Search Results (248)

With the increasing complexity of optimization problems, existing methods are often inadequate for addressing these challenges, creating a pressing need for more versatile and robust approaches capable of solving a wide range of optimization problems. Meta-heuristic algorithms have become powerful tools in this regard, owing to their flexibility, ease of implementation, and suitability for high-dimensional and complex problems. This paper introduces the Higher-order Thinking Skills Optimizer (HTSO), a novel meta-heuristic algorithm inspired by Higher-order Thinking Skills (HOTS) from educational theory. HTSO simulates the four key aspects of HOTS: creativity, problem-solving, critical thinking, and decision-making. Creativity reflects the intrinsic human drive for knowledge, prompting exploration of unknown domains. When faced with difficulties, individuals focus on gathering information to solve problems. However, due to the inconsistent quality of information, critical thinking is essential for effectively assessing it. In HTSO, creativity and problem-solving serve as the exploration and exploitation mechanisms, respectively. Crucially, critical thinking functions as a metacognitive controller that evaluates the quality of solutions and dynamically guides the selection and adaptation of creativity strategies, thereby ensuring an effective balance between exploration and exploitation. Moreover, HTSO is designed as a user-friendly algorithm with minimal parameter tuning requirements, and its key parameter demonstrates strong robustness across diverse problem types and dimensions, which enhances its practical applicability. Extensive experiments were conducted across three CEC benchmark sets with multiple dimensions (CEC-2017: 30, 50, 100 dimensions; CEC-2020: 10, 15, 20 dimensions; CEC-2022: 10, 20 dimensions), comparing HTSO with 21 other algorithms, including several CEC champion algorithms. The results demonstrate that HTSO outperforms all comparative algorithms on most test functions, indicating high effectiveness and robustness. Furthermore, HTSO was compared with 14 algorithms on 12 real-world constrained engineering optimization problems. Finally, HTSO and 14 other algorithms were applied to unmanned aerial vehicle 3D path planning in seven different complex mountainous scenarios. HTSO also achieved the best performance across all tested real-world engineering problems and UAV path planning scenarios, consistently outperforming the comparative algorithms. These results demonstrate the effectiveness and potential of HTSO in addressing real-world optimization challenges. Full article

Surgical robots require sub-millimeter accuracy and reliable inverse kinematics across anatomies. Population-based metaheuristics address this, but static parameters may limit achieving the needed precision for clinical use. This study introduces the Rough Sets Meta-Heuristic Schema (RSMS) for dynamic, context-aware control. RSMS categorizes agents (Elite, Boundary, Poor) via Rough Set discretization based on fitness and distribution, allocating resources accordingly without problem-specific heuristics. To demonstrate the approach’s effectiveness, RSMS was implemented within Particle Swarm Optimization and evaluated as a surgical robotics inverse kinematics solver and path planner. In simulations using three surgical problems, RS-PSO allowed upgrading of the performance of the standard PSO in terms of consistent convergence and success in tight search spaces. Statistical tests confirmed these improvements. Using a 7-DOF KUKA LBR iiwa robot and surgical benchmarks of landmark acquisition, spiral trajectory tracking, and constrained path, RS-PSO achieved success rates of 100%, 67%, and 100%, respectively, meeting surgical requirements. The results demonstrate clinical gains in accuracy, consistency, and reproducibility for minimally invasive surgery. These findings support the practical advantages of RS-PSO and, more importantly, show that the RS-MH framework can be used as a general, reusable tool to improve the robustness, precision, and reproducibility of many swarm-based meta-heuristics for surgical robotics and other applications. Full article

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position $(9.97\times {10}^{-5}$ vs. $7.39\times {10}^{-5}$) and velocity $(2.52\times {10}^{-5}$ vs. $3.76\times {10}^{-5})$, outperforming periodic communication. Full article

This study benchmarks three ROS 2 Navigation2 local controllers—Dynamic Window Approach Based (DWB), Regulated Pure Pursuit (RPP), and Model Predictive Path Integral (MPPI)—under three complementary operational stressors in simulation: (i) a structured corridor with a transient dynamic obstacle, (ii) a sloped environment where terrain inclination biases a planar 2D LiDAR costmap through spurious occupancy projections, and (iii) a narrow corridor that amplifies inflation effects. A reproducible rosbag2-based protocol records five key performance indicators per trial: time-to-goal, lateral tracking RMSE, stopped time, heading oscillations, and control effort. With 15 independent repetitions per cell (scene × controller × direction), the design yields 270 trials. The results expose complementary value profiles: RPP minimizes mission time, DWB produces the fewest heading oscillations through critic-based shaping, and MPPI achieves the lowest control effort via smooth trajectory generation. In the sloped scene, the tracking RMSE differences compress across all controllers—a signature of a perception-limited regime in which costmap bias overshadows controller logic. These findings translate into an actionable controller-selection guide and a reproducible baseline for quantifying gains from upstream perception and cost-representation improvements. In concrete terms, we contribute (i) a controlled benchmark with fixed planning, localization, and costmaps, (ii) full configuration disclosure (controller parameters, costmap settings, and software versions with package pinning), and (iii) a scene-specific costmap distortion index that links slope-induced local cost bias to measurable performance shifts, underpinning a decision matrix for controller selection in semi-structured environments. Full article

With the rapid development of artificial intelligence and robotics, the application of robotics in the chemical domain is driving a transformation toward intelligent and large-scale research in chemistry and material science. However, sample weighing and synthesis reactions constitute critical stages in chemical experiments, which presents significant challenges for robotic gripping of reagent tubes to achieve precise measurements and collision-free path planning autonomously. Therefore, this study aims to address automation of manipulation in chemical experiments, achieving collision-free path planning and optimization under multi-objective constraints. Specifically, the trajectory planning problem for such tasks is formulated as a multi-objective optimization to minimize motion time, joint jerk and energy consumption. Then, an improved optimized multi-objective particle swarm optimization (OMOPSO) algorithm that incorporates seventh-order polynomial interpolation is proposed to improve the smoothness of robotic motion trajectory. A uniform Pareto front is obtained through a reference vector-guided leader selection mechanism, and an update strategy based on $\epsilon$-domination, and inflection point selection is proposed to balance the convergence and diversity of the solution set. Finally, simulation results and demonstrations on a manipulation platform have fully validated the feasibility and practicality of the proposed method, which further provides a reference for robotic execution of chemical experiments. Full article

This work is axiomatic and structural in nature and is not intended as a phenomenological physical theory, but as a framework clarifying minimal informational primitives from which geometric and dynamical descriptions may emerge. We present a background-independent framework in which physical geometry, interaction-like forces, and spacetime arise as effective descriptions of constrained relational information rather than as fundamental entities. The only primitive structure is a network of degrees of freedom linked by admissible informational relations, each subject to quantifiable constraints on accessibility or flow. The motivation is to identify whether a single minimal relational primitive can account jointly for the emergence of geometry, forces, and spacetime, without presupposing a manifold, fields, or fundamental interactions. The framework is formalized using weighted relational graphs in which constraint weights encode limitations on information flow between degrees of freedom. Effective geometry is defined operationally through minimal constraint cost along relational paths, yielding an emergent metric without assuming spatial embedding. Relational evolution is modeled via a minimal configuration-space dynamics defined by local rewrite moves, and a statistical description is introduced through an informational action that governs coarse-grained response rather than serving as a fundamental dynamical law. Curvature-like observables are defined using transport-based comparisons of local accessibility structure. Within this setting, metric structure emerges from constrained relational accessibility, while curvature-like behavior arises from heterogeneity in constraint structure. Effective forces appear as entropic or informational action gradients with respect to coarse-grained control parameters that modulate relational constraints, and are interpreted as emergent responses rather than primitive interactions. A finite worked example explicitly demonstrates the emergence of nontrivial distance, curvature proxies, and an effective force via geodesic switching under constraint variation, without assuming fundamental spacetime, fields, or particles. The results support an interpretation in which geometry, forces, and spacetime are representational features of constrained information flow rather than fundamental elements of physical law. The framework clarifies conceptual distinctions and points of compatibility with existing approaches to emergent spacetime, and it outlines qualitative expectations for regimes in which smooth geometric descriptions are expected to break down. The work delineates the scope and limits of geometric description without proposing a complete phenomenological theory. Full article

RRT*, which augments the RRT with the $\mathrm{C}\mathrm{h}\mathrm{o}\mathrm{o}\mathrm{s}\mathrm{e}\mathrm{P}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}$ and Rewire steps, is a widely used algorithm for path planning. RRT*-based algorithms are effective for improving the quality of paths in the Rewire step. However, as the expansion continues, there are more nodes to be inspected, which can slow down the path search. In addition, due to the parameters set in the rewire step, a trade-off issue between path quality and planning time arises. In this paper, we propose Straight-RRT to improve upon these limitations. Straight-RRT applies the Straight strategy to rapidly obtain an initial path and then returns a refined path using the MoveParent strategies. Accordingly, Straight-RRT adopts the following mechanisms. (1) The Straight strategy is applied for rapidly finding an initial path. This procedure quickly finds a feasible initial path. (2) MoveParent is a strategy inspired by parametric equations for path optimization. This complements the weaknesses of the Straight strategy and the limitations of the triangle inequality. The MoveParent strategy is applied bidirectionally. These procedures progressively refine the path and improve efficiency. We propose an algorithm that is faster than other algorithms using these strategies and minimizes the trade-off caused by parameter settings. In the experimental comparison results across most environments, our approach achieved shorter planning times than the compared baseline algorithms and produced paths of comparable quality. Full article

Static honeypot deployment and one-shot attack-path analysis often become ineffective against adaptive adversaries because fixed decoy layouts are easy to fingerprint and risk estimates quickly go stale. This paper presents a unified, mathematically grounded TrapManager framework that couples graph representation learning with budget-constrained combinatorial optimization for dynamic cyber deception. We model attacker progression on vulnerability-based attack graphs and learn context-aware node embeddings using a Graph Attention Network (GAT) that fuses vulnerability-driven risk signals (e.g., CVSS-derived node scores) with structural features. The learned representations are used to estimate edge plausibility and rank candidate source–target routes at the path level. Given limited resources, we formulate pointTrap placement as a Mixed-Integer Programming (MIP) problem that maximizes the expected interception of high-risk paths while penalizing deployment cost under explicit budget constraints, including mandatory coverage of the top-ranked critical paths. To enable online adaptiveness, a pointTrap-triggered, event-driven feedback mechanism locally amplifies risk around alerted regions, updates path weights without retraining the GAT, and re-solves the MIP for rapid redeployment. Experiments on MulVAL-generated benchmark attack graphs and cross-domain transfer settings demonstrate fast convergence, strong discrimination between attack and non-attack edges, and early interception within a small number of hops even with minimal decoy budgets. Overall, the proposed framework provides a scalable and resource-efficient approach to closed-loop attack-path defense by integrating attention-based learning and integer optimization. Full article

High-altitude low-speed aerostats are ideal unmanned platforms for communication coverage, remote sensing, environmental monitoring, aviation support, and other applications. To address practical operational needs such as rapid emergency deployment, this paper proposes a path planning method for low-speed aerostats based on the Markov decision process (MDP). The method is optimized to minimize deployment time while accounting for discrepancies between forecasted and actual wind fields. An uncertain wind field model is established to incorporate wind-related uncertainties into the MDP framework, with key parameters—including the state space, action set, immediate reward, and transition probability—designed accordingly. A mathematical model is formulated to address the global path planning problem under complex constraints, such as horizontal wind resistance capability, altitude control capacity, and flight time requirements. Simulation results demonstrate that the proposed method enables aerostats to achieve optimal 2D and 3D path planning under complex constraints. Furthermore, regional reachability is quantitatively analyzed, providing technical support for the rapid deployment of aerostats to target areas in practical applications. The core innovations of this work lie in the integration of a probabilistic wind uncertainty model with a constraint-aware MDP framework, enabling optimal 3D path planning and quantitative reachability analysis for high-altitude low-speed aerostats. Full article

We introduce a geometric framework in which classical transforms are represented as coordinate charts on a symbolic manifold. The construction defines symbolic curvature (κ), strain (τ), compressibility (σ), and the ratio Γ = κ/τ, which together provide a diagnostic coordinate system for comparing representational stability across chart transitions. Within this setting, transforms such as Fourier, Laplace, wavelet, Jordan, and polynomial projection can be treated as charts connected by transition maps that preserve Γ on specified domains. We also introduce a symmetric positive-definite metric tensor $G_{ab}$ to quantify displacement in the invariant coordinates and to formalize minimal-effort paths (geodesics) under modeling assumptions stated in the text. The resulting framework provides a reproducible screening method for evaluating transform stability, diagnosing closure failure, and comparing transform behavior under a shared set of invariants. Full article

Soft errors and Hardware Trojans (HTs) constitute major reliability concerns, and in combination they can pose an even greater threat to circuit security. The main aim of this research is to develop and implement a reliability-based HT and to identify the optimal regions for its injection, enabling the creation of challenging benchmarks for evaluating detection techniques. In this context, a reliability-based HT is designed and evaluated using different components to achieve the required time overhead. Next, a method that combines the generation and propagation of Single-Event Transients (SETs), while accounting for both masking effects and the design’s timing constraints, is employed to efficiently identify the most vulnerable and critical gates. The sensitive gates selected for HT insertion exhibit 50–70% vulnerability to soft errors. At the same time, their insertion and the resulting path delay overhead must not violate the design’s timing constraints, and the additional area must remain below 10% of the total area. These three conditions ensure that the inserted HTs remain stealthy and, therefore, challenging to detect. The experimental results demonstrate that selecting this category of gates is highly effective, as it leads to a significant increase in the number of soft errors and, consequently, aggravates circuit vulnerability with minimal impact on the design. On average, the targeted gates exhibit a 130% increase in sensitivity, and the overall Soft Error Rate (SER) increases by 78%, confirming the importance of providing robust benchmarks to combat potential attacks of this kind. Full article

Directed Energy Deposition-Laser (DED-L) enables high-performance coatings through melting and successive powder deposition. Its compositional flexibility suits functionally graded layers that enhance corrosion and wear resistance. This study aimed to improve parameters for producing dense, defect-free, graded Ni- and Fe-based coatings by varying the scanning speed and deposition strategy (monodirectional versus bidirectional, with/without layer rotation), while keeping the power and hatch distance constant. Laser and electron microscopy were used to link parameters to porosity and uniformity. Optimal settings minimized pores, improved interlayer bonding and preserved geometry; inadequate parameters yielded porous, irregular deposits. A bidirectional path with 90° rotation appeared best. Ongoing research activities are needed to assess its properties. Full article

In modern shipbuilding, the structural complexity of ship components and the constrained workspace make robotic grinding prone to collisions. To improve safety and stability, this paper proposes a collision-free posture optimization method for ship-component operations. First, forward and inverse kinematic models are established, and postures along the path are organized into a directed graph. Feasible postures are then identified under joint-limit and singularity constraints. Directed bounding boxes and the GJK collision detection algorithm are applied to construct a collision-free posture set. An improved A* algorithm is then introduced. It incorporates a multi-source heuristic based on joint-space geometric distance and a safety-distance penalty to compute an optimal posture sequence with minimal joint deviation. This design promotes smooth transitions between consecutive postures. Simulation results show that the proposed method avoids robot–workpiece interference in constrained environments and improves obstacle avoidance and motion smoothness. Compared with the standard A* algorithm, the proposed approach reduces search time by 15.8% and increases the minimum safety distance by nearly fivefold. Compared with a non-optimized posture sequence, cumulative joint variation is reduced by up to 92.5%. The joint amplitude range decreases by an average of 41.2%, and the standard deviation of joint fluctuations decreases by 37.8%. The proposed method provides a generalizable solution for robotic measurement, assembly, and machining in complex and confined environments. Full article

Onboard models serve as the foundation for the advanced control and fault diagnosis of aero-engines. Currently, to address the issues of high computational complexity and insufficient real-time performance in component-level aero-engine models, three improvement methods are proposed: constructing the Jacobian matrix along the reverse flow path to avoid redundant calculations; reducing the number of initial guess variables and equations in the engine co-working system through aerothermodynamic analysis, thereby achieving dimensionality reduction in the nonlinear equation sets; and leveraging the minimal variation in Jacobian inverse elements across the full flight envelope to replace them with fixed gains, thus simplifying transient performance calculations. Simulation results demonstrate that, compared to the regular Newton-Raphson method, the reverse flow method reduces the steady-state, regular transient, and small-step transient calculation time by 27.6%, 33.9%, and 30.8%, respectively, with a maximum relative error within 1.6%; the dimensionality reduction method for equations cuts the steady-state, regular transient, and small-step transient calculation time by 20.1%, 11.4%, and 11.8%, with a maximum relative error within 1.4%; and the constant Jacobian matrix inverse method reduces the calculation time by 50.9% during full flight envelope transient performance simulation, with a maximum relative error below 1.6%. All methods improve real-time performance under rated operating conditions. However, only the reverse flow method preserves both high efficiency and accuracy under off-design operating conditions. Full article

Drone swarms are increasingly used in critical civil applications like agriculture, machine maintenance and search-and-rescue, where maintaining network connectivity is essential for effective coordination. However, harsh environmental conditions can lead to drone failures, risking network fragmentation. To improve resilience, designing k-connected networks, where up to $k-1$ drone failures can be tolerated without losing connectivity, offers a practical solution by providing multiple independent communication paths between drones. The k-connectivity restoration problem is repositioning drones to achieve k-connectivity with minimal movement. In this study, we address this NP-Hard problem and propose novel solutions. Unlike existing k-connectivity restoration algorithms that constrain drones to predefined points, our model allows free repositioning within the mission area, increasing flexibility but also expanding the solution space and complexity. To address this problem, we propose three center-based algorithms that guide drones toward different central points computed from the network layout: in the first algorithm (ORIGIN), the center point is the geometric origin of the mission area; in the second algorithm (CENTROID), nodes move toward the centroid of all drone positions; and in the third algorithm, the center position is defined as the CENTer of the FARthest nodes (CENTFAR). We also introduce a Minimum Spanning Tree-based (MST) algorithm that moves drones along a minimum spanning tree to achieve and theoretically guarantee k-connectivity. Besides checking k-connectivity after each individual move, we also develop group-based variants where all drones move simultaneously and k-connectivity is checked afterward. We conduct comprehensive simulations under varying drone counts, network sizes, k values, and transmission ranges to evaluate the effectiveness and scalability of the proposed algorithms. CENTFAR provides the best movement efficiency among the center-based algorithms, slightly outperforming CENTROID and ORIGIN and achieving up to 21% lower total and 29% lower maximum movement than MST in smaller areas and higher k values. MST, however, performs best under low k and high transmission ranges, offering up to 57% lower total movement and 20% lower execution time than CENTFAR. Group-based variants accelerate convergence (up to a tenfold speedup) at the cost of a slight increase in movement. Our findings reveal that MST is ideal for low-k settings, while CENTFAR is better suited for high-connectivity deployments. Full article