Outdoor mobile robots must navigate uneven terrains with obstacles that sometimes cannot be avoided; therefore, strategies have been developed for robots to overcome them. In most cases, these strategies have been modeled considering movement over horizontal surfaces and with the robot positioned directly in front of the obstacle, but this idealization does not occur in most real cases. Therefore, this article describes a strategy for obstacle overcoming, useful when the robot faces obstacles on inclined terrains and at an oblique angle relative to the robot’s trajectory, considering rollover stability during the process, based on the reaction force criterion. This strategy can be used by mobile robots with wheels and an articulated arm whose end effector can contact the ground, and it consists of a sequence of standard movements that include the use of the arm, whose variable location was defined through a system developed using fuzzy logic. The designed strategy was validated through simulations and then implemented on the Lázaro robot, verifying its effectiveness through experimental tests. With it, the robot can overcome obstacles such as steps, ramps, and ditches from any position; additionally, it increased the ability to overcome obstacles with a height close to twice the radius of the robot’s wheels.

Parallel manipulators (PMs) are adopted in different fields due to their superior characteristics compared to serial manipulators. PMs with flexible links are likely more energy efficient and have high dynamic performance since they are lighter than those with rigid links. On the other hand, due to their lightweight design, the flexibility can lead to undesired deformation and vibration, decreasing the tracking trajectory and transient errors. This work proposes a two-loop active vibration control strategy, using strain gauges and piezoelectric lead zirconate (PZT) actuators, to compensate for the undesired effect of the flexibility. A pose control loop exploits the sliding mode control using data collected from images acquired by an oCam-5CRO-U camera, while the active vibration control loop uses strain gauge sensors and PZT actuators. Strain gauges are responsible for measuring the deformation of each link, and after being treated by digital filters, these signals are applied to the PZT actuator. Combining both loops allows the manipulator to be guided over the desired trajectory with positive vibration attenuation. The results reveal that the presence of the PZT on both sides of the flexible links increases the links’ rigidity, yielding overshoot and vibration reduction during the manipulator’s motion. In addition, the maximum peak is significantly attenuated, and the overall oscillations are also positively reduced when using the two-loop active control strategy. The root mean square error quantifies this attenuation, showing an average reduction of 30% in the corresponding step input directions. Therefore, the proposal improves the system performance by enhancing the tracking trajectory with lower vibrations.

Locomotion control of inchworm robots presents significant challenges due to their highly nonlinear dynamics and complex interactions with the environment. Traditional control methods often struggle with achieving precise tracking and adaptive performance in dynamic conditions. To address these limitations, this article proposes a novel data-driven compound control system that integrates fractional proportional-integral derivative (FPID) control with Koopman operator theory. Unlike conventional approaches, which rely on direct nonlinear control or simplified linear approximations, our method leverages data-driven modeling to transform the nonlinear dynamics into a linear representation, making control design more systematic and scalable. A deep neural network is trained to identify the Koopman operator, enabling an FPID controller to operate within this transformed space for improved tracking accuracy and robustness. The proposed framework is validated using NVIDIA Isaac SIM simulation software, demonstrating superior locomotion efficiency and tracking performance compared to existing control strategies. This study advances the control of bio-inspired robots by bridging fractional-order control with data-driven Koopman-based modeling, addressing the fundamental challenge of achieving high-precision locomotion in complex environments.

Connecting individual robots to form an inter-reconfigurable system with a flexible base size enhances the ability to access and cover areas for cleaning and maintenance tasks. Given that increased configuration complexity expands the search space dimension, an optimal routing solution ensuring efficiency is essential. In this paper, we present an inter-reconfigurable multi-robot system capable of adjusting the bases of its two units, along with an optimal path planning approach for confined spaces based on a modified informed rapidly-exploring random tree algorithm by a greedy set (RIRRT*). We validate the navigation of the proposed inter-reconfigurable platform using RIRRT* for four informed dimensional search spaces as a case study in both simulated and real-world environments. The proposed path planning method for the inter-reconfigurable system outperformed conventional strategies, achieving significant reduction in both execution time and energy utilization.

Traditional path planning algorithms often encounter challenges in complex dynamic environments, including local optima, excessive path lengths, and inadequate dynamic obstacle avoidance. Thus, the development of innovative path planning algorithms is essential. This article addresses the challenges of mobile robot path planning in complex environments, where traditional methods often converge to local optima, leading to suboptimal path lengths, and struggle with dynamic obstacle avoidance. To overcome these limitations, we propose an integrated algorithm, the enhanced sparrow search algorithm combined with the dynamic window approach (ESSA-DWA). The algorithm first utilizes ESSA for global path planning, followed by local path planning facilitated by the DWA. Specifically, ESSA incorporates Tent chaotic initialization to enhance population diversity, effectively mitigating the risk of premature convergence to local optima. Moreover, dynamic adjustments to the inertia weight during the search process enable an adaptive balance between exploration and exploitation. The integration of a local search strategy further refines individual updates, thereby improving local search performance. To enhance path smoothness, the Floyd algorithm is employed for path optimization, ensuring a more continuous trajectory. Finally, the combination of ESSA and DWA uses key nodes from the global path generated by ESSA as reference points for the local planning process of DWA. This approach ensures that the local path closely follows the global path while also enabling real-time dynamic obstacle detection and avoidance. The effectiveness of the algorithm has been validated through both simulations and practical experiments, offering an efficient and viable solution to the path planning problem.

Spherical robots face significant challenges in motion control on non-horizontal terrains, such as slopes, due to their unique spherical structure. This paper systematically investigates the motion stability of spherical robots on inclined surfaces through modeling, control algorithm design, and experimental validation. Precise Equilibrium Modeling: Using the virtual displacement method, the precise equilibrium equation for spherical robots on slopes is derived, addressing the issue of insufficient accuracy in describing the actual center of gravity in existing studies. Control Algorithm Design: For known slope conditions, a Backstepping Control (BSC) algorithm is designed, demonstrating excellent tracking performance. For unknown slope conditions, an Adaptive Backstepping Control (ABSC) algorithm is proposed, which significantly reduces tracking errors and enhances system robustness through parameter adaptation. Simulation and Physical Validation: Simulations confirm the effectiveness of the algorithms: BSC achieves high-precision control under known slopes, while ABSC exhibits strong adaptability under unknown slopes. Physical experiments validate the stability of the algorithms in a $5^\circ$ slope environment, demonstrating reliable performance across different control angles.

The underwater target detection is affected by image blurring caused by suspended particles in water bodies and light scattering effects. To tackle this issue, this paper proposes a reparameterized feature enhancement and fusion network for underwater blur object recognition (REFNet). First, this paper proposes the reparameterized feature enhancement and gathering (REG) module, which is designed to enhance the performance of the backbone network. This module integrates the concepts of reparameterization and global response normalization to enhance the network’s feature extraction capabilities, addressing the challenge of feature extraction posed by image blurriness. Next, this paper proposes the cross-channel information fusion (CIF) module to enhance the neck network. This module combines detailed information from shallow features with semantic information from deeper layers, mitigating the loss of image detail caused by blurring. Additionally, this paper replace the CIoU loss function with the Shape-IoU loss function improves target localization accuracy, addressing the difficulty in accurately locating bounding boxes in blurry images. Experimental results indicate that REFNet achieves superior performance compared to state-of-the-art methods, as evidenced by higher mAP scores on the underwater robot professional competitionand detection underwater objects datasets. REFNet surpasses YOLOv8 by approximately 1.5% in $mAP_{50:95}$ on the URPC dataset and by about 1.3% on the DUO dataset. This enhancement is achieved without significantly increasing the model’s parameters or computational load. This approach enhances the precision of target detection in challenging underwater environments.

The documentation of sound art installation has received scant research attention. This ARTICLE investigates the sensory experience of spatial audio recordings of two sound art installations: Écosystème(s) by Estelle Schorpp and Générateur Stochastique by Jean-Pierre Gauthier. Interactive listening sessions WERE CONDUCTED with participants from different fields of expertise: sound artists, sound engineers, new media and sound art curators, and new media and sound art conservators. Listening sessions were followed by semi-structured interviews questioning the selection of significant positions in time and space in the recordings. The analysis revealed a broad range of listening strategies which expand the literature on documentation frameworks. This research shows the potential for methodologically including the sensory experience in the documentation of sound art installations and discusses the use of spatial recording as a tool for the specification of documentation in a multi-expertise context.

The selection of random sampling points is crucial for the path quality generated by probabilistic roadmap (PRM) algorithm. Increasing the number of sampling points can enhance path quality. However, it may also lead to extended convergence time and reduced computational efficiency. Therefore, an improved probabilistic roadmap algorithm (TL-PRM) is proposed based on topological discrimination and lazy collision. TL-PRM algorithm first generates a circular grid area among start and goal points. Then, it constructs topological nodes. Subsequently, elliptical sampling areas are created between each pair of adjacent topological nodes. Random sampling points are generated within these areas. These sampling points are interconnected using a layer connection strategy. An initial path is generated using a delayed collision strategy. The path is then adjusted by modifying the nodes on the convex outer edges to avoid obstacles. Finally, a reconnection strategy is employed to optimize the path. This reduces the number of path waypoints. In dynamic environments, TL-PRM algorithm employs pose adjustment strategies for semi-static and dynamic obstacles. It can use either the same or opposite pose adjustments to avoid dynamic obstacles. Experimental results indicate that TL-PRM algorithm reduces the average number of generated sampling points by 70.9% and average computation time by 62.1% compared with PRM* and PRM-Astar algorithms. In winding and narrow passage maps, TL-PRM algorithm significantly decreases the number of sampling points and shortens convergence time. In dynamic environments, the algorithm can adjust its pose orientation in real time. This allows it to safely reach the goal point. TL-PRM algorithm provides an effective solution for reducing the generation of sampling points in PRM algorithm.

The robot manipulator is commonly employed in the space station experiment cabinet for the disinfection task. The challenge lies in devising a motion trajectory for the robot manipulator that satisfies both performance criteria and constraints within the confined space of an experimental cabinet. To address this issue, this paper proposes a trajectory planning method in joint space. This method constructs the optimal trajectory by transforming the original problem into a constrained multi-objective optimization problem. This is then solved and integrated with the seventh-degree B-spline curve. The optimization algorithm utilizes an indicator-based adaptive differential evolution algorithm, enhanced with improved Tent chaotic mapping and opposition-based learning for population initialization. The method employed the Fréchet distance to design a trajectory selection strategy based on the Pareto solutions to ensure that the planned trajectory complies with Cartesian space requirements. This allows the robot manipulator end-effector to approximate the desired path in Cartesian space closely. The findings indicate that the proposed method can effectively design the robot manipulator trajectory, considering both joint motion performance and end-effector motion constraints. This ensures that the robot manipulator operates efficiently and safely within the experimental cabinet.

We present a short and simple proof of the celebrated hypergraph container theorem of Balogh–Morris–Samotij and Saxton–Thomason. On a high level, our argument utilises the idea of iteratively taking vertices of largest degree from an independent set and constructing a hypergraph of lower uniformity which preserves independent sets and inherits edge distribution. The original algorithms for constructing containers also remove in each step vertices of high degree, which are not in the independent set. Our modified algorithm postpones this until the end, which surprisingly results in a significantly simplified analysis.

In this work, the problem of reliably checking collisions between robot manipulators and the surrounding environment in short time for tasks, such as replanning and object grasping in clutter, is addressed. Geometric approaches are usually applied in this context; however, they can result not suitable in highly time-constrained applications. The purpose of this paper is to present a learning-based method able to outperform geometric approaches in clutter. The proposed approach uses a neural network (NN) to detect collisions online by performing a classification task on the input represented by the depth image or point cloud containing the robot gripper projected into the application scene. Specifically, several state-of-the-art NN architectures are considered, along with some customization to tackle the problem at hand. These approaches are compared to identify the model that achieves the highest accuracy while containing the computational burden. The analysis shows the feasibility of the robot collision checker based on a deep learning approach. In fact, such approach presents a low collision detection time, of the order of milliseconds on the selected hardware, with acceptable accuracy. Furthermore, the computational burden is compared with state-of-the-art geometric techniques. The entire work is based on an industrial case study involving a KUKA Agilus industrial robot manipulator at the Technology $\&$ Innovation Center of KUKA Deutschland GmbH, Germany. Further validation is performed with the Amazon Robotic Manipulation Benchmark (ARMBench) dataset as well, in order to corroborate the reported findings.

The increasing number of applications for spatial audio technologies has led to a growing interest in the subject from academic institutions and a more capillary diffusion of techniques and practices to non-institutional contexts, especially independent sound artists. However, the lack of a methodology for learning these technologies motivated our team to develop the Open Ambisonics Toolkit (OAT). Our goal is to promote the diffusion of spatial audio technologies by combining three pedagogical components: a DIY approach to hardware, a selection of open-source software, and a step-by-step introduction to Ambisonics theory through practical applications. The present article focuses on the development of a flexible toolkit and is based in our own practical experience as sound artists and teachers. We describe the process of designing hardware and selecting software components, and report results from objective measurements and listening tests conducted to evaluate different loudspeakers and spatial configurations. To conclude, we discuss future perspectives on the development of tutorials for learning spatial audio with OAT, which we are continually testing in workshop settings with students and independent sound artists.

Screw theory serves as an influential mathematical tool, significantly contributing to mechanical engineering, with particular relevance to mechanism science and robotics. The instantaneous screw and the finite displacement screw have been used to analyse the degree of freedom and perform kinematic analysis of linkage mechanisms with only lower pairs. However, they are not suitable for higher pair mechanisms, which can achieve complex motions with a more concise structure by reasonably designing contact contours, and they possess advantages in some particular areas. Therefore, to improve the adaptability of screw theory, this paper aims to analyse higher kinematic pair (HKP) mechanisms and proposes a method to extend instantaneous screw and finite displacement screw theory. This method can not only analyse the instantaneous degree of freedom of HKP mechanisms but also determine the relationships between the motion variables of HKP mechanisms. Furthermore, this method is applied to calculate the degree of freedom and the relationships between the motion angles in both planar and spatial cam mechanisms, thereby demonstrating its efficiency and advantages.

This paper focuses on the comparison of networks on the basis of statistical inference. For that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that is able to capture complex structural patterns. The graphon itself can be viewed more broadly as local density or intensity function on networks, making the model a natural choice for comparison purposes. More precisely, to gain information about the (dis-)similarity between networks, we extend graphon estimation towards modeling multiple networks simultaneously. In particular, fitting a single model implies aligning different networks with respect to the same graphon estimate. To do so, we employ an EM-type algorithm. Drawing on this network alignment consequently allows a comparison of the edge density at local level. Based on that, we construct a chi-squared-type test on equivalence of network structures. Simulation studies and real-world examples support the applicability of our network comparison strategy.

Structural health monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an “input–output” and an “output-only” method.