Industrial robots are widely utilized in the machining of complex parts because of their flexibility. However, their low positioning accuracy and spatial geometric error characteristics significantly limit the contour precision of robot machined parts. Therefore, in the robot machining procedure, an in situ measurement system is typically required. This study aims to enhance the trajectory accuracy of robotic machining through robotic in situ measurement and meta-heuristic optimization. In this study, a measurement-machining dual-robot system for measurement and machining is established, consisting of a measurement robot with a laser sensor mounted at the robot end and a machining robot equipped with a machining tool. In the measuring process, high-precision standard spheres are set on the edge of the machining area, and the high-precision standard geometry is measured by the measurement robot. According to measured geometry information in the local area, the trajectory accuracy for the machining robot is improved. By utilizing the standard radius of the standard spheres and adopting a meta-heuristic optimization algorithm, this study addresses the complexity of the robot kinematics model, while also overcoming local optima commonly introduced by gradient-based iterative methods. The results of the experiments in this study confirm that the proposed method markedly refines the precision of the robot machining trajectory.

This article introduces the Voice Leaf, an outsider among Baschet’s numerous sound sculptures because of the use of the performative voice. Conceived in 1965 by French pioneers Bernard and François Baschet, the sculpture for voice consists of a stainless steel sheet folded as a leaf using origami technique. This article explores how voice and sculpture interplay acoustically by evaluating the voice’s agency and the sculpture’s aural dynamic gain. In this mutualist relationship, multiple senses are mobilised: aural, visual and haptic. The voice harboured in the sculptural leaf gains materiality and a resonance altered by the sculpture’s intrinsic properties. The article draws from conversations at the Structures Sonores Baschet Association open day with chairperson Pierre Cuffini and former workshop and acoustics research director Frédéric Fradet, as well as an interview with multidisciplinary artist and long-term collaborator of Bernard Baschet, Sophie Chénet.

This article is a discussion with supporting commentary, exploring the complex interplay and role of experimentation in various British Black music genres. We consider these as rich sources of cultural production, what we term the ‘Black Box’. As part of this Black Box discussion, we consider the researcher’s role in studying cultural production at global, national, regional and community levels. We critique the tendency of Western markets to both commodify and homogenise as well as raise concerns about perpetuating forms of neo-colonialism, especially with the increased importance of Africa, particularly styles such as afrobeats. Our discussion highlights the paradox of late corporate capitalism’s short-term focus, and we consider whether there is potential for a technological infrastructure to create genuine cultural and economic growth, that also challenges Eurocentric and Anglo-American dominance of the music industry. Within this flux, the importance of experimentation and the emergence of micro-genres facilitated by the internet advances a global dispersal of new sounds. However, this diversity is shadowed by the continued relevance of major label structures and the role of streaming platforms in controlling and mediating artist–fan relationships.

This editorial examines the systemic exclusion of Black and South Asian artists from the field of experimental sound, highlighting the historical and institutional biases that have marginalised their contributions. While experimental sound is often framed as a universal, ethnically neutral practice, this narrative obscures the racial and cultural biases shaping the discipline. The marginalisation of these artists is not simply about visibility; it reflects deeper socio-cultural and institutional mechanisms that have historically sidelined their radical sonic innovations. This issue challenges the Eurocentric frameworks that dominate the discourse, drawing attention to the pioneering contributions of Black and South Asian musicians whose work expands the possibilities of experimental sound. By centring these voices, we aim to decolonise the field and offer a more inclusive understanding of experimental sound that recognises its global, diverse influences. Through contributions from artists and scholars, this issue explores how race, identity, and culture intersect within sonic experimentation, offering critical perspectives that question established narratives. Ultimately, this collection aims to reshape the future of experimental sound by amplifying underrepresented voices, advocating for a more equitable and representative sonic landscape that acknowledges the depth of contributions from historically marginalised communities.

This article investigates the innovative pedagogical approaches and cultural integration of electroacoustic music in Papua, Indonesia, through the work of composer and educator Markus Rumbino. Born in 1989 in Jayapura, Papua, Rumbino is the first professional electroacoustic composer from eastern Indonesia. After returning to Jayapura in 2013 to join the Institute of Arts and Culture (ISBI) Tanah Papua, he faced unique challenges in a region where electroacoustic music is largely unfamiliar and often misunderstood. The study explores how Rumbino bridges Western music education with Indigenous Papuan sound environments to foster cultural identity and confidence among his students – primarily Indigenous from East Indonesia, including natives from the Papuan Highlands with limited formal musical training. Through detailed interviews and analysis, the article examines his innovative use of soundscape composition, listening exercises and soundwalk methodologies as pedagogical tools. By engaging students in critical listening and exploration of their local soundscapes, Rumbino reconnects them with their cultural heritage while introducing contemporary artistic expressions. Situating his methods within the broader context of soundscape literature and inclusive educational practices in electroacoustic music, this article highlights the transformative potential of integrating local soundscapes into music education. This contributes to discussions on culturally responsive teaching methods and the role of environmental sounds in fostering musical creativity.

This article explores an under-discussed and unclaimed conceptualisation of futurity that can be located within historical sound practices and sonic thoughts of the Indian subcontinent. In the 1950s and 1960s, this alternative sonic worldview influenced Western music and its sound pallet without credit. The intervention of this futurism in the Western model of music, sounding and listening was revolutionary, proliferating an alternate aesthesis of time, space and subjectivities in sound practices – with an emergent environmentality, manifesting arguably in the birth of ambient music and sounding arts and remodelling of sensing the world from a relational perspective. Yet, this sonic worldview, knowledge system and a radical sense of non-linear futurity were not recognised then. But the importance of the futurity can be appreciated today on the verge of multiple planetary crises. It is in this time and day that a futurist vision may provide a new sense of surviving for a posterity and generate a possibility of emancipation from the fear and loathing for a dystopian tomorrow, which is construed from a Western perspective entrenched in its rationality. How can we hear possible futures from perspectives of South Asia that have been marginalised in sonic epistemologies by an absence of voices, which could offer new grounds?

We have all made poor decisions, and some such questionable decisions are artistic in nature. When looking back on one’s early work, it is easy to have tinges of embarrassment that are counterbalanced by nostalgia. John Baldessari made this dynamic tangible in 1970 through his Cremation Project, an undertaking in which he burned all of his paintings and baked some of the resulting ashes into cookies. Viewing some of these cookies/ex-paintings several years ago, I felt that Baldessari’s approach to his previous work, simultaneously embracing, annihilating and remaking, was a fitting way to let go of one’s artistic past. My user-driven installation Confessional provides the opportunity for composers to briefly take pleasure in and (symbolically) destroy one of their dubious creations. This process is accomplished with a computer running Max and a user-provided recording that is processed live. The audio processing unfolds in stages that mirror the phases of animal decomposition. Through this series of transformations, the user’s piece transitions from its original state to nearly imperceptible bits of noise. In this article, I examine Confessional, focusing on the work’s conceptual background, related issues such as memory and hierarchy, and the structure of the Max patch that is used for processing.

Auditory-based illusions and effects are fascinating fields for both psychoacoustic research and sound installations. While such illusions and effects are usually researched in isolated scientific studies, they can also be applied as compositional tools in sound installations. This article addresses the suspenseful connection between psychoacoustic research and sound installations. After defining terms relevant to auditory-based illusions and effects, various aspects of sound installations are described. In that light, auditory-based illusions and effects are described and categorised and examples are provided for their scientific investigation by means of references to key experiments. Further, examples of applications are included that showcase the use of auditory-based illusions and effects in compositions and sound installations. Finally, in order to foster future artistic applications, the connections between illusions and effects are visualised, and sound-installation aspects are provided in a table. Such a combined consideration of psychoacoustic fundamentals and sound-installation aspects aims not only at deepening the methodological knowledge of sound artists, but also inspiring innovative compositional perspectives.

The widespread deployment of artificial intelligence (AI) and machine learning tools has created a shift in knowledge culture. The marginalisation of slower, more traditional modes of engagement for quantifiable data easily parsed by mathematical algorithms has resulted in prioritising proprietary or opaque datasets (knowledge) explicitly constructed with measurable parameters. Well-documented concerns persist regarding the narrow range of human data used by algorithmic tools, data that arguably encapsulates the many failures of human society. The inevitable result of the use and priority of this data, alongside very particular notions of value and what is valuable, is a replication of many of the foibles of our history as a species.

Cultural practice in general necessitates the communication of what drives our hopes and underlies our experiences. In algorithmic times we can see that this kind of communication supports some of the many critiques of AI and machine learning already extant in activist circles. Through investigating some of the theoretical backgrounds of this resistance, this article uses the first iteration of HEXORCISMOS’S SEMILLA AI project and the resulting album release as one of the many possible ways in which we might use machine learning and AI tools alongside very deliberate and uplifting models of community and community building.

Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk interactions. This paper introduces various types of contribution ratio measures based on the multivariate conditional value-at-risk (MCoVaR), multivariate conditional expected shortfall (MCoES), and multivariate marginal mean excess (MMME) studied in [34] (Ortega-Jiménez, P., Sordo, M., & Suárez-Llorens, A. (2021). Stochastic orders and multivariate measures of risk contagion. Insurance: Mathematics and Economics, vol. 96, 199–207) and [11] (Das, B., & Fasen-Hartmann, V. (2018). Risk contagion under regular variation and asymptotic tail independence. Journal of Multivariate Analysis 165(1), 194–215) to assess the relative effects of a single risk when other risks in a group are in distress. The properties of these contribution risk measures are examined, and sufficient conditions for comparing these measures between two sets of random vectors are established using univariate and multivariate stochastic orders and statistically dependent notions. Numerical examples are presented to validate these conditions. Finally, a real dataset from the cryptocurrency market is used to analyze the spillover effects through our proposed contribution measures.

This paper addresses the issue of energy-efficient trajectory optimization for planetary surface manipulators under kinematic and dynamic constraints. To mitigate the inefficiency of existing algorithms, an adaptive boundary adjustment strategy for the multi-dimensional decision space is proposed, which modifies the time intervals between neighboring configuration nodes, enabling precise adaptation of the decision space boundaries. Additionally, a complementary dual-archive guided boundary exploration strategy is introduced to connect the feasible and infeasible regions, allowing for the effective utilization of information from infeasible solutions near the constraint boundaries. This heuristic approach guides the particle swarm in efficiently exploring areas close to the constraints, significantly enhancing the evolutionary optimization capability of the swarm. Furthermore, a swarm optimal position updating strategy based on sparsity sorting is developed. This guides the particle swarm to concentrate on exploring positions where non-dominated solutions on the Pareto front are more sparsely distributed, ensuring uniformity and completeness in the final Pareto front. Finally, the aforementioned strategies are integrated into a heuristic multi-objective particle swarm optimization (HMOPSO) algorithm for the trajectory optimization of manipulators. Comparative experiments are conducted with HMOPSO and existing advanced algorithms in the field of multi-objective optimization. Experimental results demonstrate that HMOPSO exhibits superior evolutionary optimization capabilities and faster convergence rates. Moreover, performance metrics such as inverse generation distance and dominant area during the iterative process of HMOPSO significantly outperform those of existing optimization algorithms.

Among various deep learning-based SLAM systems, many exhibit low accuracy and inadequate generalization on non-training datasets. The deficiency in generalization ability can result in significant errors within SLAM systems during real-world applications, particularly in environments that diverge markedly from those represented in the training set. This paper presents a methodology to enhance the generalization capabilities of deep learning SLAM systems. It leverages their superior performance in feature extraction and introduces Exponential Moving Average (EMA) and Bayes online learning to improve generalization and localization accuracy in varied scenarios. Experimental validation, utilizing Absolute Trajectory Error (ATE) metrics on the dataset, has been conducted. The results demonstrate that this method effectively reduces errors by $20\%$ on the EuRoC dataset and by $35\%$ on the TUM dataset, respectively.

In this article, the performance analysis and multiobjective structure optimization of 4RRR parallel mechanism are carried out. Firstly, the 4RRR pure rotation parallel mechanism and its design route are introduced. Secondly, the Jacobian matrices in 2DoF pure rotation and 3DoF pure rotation modes are derived using the motion equations of the mechanism. Next, the singularity analysis, kinematic dexterity analysis, dynamic dexterity analysis, and stiffness analysis of the mechanism are carried out, respectively, and it is proved that there is no singularity in the mechanism in its workspace. Since the dexterity performance expression is a nonlinear piecewise function, the kinematic local comprehensive dexterity index and the dynamic local comprehensive dexterity index are proposed as the objects of analysis. Furthermore, the kinematic global comprehensive dexterity index, the dynamic global comprehensive dexterity index, and the global comprehensive stiffness index are proposed to carry out the multiobjective structural optimization. Finally, NSGA3 was used to complete the optimization, and the comprehensive optimal solution of the structure size was obtained.

Humanoid robots are highly redundant, and finding whole-body optimal trajectories for various tasks is very complex. This paper proposes a method to find an energy-optimal, dynamically balanced, and collision-free trajectory of the 20 degrees of freedom humanoid robot in pick and place application. The task of pick and place is divided into three subtasks using the Pseudoinverse Jacobian method of redundancy resolution. The three subtasks are end effector trajectory represented by $\mathcal {T}_1$, hip trajectory represented by $\mathcal {T}_2$, and maximizing the manipulability represented by $\mathcal {T}_3$. The Pseudoinverse Jacobian method is coupled with particle swarm optimization (PSO) to find the optimal trajectories. The main contribution of this paper is the decomposition of the whole-body task of the humanoid robot into three distinct subtasks to find energy-optimal, dynamically balanced, and obstacle-free trajectories. The concept of virtual surface is used to avoid dragging objects on the table surface. The problem is optimized with Particle Swarm Optimization. Simulations were conducted to pick up and place objects from a table and constrained spaces like a drawer. The results show that the robot can pick and place objects from defined locations on the table.

Collaborative robotics in manufacturing introduces a new era of seamless human–robot collaboration (HRC), enhancing production line efficiency and adaptability. However, guaranteeing safe interaction while maintaining performance objectives presents significant challenges. Integrating safety with optimal robot performance is paramount to minimize task time and ensure its completion. Our work introduces an architecture for safety in confined human–robot workspaces by integrating existing safety and productivity methods into a unified framework specifically designed for constrained environments. By employing an improved artificial potential field, we optimize paths based on length and bending energy and compare baseline algorithms like gradient descent algorithm and rapidly exploring random tree (RRT*). We propose an evaluation metric for system performance that objectively maps to the system’s safety and efficiency in diverse collaborative scenarios. Additionally, the architecture supports multimodal interaction, including gesture-based inputs, for intuitive control and improved operator experience. Safety measures address static and dynamic obstacles using potential fields and safety zones, with a real-time safety evaluation module adjusting trajectories under specified constraints. A performance recovery algorithm facilitates swift resumption of high-speed operations post safety interventions. Validation includes comparing the algorithmic performance through simulations and experiments using the 6-degrees of freedom UR5 robot by universal robots to identify the most suitable algorithm. Results demonstrate an 83.87% improvement in system performance compared to ideal case scenarios, validating the effectiveness of the proposed architecture, evaluation metric, and multimodal interaction in enhancing safety and productivity.