream branches connected to a shared main bus. A safe-channel startup strategy ensures reproducible enumeration before downstream devices become visible. A circular multi-sensor array based on VL53L7CX time-of-flight (ToF) sensors was implemented to evaluate scalability and acquisition behavior. Experimental results demonstrate bounded and reproducible startup behavior and cycle-time jitter, and sustained multi-sensor operation without additional runtime penalty compared to direct bus wiring. The approach establishes a modular scaling principle for I2C-based sensor arrays in mechatronic systems while preserving standard bus compatibility.

Thumb degree-of-freedom (DOF) allocation in anthropomorphic robot hands involves a trade-off between functional mobility and mechanical-control complexity. This study presents a controlled multi-metric framework for comparing recurring thumb DOF configurations under common palm geometry, non-thumb finger structure, reference frames, Denavit-Hartenberg kinematics, and sampling assumptions. Five literature-derived thumb configurations, namely 3-1-1, 2-2-1, 2-1-1, 2-0-1, and 1-1-1, were evaluated to determine which thumb DOFs should be preserved when kinematic complexity is reduced. The theoretical evaluation included Kapandji Opposition Test reachability, opposition alignment, workspace volume, workspace compactness, cylindrical grasp opportunity, and Jacobian-based dexterity. A targeted experimental validation of the 2-1-1 and 2-0-1 prototypes was then performed on a tendon-driven test bench. The results showed that qualitatively similar thumb configurations are quantitatively unequal: several designs achieved identical Kapandji scores but differed substantially in workspace, alignment, dexterity, and grasp feasibility. Overall, 3-1-1 achieved the strongest overall capability, while 2-2-1 emerged as the strongest reduced-complexity alternative and achieved the best mean dexterity. Retaining two active carpometacarpal DOFs preserved a large share of dexterous function, whereas metacarpophalangeal fixation maintained selected cylindrical grasps but narrowed the feasible task boundary.

The mechanical design of anthropomorphic robot hands is complex and comprehensive and accessing relevant information remains challenging. Existing reviews provide valuable insights but lack a structured overview of the mechanical design approaches for robot hands. This work addresses this gap by introducing the Library of Approaches (LoA), a collection of mechanical design approaches for tendon-driven, rigid-sequential anthropomorphic robot hands, organized in morphological boxes. In a systematic mapping following the PRISMA guideline, joint-related design approaches are collected and used to synthesize initially undocumented principal solutions (PSs) for fields of interest (FoIs). Literature references are then categorized into the established PSs and assigned to different morphological boxes. To comprehensively capture the state of the art, five separate search strategies are conducted, resulting in the analysis of 147 publications, including 87 robot hands and 110 authors. In total, 92 FoIs and 177 PSs are identified. The main objective is to provide a convenient initial source of references for the early mechanical design of anthropomorphic robot hands, without claim of completeness. The study concludes with a discussion of current limitations and potential future work.

In this paper, we present the robotics research at IGMR with special emphasis on Delta robots from its start about 10 years ago until very recently. This research history is also strongly connected with our research collaboration with Science Tokyo and an intensive exchange of students and PhD candidates between IGMR of RWTH Aachen University and the Mechanical Systems Design Laboratory (MSD) of Tokyo Tech, now Science Tokyo, led by Yukio Takeda. Starting from topological, kinematic and dynamic optimization of Delta robots, the research concentrated on sustainable Delta robots with increased energy efficiency, high precision and minimized vibrations.

This contribution presents the concept of a Digital Tool Chain based on a thorough literature review. It combines the methods of mechanism synthesis, load analysis and structural optimization in a model-based systems approach and thus enables the development of dynamically more powerful and resource-efficient planar mechanisms in a significantly shorter development time. The novelty of this approach is the orchestration and connection of well-known computer-aided engineering tools.

Systematic design, development and applications of Compliant Grippers (CGs) have surged in the past decade. The works are diverse but information is dispersed. This paper provides a systematic review of 1009 peer reviewed manuscripts in the last ten years, sourced from the Scopus database. Keywords search on CG design, analytical methods, gripper size and design verification. 239 papers are mapped onto applications, types of workpieces, actuation technologies, focusing onto CG design methodologies. Actuation methods are classified into indirect and direct. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol is followed. Key findings include: (i) CGs are mostly designed with direct mechanical load actuation, the corresponding synthesis methods follow well defined processes; (ii) Most CGs cater to convex, regular and small objects; (iii) much focus on their application is on research and development followed by manufacturing and assembly, healthcare, electronics and semiconductors and food processing; (iv) fluidic actuation is gaining prominence but not as much as direct actuation; and (v) systematic synthesis methods are needed for other existing and emerging technologies like controlled adhesion, smart materials and jamming.

Portable hand exoskeletons are reaching homes, providing daily assistance to people with hand impairments and enhancing their quality of life. Their design, however, is rarely based on the biomimetics of human finger kinematics, missing the benefits of anatomical tendon pulley-based actuation, such as natural and comfortable movement with efficient force transmission. We present a case study of a portable biomimetic hand exoskeleton system for assistance in grasping and rehabilitation. Components of the exoskeleton are parameterized via computer-aided design (CAD) using human hand dimensions measured at selective locations facilitating easy customization. Easy-to-use voice and manual button input strategies control the exoskeleton. A biomimetic flexor system with passive extension is used. A stiffness adjuster mechanism is provided at a metacarpophalangeal (MCP) joint to make suitable adjustments to the extension force. We evaluate the system with a hemiparetic subject with low grip strength poststroke that occurred a decade ago, showing promising results of high contact forces when grasping a variety of objects. This lightweight exoskeleton could be helpful for people finding difficulties in grasping related activities of daily living due to neurological conditions and/or aging.