Search Results (360)

In flexible manufacturing systems, the composite mobile manipulator (CMM) is subject to nonlinear inertial disturbances arising from the dynamic coupling between the mobile platform and the robotic arm. These disturbances significantly impair positioning precision during grasping tasks. This paper addresses the dynamic decoupling of multi-body nonlinear inertial disturbances within CMM systems. Departing from the conventional “stop-then-plan” serial execution paradigm, we propose a full-cycle spatiotemporally coupled trajectory optimization method. The operation cycle is bifurcated into two synergistic stages: “dynamic calibration” and “static execution.” The dynamic calibration trajectory is pre-planned and executed synchronously during platform movement to actively compensate for inertial-induced pose deviations. Concurrently, the static execution trajectory is optimized and then triggered immediately upon platform standstill, ensuring a seamless and precise transition to the “Grasping Pose”. It is worth noting that the temporal characteristic central to this framework lies in the concurrent execution of static trajectory optimization and platform transit: by the time the platform reaches its destination, the pre-planned trajectory is already available for immediate triggering, achieving zero task-switching wait time at the planning layer. The term “zero-latency” here does not imply a fixed-cycle real-time response at the control layer, but rather the complete elimination of decision latency afforded by the parallel planning architecture. This framework eliminates computational latency, markedly enhancing operational efficiency. Key innovations include two novel constraints. First, the Adaptive Task-space Bounded Search Constraint (ATBSC) framework restricts optimization to a geometry-inspired search region, thereby enhancing search efficiency and ensuring controllable deviations. Second, the Multi-Rigid-Body Coupling Constraint (MRBCC) system explicitly models inertial transmission across motion phases to suppress pose fluctuations. The proposed framework is developed and validated within an obstacle-free workspace. In simulation-based validation on a UR10 6 degree-of-freedom manipulator model, experimental results indicate that ATBSC increases valid solution density to 84.7% and reduces average deviation by 72.8%. Furthermore, under the tested conditions, MRBCC mitigates end-effector position errors by 79.7–81.0% with a 97.5% constraint satisfaction rate. The improved Cuckoo Search algorithm (ICSA), serving as the solver component of the proposed framework, achieves an 11.9% lower fitness value and a 13.1% faster convergence rate compared to the standard Cuckoo Search algorithm in the tested scenarios, suggesting its effectiveness as a reliable solver for the constrained multi-objective trajectory optimisation problem. Full article

With the continuous advancement of marine development, underwater operational tasks are becoming increasingly diverse and complex. Addressing the limitations of traditional methods and intelligent planning—which focus solely on acquiring task skills while separating grasp planning from force planning—this paper proposes a modeling approach integrating impedance control with deep reinforcement learning. Using a five-finger humanoid underwater dexterous hand as the grasping execution platform, this method achieves collaborative decision-making between grasp planning and force control for underwater dexterous hands. First, a modular underwater dexterous grasping scenario is established. Its kinematic model and inverse solution are analyzed, and the grasping problem is modeled as a Markov decision process. Second, based on the dexterous fingertip impedance control model for simulation, a grasping strategy learning method grounded in deep reinforcement learning is constructed to address the complex control challenges posed by the high degrees of freedom of the dexterous manipulator. Finally, the Proximal Policy Optimization (PPO) algorithm is employed for grasping strategy learning. An underwater dexterous grasping parallel training and testing environment is established using the Isaac Lab simulation platform to rapidly validate the learning method. Simulation results demonstrate the proposed method’s excellent dexterous compliant control performance and strong robustness to underwater variable environments: the PPO-based impedance control scheme reduces contact force variance by 56% compared to pure position control. The average maximum contact force is suppressed to 3.26 N, representing a 60.4% reduction compared to pure position control. This study achieves the organic integration of underwater hydrodynamic compensation, adaptive impedance control, and grasping strategy learning, providing a novel and effective solution for compliant grasping control of underwater dexterous manipulators. Full article

Disruption management is critical for airline operations, yet existing recovery models often assume deterministic disruption durations, limiting their effectiveness in real-world, uncertain environments. This paper addresses the integrated airline recovery problem under uncertain disruptions. To capture this uncertainty, delay times are modeled as fuzzy variables and a fuzzy chance-constrained programming model is developed, aimed at minimizing total recovery costs. The model is transformed into a deterministic equivalent using trapezoidal fuzzy numbers. An improved Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is designed to efficiently solve the problem, balancing solution quality and computational efficiency through insert, exchange, and cancel. The local search process is enhanced by incorporating the acceptance criteria of the simulated annealing algorithm. The proposed method is validated using real-world airline data. Results show that, compared to the traditional GRASP algorithm, the improved GRASP algorithm can obtain better solutions in a shorter time; the solutions in deterministic scenarios tends to be more conservative, leading to resource waste; the proposed method can achieve airline recovery at the minimum recovery cost. Sensitivity analysis reveals that selecting an appropriate confidence level significantly influences recovery costs. This paper provides a robust framework for enhancing operational resilience and passenger satisfaction under uncertain conditions, offering practical insights for real-world application. Full article

The Yizhuan constructs a temporal ethics centered on “time” (shi 時), bridging the Will of Heaven and human affairs. This ethical paradigm is primarily manifested in the tripartite conceptual framework: First, as the transcendent source of temporal ethics, “The Will of Heaven” (tianming 天命) endows the ever-changing processes of cosmic existence with a moral teleological dimension. Secondly, “Heaven’s Timing” (tianshi 天時) manifests as the Will of Heaven within specific time and spatial contexts, guiding actors to discern the operational principles of fortune and opportunity. Finally, “the Timely Mean” (shizhong 時中), as the fundamental principle of practical life, refers to the practice of acting in harmony with the times, based on the agent’s insight into the Will of Heaven and grasp of Heaven’s Timing. Its essence lies in adapting to the times to achieve the supreme realm of morality, as outlined in the Way of the Mean (zhongdao 中道). The core purpose of the temporal ethics in the Yizhuan is to emphasize understanding moral practice within the dimension of time, opposing the abstract application of moral principles divorced from specific contexts. It requires the agent to make choices aligned with the Will of Heaven at the appropriate moment, cultivating moral character and addressing complex practical matters within the flow of time. Full article

In robotic fine-grained tasks (e.g., grasping and assembly), precise interaction requires a detailed understanding of object components. While Visual Language Models (VLMs) excel at object-level recognition, they struggle with part-level segmentation (e.g., knife handles), limiting performance in complex scenarios. VLMs face three key challenges: (1) Visual granularity mismatch—object-level features lack part-level details; (2) Semantic hierarchy gaps—parts and objects differ significantly in semantics; (3) Cross-modal bias—CLIP’s text–image alignment favors global over local features. To address these, we propose a one-stage VLM-based part segmentation method. First, the Hierarchy-Aware Feature Selection mechanism analyzes Transformer features in different hierarchies to enhance spatial and semantic precision for part segmentation. Second, the Multi-Hierarchy Feature Adapter bridges object-to-part feature granularity via the hierarchical adaptation. Finally, the Hierarchical Multimodal Alignment Module harmonizes classification accuracy and mask integrity via hierarchical alignment of vision–language, mitigating the bias of CLIP’s object-level priori knowledge. Experiments show the proposed method improves part segmentation performance for Zero-Shot, achieving 25.86% on Pascal-Part and 13.09% on ADE20K-Part (gains of +0.81% hIoU and +2.96% hIoU over baseline). This work advances robotic visual perception, with applications in intelligent manufacturing and intelligent service. Full article

This paper presents robotic manipulation methods for rapid high-arc object transfer using dynamic, non-prehensile interactions. Two complementary techniques are introduced, two-fingered scoop-and-flick and one-fingered topple-and-flick, designed for objects with low and high centers of mass, respectively. Both methods enable a robot to retrieve objects resting on a surface and launch them into controlled projectile trajectories without requiring stable grasp formation. To support these maneuvers, we develop physics-based models of object acquisition and release, and combine them with a data-driven framework. While analytical modeling guides the acquisition phase, the highly nonlinear flicking dynamics are captured using learned predictive models that enable accurate selection of control parameters for desired trajectories. The proposed techniques enable dynamic object transfer, reduced grasp planning complexity, and adaptability to environmental constraints. Experiments conducted on a custom robotic platform demonstrate reliable and accurate high-arc object transfer, in which the majority of object displacement is achieved through projectile motion. Full article

Innovation is a central driver of competitiveness, resilience, and sustainability in the agri-food sector, particularly among small and medium-sized enterprises (SMEs). However, traditional science- and technology-based models may not fully grasp the innovation dynamics in this domain, and research explicitly addressing agri-food SMEs remains limited. This study adapts, integrates, and extends existing Innovation Capability (IC) and related constructs into a unified WI–PI–IA framework (Willingness to innovate–Propensity to innovate–Innovation Ability) for agri-food SMEs. The framework is empirically tested through a sectoral quantitative case-study based on structured questionnaires administered to twenty SMEs operating in the Sardinian sheep dairy industry. The findings confirm the framework’s validity, highlighting the role of contextual factors and revealing distinct innovation patterns between cooperatives and private firms. This study is, to our knowledge, the first to conceptualise IC in agri-food SMEs as the outcome of the three above constructs and offers a comprehensive and context-sensitive approach that contributes to academic research and directs policymakers towards factors that affect agri-food SME innovation outcomes, considering their unique structures and specific challenges they face. Full article

Existing robot end-effectors design methods lack structured domain prior knowledge support and have insufficient interaction with the environment, making it difficult to guarantee the accuracy of the design results. An adaptive design method is proposed that deeply embeds domain knowledge of end effectors into the design process, treats key design parameters as environmental variables, and optimizes them adaptively through reinforcement learning algorithms in perception and feedback. In a simulation environment constructed by combining a knowledge graph, a two-finger translational gripper is used as an example robot end-effector to acquire target data via sensors, and reinforcement learning is used to adaptively optimize the gripper’s key parameters. Experiments are conducted on a simulation platform with three typical tasks, yielding the optimal parameter range. Compared to the proximal policy optimization (PPO) algorithm, which has no prior knowledge input, the knowledge graph embedding proximal policy optimization (KGPPO) algorithm improves the average reward for gripper length and gripper force by 63.96% and 43.09%, respectively, for grasping eggs. The KGPPO algorithm achieves the highest average reward and the best stability compared with other algorithms. Experiments show that this method can significantly improve the efficiency, stability, and accuracy of design parameter optimization. Full article

As critical end-effectors enabling the practical deployment of marine robotic systems, soft hands face persistent challenges including multi-finger asynchronization, unbalanced force distribution, and insufficient anti-disturbance robustness, compounded by constraints from soft material nonlinearity and harsh marine environmental disturbances. To address these limitations, this paper proposes a dexterous grasping method integrating coupled dynamical systems and adaptive force planning control, designed to enhance operational reliability in complex marine environments. An intermediate dynamic layer is embedded to ensure precise multi-finger synchronization, a hybrid force planning algorithm balances force uniformity and constraint satisfaction, and an adaptive controller synergizes with a Neo-Hookean model to compensate for nonlinear deviations. Simulations and physical experiments demonstrate that the method delivers excellent grasping stability and accuracy for uneven mass distribution targets such as cylinders and spheres, while balancing synchronization precision, constraint compliance, and anti-disturbance capability. Compared with the traditional coupled dynamical systems (DSs), the constraint violation is reduced by up to 18.2%, the friction force is increased by 4.0%, and the force distribution uniformity is improved by approximately 5.1%.Compared with the particle swarm optimization (PSO) strategy, the constraint violation is reduced by up to 50.5%, the friction force is increased by 40.9%, and the force distribution uniformity is also improved by about 5.1%. This work fills a key gap in balancing multiple performance metrics for marine soft hands, providing a reliable technical solution to accelerate the real-world deployment of marine robotic systems. Full article

This paper presents the design of a high-efficiency apple harvesting robot based on a hybrid pneumatic-electric drive system, capable of operating around the clock. The robotic system comprises a mobile platform with two degrees of freedom (DOF) and a five-DOF PRRRP manipulator for fruit picking. To meet the harvesting requirements, a spoon-shaped end-effector with pneumatic control was developed, enabling precise manipulator control and flexible grasping. The robot’s vision system integrates machine vision and deep neural network approaches. Additionally, an industrial computer and AC servo drivers were employed to control the manipulator and end-effector. An integrated nighttime illumination system allowed for all-weather operation. Initial experiments were conducted in a controlled laboratory. Subsequently, comprehensive identification and harvesting tests were performed in both laboratory and field environments to validate system robustness. Experimental results validated the effectiveness of the proposed system, demonstrating an apple harvesting success rate of 81% and an average harvesting time of 7.81 s per apple. The system achieved a fruit damage rate of less than 5% during field experiments, demonstrating its potential for gentle handling. The primary innovation of this work lies in its hybrid drive architecture and adaptive vision strategy, which together offer a cost-effective and robust solution for all-weather automated harvesting, addressing key limitations of high cost and environmental sensitivity in existing robotic harvesters. Full article

Fast and universal grasping remains a critical challenge for robotic hands operating in unstructured and industrial environments. Conventional pin-array-based robotic hands exhibit strong adaptability to objects with diverse geometries, yet their grasping speed is often limited by centralized motor-driven actuation. To address this limitation, this paper presents the development of a fast-acting cluster-tube self-adaptive robotic hand (CTSA-FA hand), which transforms traditional active actuation into a passive energy-storage-and-release-driven grasping mechanism. A spring-cam-based structure is introduced to enable rapid energy release during the grasping phase, significantly reducing the gathering time. A theoretical model of the CTSA-FA hand is established, including cam trajectory planning and mechanical analysis, to guide parameter design and performance optimization. A physical prototype is developed and experimentally validated. Experimental results demonstrate that the proposed CTSA-FA hand can complete a grasp-release frequency of approximately 3 Hz, corresponding to a grasping time of about 0.25 s per cycle, while maintaining robust adaptive grasping performance. These characteristics indicate that the proposed design is well suited for applications requiring fast and universal grasping, particularly in intelligent mining equipment and industrial automation scenarios. Full article

Sustainability, as meeting present needs without compromising future generations, is challenged by resource scarcity and economic uncertainty. An organization’s environment affects its functioning and also its resilience. While often studied at the organizational level, achieving the interconnected Sustainable Development Goals (SDGs) requires network-level cooperation. This led to asking the main question: How to develop business network resilience and sustainability in a context of scarcity of resources facing SDGs? To find the answer on that research problem, a bibliometric (5981 abstracts) and widespread systematic literature review (SLR, with 94 full text papers) was conducted. After the review of the state-of-the-art in terms of resilience, sustainability, business network, and SDGs, the ARA model (actors-resources-actions) was employed, to conceptually grasp how relational resources allow the evolution towards sustainable and resilient business networks. The analysis demonstrates that relational resources—such as trust, knowledge sharing, and collaborative partnerships—are pivotal. The study concludes that business networks have to strengthen multi-stakeholder cooperation for sustainable development, focusing on relational resources. These resources enable coordinated actions, foster resource stewardship, and enhance adaptive capacity within the network, directly supporting SDG implementation, particularly SDG 17 (Partnerships for the Goals). That SDG is like organizational umbrella for the remaining 16 SDGs and there is a need to contribute to systemic sustainable development, moving beyond isolated organizational efforts to achieve broader impact. Full article

In nature, structures such as earwig wings and mimosa leaves exhibit remarkable folding and unfolding capabilities. Inspired by these biological mechanisms, this work investigates soft foldable and torsional actuators based on Kresling crease pattern, fabricated using soft TPE 85A material through 3D printing. These actuators enable both foldable grasping and torsional motions. An analytical geometric model is developed to characterize the relationship between structural parameters and the inscribed circle area of a single-layer soft actuator, thereby elucidating their influence on contraction magnitude and relative deflection angle. Treating the soft actuator as an equivalent spring system, a mechanical model relating vacuum pressure to contraction ratio is further established, revealing an approximately linear relationship. The actuators are subsequently integrated with suction cups to form two end-effectors, a foldable soft gripper and a torsional soft gripper, and mounted onto a UR5 robotic arm via a customized flange. Demonstration experiments show that the foldable gripper achieves gentle, adaptive grasping of diverse objects, while the torsional gripper replicates human-like twisting motion, such as opening a bottle cap. This study highlights the potential of Kresling-based soft grippers for practical deployment in automated production tasks, including precision assembly and fruit harvesting. Full article

Current multi-axis force sensors often rely on complex mechanical structures or arrays of discrete transducers, resulting in larger footprints, higher complexity, and limited scalability for compact applications such as robotic fingertips or wearable tactile interfaces. To address these limitations, this paper introduces a novel optical sensing approach that uses a top-layer patterned color surface and an array of color sensors to decouple and measure normal, shear, and torsional forces within a highly compact 15 × 15 mm footprint. The patterned surface functions as a visual encoding layer, where applied forces induce measurable, direction-dependent shifts in reflected color distribution. By deploying multiple color sensors in an array, each sensor captures localized color variations, enabling spatial reconstruction of both magnitude and direction of applied loads through differential color analysis. The sensor’s performance was validated through robotic gripper integration, where it successfully provided multi-axis force feedback and enabled adaptive gripping force adjustment to achieve robust and stable object manipulation. The experimental results confirm the system’s ability to effectively sensing 3D forces and torsion forces, and support closed-loop control in adaptive robotic grasping. This design presents a scalable, low-profile alternative to conventional multi-axis force sensors, suitable for integration into space-constrained robotic and haptic systems. Full article

Stroke-induced hand motor dysfunction severely limits activities of daily living (ADL). While conventional systems face challenges in portability and sustained actuation accuracy, this work addresses these limitations through an integrated adaptive control framework and a lightweight 10-degrees-of-freedom (DoFs) tendon-driven exoskeleton. The system employs a rigid–flexible coupling design with a wearable mass under 300 g, ensuring compatibility across various finger lengths. The system is implemented via a motor imagery-based brain–computer interface (MI-BCI); by processing 64-channel electroencephalogram (EEG) signals, the system adaptively maps motor intent into three discrete grasp intensity levels (20%, 50%, and 80% maximum voluntary contraction). To reduce cognitive load and enhance system stability during rehabilitation, we propose a novel “Force–Topology Coupling” control paradigm. This paradigm functions as a synergistic filter, leveraging the correlation between intended effort level (IEL) and grasp taxonomy to map intensity levels to ADL-specific grasps (lateral, precision, and power). Validation with healthy subjects demonstrated 0° to 90° joint mobility and the successful execution of 9 ADL tasks. The results verify the efficacy of utilizing adaptive MI-BCI modulation to trigger biomechanically precise assistance, establishing a foundational computational paradigm with significant potential for clinical stroke rehabilitation. Full article