Aiming at the issues of traditional ant colony algorithm (ACO) in mobile robot path planning, including initial search blindness, susceptibility to local optima, and slow convergence, this paper proposes a multi-strategy improved ant colony algorithm (MS-ACO). Firstly, dynamic non-uniform distribution of initial pheromones is implemented by integrating the repulsive field from artificial the potential field method. Secondly, the heuristic information is enhanced to improve global search capability while constraining unnecessary path turns. Thirdly, an improved pheromone update strategy is developed by adopting distinct updating mechanisms for different evolutionary phases. Finally, dynamic parameter adaptation is achieved through optimized weight coefficients and volatility coefficients that coordinate with the pheromone update strategy, better aligning with the iterative characteristics of ant colony optimization. Experimental results demonstrate that MS-ACO effectively addresses the limitations of traditional ACO. Under identical experimental conditions, it achieves a 30.4% reduction in path length, 37.8% decrease in pathfinding time, and 71% fewer turns compared to conventional methods, verifying the feasibility and superiority of the proposed algorithm.

Sit-to-stand (STS) motion is an essential daily activity. However, this motion becomes increasingly difficult for older adults as their muscle strength declines with age. To assist individuals in standing up while maximizing their muscle strength based on the assist-as-needed (AAN) strategy, assistive devices must detect early STS intent, specifically before the buttocks leave the chair, to ensure timely assistance. This study proposes a novel method for detecting STS intent by applying external mechanical stimuli to the toes and analyzing the resulting changes in heel and toe-reaction forces. Moreover, a structured detection framework was developed by utilizing predefined thresholds for the change rate and magnitude of the heel and toe-reaction forces to detect STS intent. Offline tests for threshold setting of STS-intent detection were established in the offline tests: change rate and magnitude of the reaction forces on the heel and toes. The thresholds for each criterion were determined using the Pareto optimization method. Using the determined thresholds, these criteria were then applied in online tests to evaluate the performance of the proposed intent detection method. The results demonstrated that mechanical stimuli improved the performance of STS-intent detection, providing accurate and stable detection. This method can be applied to STS-assistive devices to effectively implement AAN functionality for standing assistance.

The study presents a novel cable-driven serial robot based on flexible joints and tensegrity structures, which features a rapid response capability in complex dynamic environments. This makes it particularly suitable for human–robot interaction scenarios. Compared to traditional rigid serial robots, the design’s compliance demonstrates significant advantages in addressing complex demands. The study delves into kinematic and dynamic modeling methods and verifies their effectiveness through simulations. The kinematic model transforms the local coordinate system to the global one using general kinematic equations. First, the static and dynamic model of the robot is derived based on the torque balance equation, and then the dynamic model of the robot is constructed. By simplifying the robot model, the relationship between tension values from driving cables and the robot’s workspace is analyzed under the constraints of tensegrity structures and flexible joints. Additionally, trajectory simulations validate the kinematic and dynamic models. The kinetic energy variation curves based on the trajectories confirm the accuracy of the theoretical analysis. This method demonstrates broad applicability and can be applied to other serial robots with flexible structures, offering effective solutions for use in complex dynamic environments.

Soft robots have emerged as a transformative technology with widespread applications across diverse fields. Among various actuation mechanisms, fluid-based actuation remains predominant in soft robotics, where precise fluid regulation is fundamental to system performance. This review aims to provide a comprehensive reference for researchers interested in fluid regulation strategies in soft robots by outlining the current state of research in this field and discussing innovations in valve designs to inspire future advancements. The fluid regulation strategies discussed in this review are systematically categorized into three main approaches: valve-based, smart fluid-based, and pressure source-based strategies, with each type systematically classified and discussed in detail. Building upon this analysis, a Task-to-Fluidic Regulation System mapping framework is proposed, integrating the V-model principles from systems engineering to provide a structured, requirements-driven methodology that links task objectives to concrete regulation system configurations through sequential design and multi-level verification. Finally, the latest advancements in fluid regulation methods in soft robotics are summarized, along with emerging trends and directions for future development.

The variable stiffness actuator (VSA) excels at tasks that are challenging for traditional rigid mechanisms to perform. A novel variable stiffness tensegrity-based compliant actuator is proposed, following an analysis of the cons and pros of existing VSAs. The proposed actuator leverages a tensegrity structure to eliminate direct contact between rigid elements, thereby reducing the internal mechanical friction. This leads to low damping and compliant behavior. Additionally, it enables a wide range of stiffness adjustments and decouples rotational stiffness from the rotation angle by utilizing different variants of the mechanically adjustable compliance and controllable equilibrium position actuator (MACCEPA). The stiffness analysis of the single-joint actuator is presented and experimentally validated. This design is then extended to multi-joint mechanism applications, including serial mechanism configuration, wire-driven mechanism configuration, and direct-drive mechanism configuration. An evaluation of the structural characteristics of these three configurations is provided, offering different options for implementing VSAs. The conducted works could provide fresh insights into the field of VSA.

Aiming at the issues of more difficult to solve and lower precision of six-axis robotic arm in inverse kinematics (IK) solution, a multi-strategy improved dung beetle optimization algorithm (ECDBO) is proposed. It improves performance in four aspects: population initiation, global search capability, search direction perturbation and jumping out of local optima. Sobol sequence strategy was introduced to initialize the dung beetle population, resulting in a more even distribution of individual dung beetles and increasing the diversity of initial population. Boundary optimization strategy is adopted to balance the requirements on search capability at different times. This approach enhances global search capability at the beginning and local search capability at the end of an iteration. Propose hybrid directional perturbation strategy to change the search direction of rolling dung beetles and stealing dung beetles. It allows for more detailed exploration and improves convergence accuracy. The Levy flight strategy is incorporated to perturb current optimal solution, enhancing algorithm’s ability to jump out of the local optimum. In order to verify performance of ECDBO algorithm, CEC2017 function tests and robotic arm IK solving experiments were conducted and compared with other algorithms. ECDBO ranked first on 21 functions in the 30 dimensions tested in CEC2017 and on 27 functions in the 100 dimensions. ECDBO performs well in the IK solving experiments of two robotic arms with better accuracy than other algorithms. The experimental results show that the ECDBO algorithm significantly improves the convergence and accuracy, and also performs excellently on the IK solving problem.

Terrain traversability analysis is essential for realizing autonomous navigation. This paper proposes a real-time light detection and ranging (LiDAR)-based network for terrain traversability classification in off-road environments. This network incorporates a fast BEV (Bird’s Eye View) feature map generation module, which performs dynamic voxelization, pillar feature encoding and scatter on point cloud, and a traversability completion module that generates accurate and dense BEV traversability maps. The network is trained with dense ground truth labels generated through offline data processing, enabling accurate and dense traversability classification of the surrounding terrain centered on the ego vehicle, with an inference speed reaching 110 + FPS. Finally, we conduct qualitative and quantitative experiments on the RELLIS-3D off-road dataset and SemanticKITTI on-road dataset, which demonstrate the efficiency and accuracy of the proposed approach.

Embracing the potential of foresight in migration policy, North Macedonia has embarked on a ground-breaking journey to institutionalize anticipatory governance through extensive capacity-building activities, imparting foresight methods to stakeholders responsible for shaping migration policies. This research provides a comprehensive overview, detailing the initiative’s origins, alignment with the Resolution on Migration Policy 2021–2025, and the accompanying Action Plan. The study assesses the impact and potential of the Anticipatory Governance in Migration in North Macedonia when fully integrated with the action plan, which focuses on data-based management that oversees the migration policy resolution and the migration policy milieu. Through a comprehensive analysis of the foresight interventions, training programs, and stakeholder engagements, this study unveils the potential impact of forward-looking planning on North Macedonia’s migration policy landscape. The conclusion and recommendations have broader significance, extending beyond North Macedonia to serve as a model for other countries confronting migration challenges in our rapidly changing world.

This article explores the transformational potential of artificial intelligence (AI), particularly generative AI (genAI) – large language models (LLMs), chatbots, and AI-driven smart assistants yet to emerge – to reshape human cognition, memory, and creativity. First, the paper investigates the potential of genAI tools to enable a new form of human-computer co-remembering, based on prompting rather than traditional recollection. Second, it examines the individual, cultural, and social implications of co-creating with genAI for human creativity. These phenomena are explored through the concept of Homo Promptus, a figure whose cognitive processes are shaped by engagement with AI. Two speculative scenarios illustrate these dynamics. The first, ‘prompting to remember’, analyses genAI tools as cognitive extensions that offload memory work to machines. The second scenario, ‘prompting to create’, explores changes in creativity when performing together with genAI tools as co-creators. By mobilising concepts from cognitive psychology, media and memory studies, together with Huizinga’s exploration of play, and Rancière’s intellectual emancipation, this study argues that genAI tools are not only reshaping how humans remember and create but also redefining cultural and social norms. It concludes by calling for ‘critical’ engagement with the societal and intellectual implications of AI, advocating for research that fosters adaptive and independent (meta)cognitive practices to reconcile digital innovation with human agency.

Changing environmental, societal, and business conditions shift the priority given to the ‘most valuable’ design solutions to be developed, which risks causing rework and mistakes during the development process. To maintain consistency among changes in what ‘value’ means in evolving business contexts, this paper presents a method – value-driven model-based systems engineering (VD-MBSE) – implemented in a software tool (named Club Design). The method is demonstrated through a case study related to aerospace electrification, highlighting its ability to maintain consistency during the iterations between business development and the design of technical solutions.

A company with n geographically widely dispersed sites seeks an insurance policy that pays off if m out of the n sites experience rarely occurring catastrophes (e.g., earthquakes) during a year. This study compares three strategies for an insurance company wishing to offer such an m-out-of-n policy, assuming the existence of markets for insurance on the individual sites with coverage periods of various lengths of a year or less. Strategy A is static: at the beginning of the year it buys a reinsurance policy on each individual site covering the entire year and makes no later adjustments. By contrast, Strategies S and C are dynamic and adaptive, exploiting the availability of individual-site policies for shorter periods than a year to make changes in the coverage on individual sites as quakes occur during the year. Strategy S uses the payoff from reinsurance when a quake occurs at a particular site to increase coverage for the remainder of the year on the sites that have not yet had quakes. Strategy C buys individual-site policies covering successive time periods of fixed length, observing the system at the beginning of each period and using cash on hand plus cash obtained from a reinsurance payoff (if any) during the previous period to decide how much cash to retain and how much reinsurance to purchase for the current period. The study relies on expected utility to determine indifference premiums and compare the premiums and loss probabilities for the three strategies.

The process to better understand the intricate evolution of our urban territories requires combining urban data from different or concurrent instances of time to provide stakeholders with more complete views of possible evolutions of a city. Geospatial rules have been proposed in the past to validate 3D semantic city models, however, there is a lack of research in the validation of multiple, concurrent and successive, scenarios of urban evolution. Using Semantic Web Ontologies and logical rules, we present a novel standards-based methodology for validating integrated city models. Using this methodology, we propose interoperable rules for validating integrated open 3D city snapshots used for representing multiple scenarios of evolution. We also implement a reproducible proof of concept test suite for applying the proposed rules. To illustrate how these contributions can be used in a real-world data validation use-case, we also provide example queries on the validated data. These queries are specifically used to construct a 3D web application for visualizing and analysing urban changes across multiple scenarios of evolution of a selected zone of interest.