The paper presents an enhanced method for unknown parameter estimation and nonlinear controller adaptation that combines the concept of unfalsification with the genetic algorithm (GA). This approach is based on the measured data and employs a bank of nonlinear controllers designed to dynamically adjust to the system’s evolving conditions. The controllers in the bank can be switched to meet the system’s requirements. This method is applied to an autonomous underwater vehicle (AUV) with uncertain parameters. Using the unfalsification method, these uncertain parameters are estimated, and a suitable controller is selected from the bank to guide the AUV along a desired trajectory. Additionally, an artificial intelligence technique, such as the GA, is employed to update the controller bank, resulting in versatile and optimised candidates. The simulation results obtained in the MATLAB/Simulink environment show that in the environment considered in this paper, the Adaptive Unfalsification algorithm in conjunction with GA estimates the unknown parameter values better than the sole GA-optimised values. Also, the convergence of the actual trajectory of the AUV using the Adaptive Unfalsification algorithm in conjunction with GA is faster and better than the sole GA-optimised algorithm. Furthermore, a survey of experimental results from established literature is included to evaluate the practical implementation of the proposed design, which concludes that within a reasonable time, the Adaptive Unfalsification algorithm in conjunction with GA can be implemented in commercially available processors.

Stone locales together with continuous maps form a coreflective subcategory of spectral locales and perfect maps. A proof in the internal language of an elementary topos was previously given by the second-named author. This proof can be easily translated to univalent type theory using resizing axioms. In this work, we show how to achieve such a translation without resizing axioms, by working with large, locally small, and small-complete frames with small bases. This requires predicative reformulations of several fundamental concepts of locale theory in predicative HoTT/UF, which we investigate systematically.

This paper shows how to set up Fine’s “theory-application” type semantics so as to model the use-unrestricted “Official” consequence relation for a range of relevant logics. The frame condition matching the axiom $(((A \to A) \land (B \to B)) \to C) \to C$—the characteristic axiom of the very first axiomatization of the relevant logic E—is shown forth. It is also shown how to model propositional constants within the semantic framework. Whereas the related Routley–Meyer type frame semantics fails to be strongly complete with regards to certain contractionless logics such as B, the current paper shows that Fine’s weak soundness and completeness result can be extended to a strong one also for logics like B.

In complex work environments, improving efficiency and stability is an important issue in robot path planning. This article proposes a new path optimization algorithm based on pseudospectral methods. The algorithm includes an adaptive weighting factor in the objective function, which automatically adjusts the quality of the path while satisfying the performance indicators of the shortest time. It also considers kinematic, dynamic, boundary, and obstacle constraints, and applies the Separating Axis Theorem collision detection method to improve computational efficiency. To discretize the continuous path optimization problem into a nonlinear programming problem, the algorithm utilizes Chebyshev polynomials for the interpolation of state and control variables, along with the adoption of the Lagrange interpolation polynomial to approximate the curve. Finally, it solves the nonlinear programming problem numerically using CasADi, which supports automatic differentiation. The results of the simulation demonstrate that the path optimized by the adaptive-weight pseudospectral method can satisfy various constraints and optimization objectives simultaneously. Experimental verification confirms the efficiency and feasibility of the proposed algorithm.

Robot hands are essential components of robots; however, the hand of more complex spatial mechanisms with coupling chains is rarely proposed. This paper proposes a hybrid hand with three underactuated finger plane limbs connected by a flexible closed-loop chain. The degree of freedom (DOF) of the hybrid hand is equal to the number of motors before grasping the object. When the contact force appears between the fingertips and the object, the flexible linkages deform, allowing the hybrid hand to maintain adaptability during contact. As the three fingers make contact with the object, the hybrid hand forms a closed-loop chain with the object, ensuring that the overall DOF remains consistent with the number of motors. Firstly, the hybrid hand’s structural characteristics and DOF are analyzed. Secondly, the kinematics of the hybrid hand are derived, and the relationships among the spring deformation, the kinematics of the fingertip and the input of the hybrid hand are obtained according to the geometric constraints. Thirdly, based on the kinematic results and the principle of virtual work method, the coupling dynamics formula of the hybrid hand is established, and the relationship between the dynamic driving force, dynamic constrained force, spring force and the force acting on the object is solved. Finally, the simulation model of the hybrid hand is constructed in MATLAB to validate the theoretical solution, and the merits of the hybrid hand were confirmed by prototype experiments. This paper aims to support a theoretical foundation for the intelligent control of novel hybrid hands.

Following the large-scale Russian invasion in February 2022, policymakers and humanitarian actors urgently sought to anticipate displacement flows within Ukraine. However, existing internal displacement data systems had not been adapted to contexts as dynamic as a full-fledged war marked by uneven trigger events. A year and a half later, policymakers and practitioners continue to seek forecasts, needing to anticipate how many internally displaced persons (IDPs) can be expected to return to their areas of origin and how many will choose to stay and seek a durable solution in their place of displacement. This article presents a case study of an anticipatory approach deployed by the International Organization for Migration (IOM) Mission in Ukraine since March 2022, delivering nationwide displacement figures less than 3 weeks following the invasion alongside near real-time data on mobility intentions as well as key data anticipating the timing, direction, and volume of future flows and needs related to IDP return and (re)integration. The authors review pre-existing mobility forecasting approaches, then discuss practical experiences with mobility prediction applications in the Ukraine response using the Ukraine General Population Survey (GPS), including in program and policy design related to facilitating durable solutions to displacement. The authors focus on the usability and ethics of the approach, already considered for replication in other displacement contexts.

In this work, we focus on stochastic modeling for sustainable systems and introduce the family of r-modified reliability systems. This new family generalizes classical reliability systems studied in the literature by considering the components in the system to exhibit a kind of dependence that relaxes the component operating requirements and provides energy and resource efficiency. From a theoretical viewpoint, such a dependence is modeled with the use of a modified binary sequence. We then derive the reliability of two members of the family, i.e., the r-modified-k-out-of-n:F system and the r-modified-consecutive-k-out-of-n:F system, under different assumptions on the component reliabilities by using a variety of approaches, including Markov chains, combinatorial methods, and simple probabilistic arguments. We finally give some examples of real-life systems wherein the developed models and results are applicable and present the corresponding numerical results.

Automatic visual localization of electric vehicle (EV) charging ports presents significant challenges in uncertain environments, such as varying surface textures, reflections, lighting and observation distance. Existing methods require extensive real-world training data and well-focused images to achieve robust and accurate localization. However, both requirements are difficult to meet under variable and unpredictable conditions. This paper proposes a 2-stage vision-based localization approach. Firstly, the image synthesis technique is used to reduce the cost of real-world data collection. A task-oriented parameterization protocol (TOPP) is proposed to optimize the quality of the synthetic images. Secondly, an autofocus and servoing strategy is proposed. A hybrid detector is employed to enhance sharpness assessment performance, while a visual servoing method based on single exponential smoothing (SES) is developed to enhance stability and efficiency during the search process. Experiments were conducted to evaluate image synthesis efficiency, detection accuracy, and servoing performance. The proposed method achieved 99% detection accuracy on the real-world port images, and guided the robot to the optimal imaging position within 16 s, outperforming comparable approaches. These results highlight its potential for robust automated charging in real-world scenarios.

With the increasing manufacturing of electric vehicles, car battery recycling is crucial for environmental sustainability. The disassembly of car batteries includes critical health hazards for the operator, due to potential chemical reactions or physical injuries. These reasons make robots particularly interesting for automatic disassembly. This paper proposes a systematic approach to automation and human–robot cooperation in car battery disassembly tasks with a case study on screw removal. A novel parameter is proposed to evaluate whether a human operator or a robot is more appropriate for each specific task, considering both performance and associated risks. The proposed metrics are validated with an experimental example, in which the performance of a robot and a human on a screw-removal task is compared numerically using statistical methods. The advantages and disadvantages of both options are examined through the application and show how the new performance criterion effectively provides insights into the distribution of tasks between humans and robots.