Trajectory optimization is a critical research area in robotics and automation, especially in manufacturing industries where mechanical systems are often required to minimize the execution time or the consumed energy. In this context, the most common mechanical systems are those with a single degree of freedom because of their simplicity and ease of control. In this paper, we present an approach for the online optimization of minimum-time and minimum-energy trajectories for a robotic system with one degree of freedom. Point-to-point motions of the considered linear axis are planned online, without idle times, by leveraging a verified dynamic model of the robotic system, which also includes an accurate identification of friction parameters. Both minimum-time and minimum-energy trajectories are considered, and the performance of the online optimization using a selected open-source optimization tool is verified in different dynamic conditions of the system. The results of extensive experiments on a belt-driven robotic axis demonstrate the feasibility and the energy-saving capabilities of the proposed approach, as well as the flexibility of the online trajectory optimization in different scenarios, while meeting the kinematics and dynamics limits of the system and guaranteeing low computational time.

Efficient local trajectory optimization of the coordinated manipulator is a bottleneck task in the narrow feeding scenario. To optimize the trajectory locally and generate collision-free trajectories with local support performance, the analytical reinforcement method is proposed. Firstly, multiple coordinated machines operating in the narrow space are transformed into decentralized dynamic constraints for the target manipulator. Combined with the circle envelope method in the dynamic constraint, the collision-free gradient optimization function determines the support region of the local optimal trajectory. Based on the forward kinematics and inverse kinematics method, the collision-prone pose of the target manipulator outside the support region is analytically optimized. And chi-square distribution further ensures the smooth interpolation of the variable-period trajectory outside the fixed-period support region. For the emergency collision avoidance of the coordinated manipulator in the flexible stamping line, the analytical reinforcement method is successfully verified by generating the collision-free and smooth trajectory. It provides an optimization direction for rapidly improving the work efficiency of multi-machine coordination in the narrow feeding scenario.

Semi-simplicial and semi-cubical sets are commonly defined as presheaves over, respectively, the semi-simplex or semi-cube category. Homotopy type theory then popularized an alternative definition, where the set of $n$-simplices or $n$-cubes are instead regrouped into the families of the fibers over their faces, leading to a characterization we call indexed. Moreover, it is known that semi-simplicial and semi-cubical sets are related to iterated Reynolds parametricity, respectively, in their unary and binary variants. We exploit this correspondence to develop an original uniform indexed definition of both augmented semi-simplicial and semi-cubical sets, and fully formalize it in Coq.

While modern definitions of business processes exist and are shared in the business process management (BPM) community, a commonly agreed meta-model is still missing. Nonetheless, several different business process meta-models have been proposed and discussed in the literature, which look at business process models from different perspectives, focusing on different aspects and often using different labels for denoting the same element or element relation.

In this paper, we extend and consolidate an effort of building a business process meta-model starting from elements and relations discovered inspecting relevant literature through a systematic literature review. The obtained literature-based business process meta-model, which is on purpose built to disclose critical issues, is then inspected, compared to a previous, more restricted, version, and discussed. The analysis confirms a lack of attention to some crucial business process elements, as well as the presence of some unclear relations and subsumption cycles. Moreover it brings about new issues and inconsistencies in the meta-models proposed in literature, which we address - at least in part - using an ontological analysis.

Stochastic actor-oriented models (SAOMs) were designed in the social network setting to capture network dynamics representing a variety of influences on network change. The standard framework assumes the observed networks are free of false positive and false negative edges, which may be an unrealistic assumption. We propose a hidden Markov model (HMM) extension to these models, consisting of two components: 1) a latent model, which assumes that the unobserved, true networks evolve according to a Markov process as they do in the SAOM framework; and 2) a measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for parameter estimation. We address the computational challenge posed by a massive discrete state space, of a size exponentially increasing in the number of vertices, through the use of the missing information principle and particle filtering. We present results from a simulation study, demonstrating our approach offers improvement in accuracy of estimation, in contrast to the standard SAOM, when the underlying networks are observed with noise. We apply our method to functional brain networks inferred from electroencephalogram data, revealing larger effect sizes when compared to the naive approach of fitting the standard SAOM.

Collaborative engineering design is increasingly important for modern engineering practices as projects routinely require collaboration across multiple domains. Reaching shared understanding within the team is a critical factor in constructing a successful and enjoyable collaboration. One way to promote shared understanding is through the use of design artifacts and design representations as boundary objects. Different design representations have unique characteristics that benefit the engineering design process but could also hinder the development of shared understanding. It is important to identify the limitations of the design artifacts to select the suitable design artifact for the situation and mitigate potential adverse effects, including design fixation and miscommunication. Despite previous studies’ findings, there are still unsolved questions regarding the exact effect of the modality of the design representations on the development of team-shared understanding. This work examines three types of commonly used design representations in the engineering design community, namely, textual description, hand sketch and engineering CAD model. Their unique effect on the development of shared understanding is investigated in a collaborative engineering design setting. The results indicate that the modality of the design artifact would affect the development of shared understanding, and using visual representations can yield better team outcomes regardless of the modality complexity, mainly for design structures. This work shows the importance of using the proper design representation in collaborative engineering design tasks, and such a finding is a critical and timely reminder in the current age when team interactions constantly involve text-dominant online communications.

The manufacturing sector is witnessing a paradigm shift toward servitization, where companies are transitioning from selling products to offering product–service systems. This shift creates additional challenges, where the providers must ensure the expected value throughout the operational phase of the solution. Especially when dealing with a system-of-systems (SoS), evaluating performance across diverse contexts and business models while understanding the interconnectedness between systems becomes critical. To address these challenges during the design phase, this article presents a novel integrated simulation framework that supports the development team in exploring value from a SoS perspective. This framework utilizes agent-based simulation and offers three key features: multifidelity, modular and multidisciplinary. The applicability of the proposed framework is further demonstrated in a quarry industry case.

Human-centered design involves designing for users who may have social identities that are dissimilar from designers’ social identities. These differences could impact designers’ ability to understand users’ needs and integrate considerations of social identity into design decisions. Reflective interventions could encourage designers to actively consider social identity in design and our aim in this research is to explore this hypothesis through an experimental study. We tested the effects of completing a social identity-based reflection exercise on novice designers’ task clarification behavior. We also qualitatively examined the quality and content of the reflection responses. We find that participants who completed the intervention generated more social identity-focused design requirements, irrespective of the persona provided to them. Additionally, the content analysis revealed that designers who occupy minority identities (e.g., women and students of color) were more likely to provide deeper and higher-quality reflection responses. These findings suggest that reflective interventions could be an effective mechanism to promote inclusive design, leading to the design of products that users across social identities can use equitably. Furthermore, designers with different social identities may require different reflection cues (e.g., ones more focused on their personal experiences), to encourage deeper reflection on the effects of social identity in design.

Unmanned surface vehicles (USVs) frequently encounter inadequate energy levels while navigating to their destinations, which complicates their successful berthing in intricate harbor environments. A bacterial foraging optimization algorithm (BFO) is proposed that takes energy consumption into account and incorporates multiple constraints (MC-BFO). The energy consumption model is redefined for wind environments, enhancing the sensitivity of USVs to wind conditions. Additionally, a reward function is integrated into the algorithm, and the fitness function is reconstructed to improve the goal orientation of the USV. This approach enables the USV to maintain a reasonable path length while pursuing low energy consumption, resulting in more practical navigation. Constraining the USV’s sailing posture for smoother paths and restricting the USV’s heading and speed near the berthage facilitate safe berthing. Finally, three distinct experimental environments are established to compare the paths generated by MC-BFO, BFO, and genetic algorithm under both downwind and upwind conditions, ensuring consistency in relevant parameters. Data on sailing posture, energy consumption, and path length are collected, generalized, and analyzed. The results indicate that MC-BFO effectively reduces energy consumption while maintaining an acceptable path length, resulting in smoother and more coherent paths compared to traditional segmented planning. In conclusion, this method significantly enhances the quality of the berthing path.

This article examines the creation of an Urban Archive as an English Garden, a work that uses GPU-accelerated low-resolution wavefield synthesis (WFS) to combine field recordings, live performance and generative audio in real time. Owing to computational overhead, WFS is often pre-rendered, leading to a less dynamic and more static scope for the embodied and intersubjective nature of human sensory understanding, a tendency that can also be found in traditional soundscape composition. We argue that engagement with real-time WFS offers a new approach to soundscape composition, wherein musical-system design may have multiple agencies, or that of virtual environment, co-creator, archive and hybrid instrument. Through a post-phenomenological lens, an analysis of the work’s creation through different domains reveals how these technologies afford novel practices to engage with our sonic environments. Additionally, the article unpacks how this same process, grounded in site-responsive design offers new approaches to composition, performance and artistic collaboration across these practices.

This paper presents the design and experimental validation of a robust flight control strategy for quadrotor unmanned aerial vehicles (UAVs) based on the Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) methodology. The proposed approach is specifically tailored to the Parrot Bebop 2, a commercial UAV. The IDA-PBC control law is derived using the Hamiltonian model of the UAV dynamics obtained from experimental data to represent the dynamics of all six degrees of freedom, including translational and rotational motions. The control strategy was validated through numerical simulations and experimental tests conducted in an indoor flight setup using MATLAB, Robot Operating System, and an OptiTrack motion capture system. Numerical and experimental results demonstrate that the controller effectively tracks desired flight trajectories, ensuring stable and robust performance.

Due to the complexity of urban and rural drainage systems, although many types of robots have been designed for this purpose, the mainstream pipeline inspection robots are currently dominated by four-wheeled designs. In this study, the shortcomings of four-wheeled pipeline robots were analyzed, including poor passability, difficulties in spatial positioning and orientation, and the limited effectiveness of conventional two-degree-of-freedom observation systems. Based on these issues, the spatial pose mathematical model of the four-wheeled robot inside the pipeline was investigated, along with the spatial geometric constraints and speed characteristics during cornering. This study was intended to reveal the spatial geometric parameter limitations and the kinematic characteristics of the four-wheeled pipeline robot under these constraints, providing corresponding recommendations. To address the issue of the outdated two-degree-of-freedom vision component, a three-degree-of-freedom visual component was designed, and forward kinematics analysis was conducted using Standard-Denavit-Hartenberg parametric modeling, revealing its motion speed and characteristics. Based on this visual component, a new concept of in-pipeline robot vision was proposed, providing new references for the design of four-wheeled pipeline robots.

This theoretical pearl shows how a graphical, relational, point-free, and calculational approach to linear algebra, known as graphical linear algebra, can be used to reason not only about matrices (and matrix algebra, as can be found in the literature) but also vector spaces and more generally linear relations. Linear algebra is usually seen as the study of vector spaces and linear transformations. However, to reason effectively with subspaces in a point-free and calculational manner, both can be generalized to an unifying concept: linear relations, much like relational algebra. While the semantics is relational, the syntax is graphical and uses string diagrams, 2-dimensional formal diagrams, which represent the linear relations. Most importantly, in a number of cases, the relational semantics allows algorithms and properties to be derived calculationally instead of just verified. Our approach is to proceed primarily by examples which involve finding inverses, switching from an implicit basis to an explicit basis (solving a homogeneous linear system), exploring both the exchange lemma and the Zassenhaus’ algorithm.

This paper presents a general approach to synthesizing closed-loop robots for machining and manufacturing of complex quadric surfaces, such as toruses, helicoids, and helical tubes. The proposed approach begins by employing finite screw theory to describe the motion sets generated by prismatic, rotational, and helical joints. Subsequently, generatrices and generating lines are put forward and combined for type synthesis of serial kinematic limbs capable of generating single-DoF translations along spatial curves and two-DoF translations on complex quadric surfaces. Following this manner, the two-DoF translational motion patterns on these complex quadric surfaces are algebraically defined and expressed as finite screw sets. Type synthesis of close-loop robots having the newly defined motion patterns can thus be carried out based upon analytical computations of finite screws. As application of the presented approach, closed-loop robots for machining toruses are synthesized, resulting in four-DoF and five-DoF standard and derived limbs together with their corresponding assembly conditions. Additionally, brief descriptions of robots for machining helicoids and helical tubes are provided, along with a comprehensive list of all the feasible limbs for these kinds of robots. The robots synthesized in this paper have promised applications in machining and manufacturing of spatial curves and surfaces, enabling precise control of machining trajectories ensured by mechanism structures and achieving high precision with low cost.

Simulations of critical phenomena, such as wildfires, epidemics, and ocean dynamics, are indispensable tools for decision-making. Many of these simulations are based on models expressed as Partial Differential Equations (PDEs). PDEs are invaluable inductive inference engines, as their solutions generalize beyond the particular problems they describe. Methods and insights acquired by solving the Navier–Stokes equations for turbulence can be very useful in tackling the Black-Scholes equations in finance. Advances in numerical methods, algorithms, software, and hardware over the last 60 years have enabled simulation frontiers that were unimaginable a couple of decades ago. However, there are increasing concerns that such advances are not sustainable. The energy demands of computers are soaring, while the availability of vast amounts of data and Machine Learning(ML) techniques are challenging classical methods of inference and even the need of PDE based forecasting of complex systems. I believe that the relationship between ML and PDEs needs to be reset. PDEs are not the only answer to modeling and ML is not necessarily a replacement, but a potent companion of human thinking. Algorithmic alloys of scientific computing and ML present a disruptive potential for the reliable and robust forecasting of complex systems. In order to achieve these advances, we argue for a rigorous assessment of their relative merits and drawbacks and the adoption of probabilistic thinking for developing complementary concepts between ML and scientific computing. The convergence of AI and scientific computing opens new horizons for scientific discovery and effective decision-making.

We introduce the framework FreeCHR which formalizes the embedding of Constraint Handling Rules (CHR) into a host language, using the concept of initial algebra semantics from category theory. We hereby establish a high-level implementation scheme for CHR as well as a common formalization for both theory and practice. We propose a lifting of the syntax of CHR via an endofunctor in the category Set and a lifting of the very abstract operational semantics of CHR into FreeCHR, using the free algebra, generated by the endofunctor. We give proofs for soundness and completeness with its original definition. We also propose a first abstract execution algorithm and prove correctness with the operational semantics. Finally, we show the practicability of our approach by giving two possible implementations of this algorithm in Haskell and Python. Under consideration in Theory and Practice of Logic Programming.

This article introduces a dome-type soft tactile sensor that can autonomously adjust its stiffness to evaluate surface contact characteristics, including the elastic modulus, contact force, and the presence of abnormal hardness within soft materials, using a strain gauge as a single sensing element. The strain sensor element is placed at the tip of the dome to measure the deformations during contact that reflect the properties of the contacted object. Using machine learning techniques, the sensor system can accurately predict these characteristics in various materials with an error rate of less than approximately 8%. A hybrid approach that combines experimental and simulation data enables the sensor to be trained effectively, generating sufficient data for accurate predictions without extensive experiments. The high accuracy results of the machine learning models demonstrate that the sensor system can precisely calculate the elastic modulus and depth of the defect. The adaptability and precision of the proposed sensor make it ideal for applications in medical diagnostics and other fields requiring careful interaction with soft materials. Furthermore, its innovative approach can be referenced for exploiting the properties of soft materials to achieve task-specific morphology without redesigning soft sensors or soft robots.