In the process of trajectory optimization for robot manipulator, the path that is generated may deviate from the intended path because of the adjustment of trajectory parameters, if there is limitation of end-effector path in Cartesian space for specific tasks, this phenomenon is dangerous. This paper proposes a methodology that is based on the Pareto front to address this issue, and the methodology takes into account both the multi-objective optimization of robotic arm and the quality of end-effector path. Based on dung beetle optimizer, this research proposes improved non-dominated sorting dung beetle optimizer. This paper interpolates manipulator trajectory with quintic B-spline curves, achieves multi-objective trajectory optimization that simultaneously optimizes traveling time, energy consumption, and mean jerk, proposes a trajectory selection strategy that is based on Pareto solution set by introducing the concept of Fréchet distance, and the strategy enables the end-effector to approach the desired path in Cartesian space. Simulation and experimental results validate the effectiveness and practicability of the proposed methodology on the Sawyer robot manipulator.

The bipartite independence number of a graph $G$, denoted as $\tilde \alpha (G)$, is the minimal number $k$ such that there exist positive integers $a$ and $b$ with $a+b=k+1$ with the property that for any two disjoint sets $A,B\subseteq V(G)$ with $|A|=a$ and $|B|=b$, there is an edge between $A$ and $B$. McDiarmid and Yolov showed that if $\delta (G)\geq \tilde \alpha (G)$ then $G$ is Hamiltonian, extending the famous theorem of Dirac which states that if $\delta (G)\geq |G|/2$ then $G$ is Hamiltonian. In 1973, Bondy showed that, unless $G$ is a complete bipartite graph, Dirac’s Hamiltonicity condition also implies pancyclicity, i.e., existence of cycles of all the lengths from $3$ up to $n$. In this paper, we show that $\delta (G)\geq \tilde \alpha (G)$ implies that $G$ is pancyclic or that $G=K_{\frac{n}{2},\frac{n}{2}}$, thus extending the result of McDiarmid and Yolov, and generalizing the classic theorem of Bondy.

Multibody dynamics methodologies have been fundamental tools utilized to model and simulate robotic systems that experience contact conditions with the surrounding environment, such as in the case of feet and ground interactions. In addressing such problems, it is of paramount importance to accurately and efficiently handle the large body displacement associated with locomotion of robots, as well as the dynamic response related to contact-impact events. Thus, a generic computational approach, based on the Newton–Euler formulation, to represent the gross motion of robotic systems, is revisited in this work. The main kinematic and dynamic features, necessary to obtain the equations of motion, are discussed. A numerical procedure suitable to solve the equations of motion is also presented. The problem of modeling contacts in dynamical systems involves two main tasks, namely, the contact detection and the contact resolution, which take into account for the kinematics and dynamics of the contacting bodies, constituting the general framework for the process of modeling and simulating complex contact scenarios. In order to properly model the contact interactions, the contact kinematic properties are established based on the geometry of contacting bodies, which allow to perform the contact detection task. The contact dynamics is represented by continuous contact force models, both in terms of normal and tangential contact directions. Finally, the presented formulations are demonstrated by the application to several robotics systems that involve contact and impact events with surrounding environment. Special emphasis is put on the systems’ dynamic behavior, in terms of performance and stability.

Since its establishment in 2014, Data for Policy (https://dataforpolicy.org) has emerged as a prominent global community promoting interdisciplinary research and cross-sector collaborations in the realm of data-driven innovation for governance and policymaking. This report presents an overview of the community’s evolution from 2014 to 2023 and introduces its six-area framework, which provides a comprehensive mapping of the data for policy research landscape. The framework is based on extensive consultations with key stakeholders involved in the international committees of the annual Data for Policy conference series and the open-access journal Data & Policy (https://www.cambridge.org/core/journals/data-and-policy), published by Cambridge University Press. By presenting this inclusive framework, along with the guiding principles and future outlook for the community, this report serves as a vital foundation for continued research and innovation in the field of data for policy.

Consider a locally cartesian closed category with an object $\mathbb{I}$ and a class of trivial fibrations, which admit sections and are stable under pushforward and retract as arrows. Define the fibrations to be those maps whose Leibniz exponential with the generic point of $\mathbb{I}$ defines a trivial fibration. Then the fibrations are also closed under pushforward.

In this paper, an $\textrm{H}_{{\infty }}$ dynamic output feedback controller is experimentally implemented for the position regulation of a fully actuated tilted-rotor octocopter unmanned aerial vehicle (UAV) to improve wind disturbance rejection during station-keeping. To apply the lateral forces, besides the standard tilt-to-translate (attitude-thrust) movement, tilted-rotor UAVs can generate vectored (horizontal) thrust. Vectored-thrust is high-bandwidth but saturation-constrained, while attitude-thrust generates larger forces with lower bandwidth. For the first time, this paper emphasizes the frequency-dependent allocation of weighting matrices in $\textrm{H}_{{\infty }}$ control design based on the physical capabilities of the fully actuated UAV (vectored-thrust and attitude-thrust). A dynamic model of the tilted-rotor octocopter, including aerodynamic effects and rotor dynamics, is presented to design the controller. The proposed $\textrm{H}_{{\infty }}$ controller solves the frequency-dependent actuator allocation problem by augmenting the dynamic model with weighting transfer functions. This novel frequency-dependent allocation utilizes the attitude-thrust for low-frequency disturbances and vectored-thrust for high-frequency disturbances, which exploits the maximum potential of the fully actuated UAV. Several wind tunnel experiments are conducted to validate the model and wind disturbance rejection performance, and the results are compared to the baseline PX4 Autopilot controller on both the tilted-rotor and a planar octocopter. The $\textrm{H}_{{\infty }}$controller is shown to reduce station-keeping error by up to 50% for an actuator usage 25% higher in free-flight tests.

Developing an artificial design agent that mimics human design behaviors through the integration of heuristics is pivotal for various purposes, including advancing design automation, fostering human-AI collaboration, and enhancing design education. However, this endeavor necessitates abundant behavioral data from human designers, posing a challenge due to data scarcity for many design problems. One potential solution lies in transferring learned design knowledge from one problem domain to another. This article aims to gather empirical evidence and computationally evaluate the transferability of design knowledge represented at a high level of abstraction across different design problems. Initially, a design agent grounded in reinforcement learning (RL) is developed to emulate human design behaviors. A data-driven reward mechanism, informed by the Markov chain model, is introduced to reinforce prominent sequential design patterns. Subsequently, the design agent transfers the acquired knowledge from a source task to a target task using a problem-agnostic high-level representation. Through a case study involving two solar system designs, one dataset trains the design agent to mimic human behaviors, while another evaluates the transferability of these learned behaviors to a distinct problem. Results demonstrate that the RL-based agent outperforms a baseline model utilizing the first-order Markov chain model in both the source task without knowledge transfer and the target task with knowledge transfer. However, the model’s performance is comparatively lower in predicting the decisions of low-performing designers, suggesting caution in its application, as it may yield unsatisfactory results when mimicking such behaviors.

This research offers an adaptive model-based methodology for autonomous control of 3-RRR spherical parallel manipulator (RSPM) based on a novel modeling framework. RSPM is an overconstrained parallel mechanism that has a variety of applications in medical procedures such as ankle rehabilitation because of its precision and accuracy. However, obtaining a complete explicit dynamic model of these mechanisms for tracking purposes has been a problematic challenge due to their inherent singularities, coupling effects of the limbs, and redundant constraints imposed by the intermediate joints. This paper presents a novel algorithm to obtain the analytical kinematic solutions of RSPMs based on the closed-loop vector method, which includes constraint analysis. By incorporating constrained kinematics into the dynamic model, a comprehensive explicit dynamic solution of the non-overconstrained version 3-RCC of RSPM is developed in task space, based on screw theory and the linear homogeneous property of algebraic equations on the manipulator twist. Based on the proposed computational framework, a robust self-tuning backstepping control (STBC) strategy is applied to the robot to overcome the effect of external disturbances and time-varying uncertainties. Furthermore, an observer-based compensation (OBC) method is presented for dealing with the nonlinear hysteresis loops of the ankle during trajectory tracking purposes. The closed-loop stability of the whole system including STBC and OBC is theoretically performed by Lyapunov methods. The proposed methodologies are validated by realistic co-simulations in different scenarios. For instant, in the presence of external disturbances, the maximum tracking error norm of STBC is 37.5% less than the sliding mode approach.

Collecting network data directly from network members can be challenging. One alternative involves inferring a network from observed groups, for example, inferring a network of scientific collaboration from researchers’ observed paper authorships. In this paper, I explore when an unobserved undirected network of interest can accurately be inferred from observed groups. The analysis uses simulations to experimentally manipulate the structure of the unobserved network to be inferred, the number of groups observed, the extent to which the observed groups correspond to cliques in the unobserved network, and the method used to draw inferences. I find that when a small number of groups are observed, an unobserved network can be accurately inferred using a simple unweighted two-mode projection, provided that each group’s membership closely corresponds to a clique in the unobserved network. In contrast, when a large number of groups are observed, an unobserved network can be accurately inferred using a statistical backbone extraction model, even if the groups’ memberships are mostly random. These findings offer guidance for researchers seeking to indirectly measure a network of interest using observations of groups.