Indoor positioning systems (IPS) are essential for mobile robot navigation in environments where global positioning systems (GPS) are unavailable, such as hospitals, warehouses, and intelligent infrastructure. While current surveys may limit themselves to specific technologies or fail to provide practical application-specific details, this review summarizes IPS developments directed specifically towards mobile robotics. It examines and compares a breadth of approaches that vary across non-radio frequency, radio frequency, and hybrid sensor fusion systems, through the lens of performance metrics that include accuracy, delay, scalability, and cost. Distinctively, this work explores emerging innovations, including synthetic aperture radar (SAR), federated learning, and privacy-aware AI, which are reshaping the IPS landscape. The motivation stems from the’ increasing complexity and dynamic nature of indoor environments, where high-precision, real-time localization is essential for safety and efficiency. This literature review provides a new conceptual, cross-border pathway for research and implementation of IPS in mobile robotics, addressing both technical and application-related challenges in sectors related to healthcare, industry, and smart cities. The findings from the literature review allow early career researchers, industry knowledge workers, and stakeholders to provide secure societal, human, and economic integration of IPS with AI and IoT in safe expansions and scale-ups.

This study presents an innovative system for upper limb rehabilitation, combining a variable stiffness device, the ReHArm prototype, with a dynamic and engaging user interface, known as Arms Rehabilitation Management System. The proposed system offers a highly customisable approach to rehabilitation, ensuring real-time adaptability to patients’ specific needs while maintaining compactness and ease of use. Key features include a modular design allowing precise stiffness adjustments, a robust control architecture, and interactive rehabilitation phases designed to enhance user engagement. Extensive multidisciplinary analyses, including kinematic, dynamic, and structural evaluations, demonstrate the system’s ability to improve therapy effectiveness through tailored interaction and feedback. Validation tests demonstrated the prototype’s reliability and robustness, and initial usability assessments suggest its potential to improve rehabilitation outcomes. Further clinical studies involving patients will be necessary to fully evaluate its therapeutic effectiveness.

Extensible continuum robots (ECRs) offer distinct advantages over conventional continuum robots due to their ability to enhance workspace adaptability through length adjustments. This makes ECRs particularly promising for applications that require variable lengths involving the manipulation of objects in challenging environments, such as risky, cluttered, or confined. The development of ECRs necessitates careful consideration of mechanical structures, actuation methods, methods of stiffness variability, and control methods. The selection of papers is based on their relevance to ECRs within the period of 2010 to 2023 in the databases of Scopus and Web of Science. Distinguishing itself from other review papers, this paper aims to deliver a comprehensive and critical discussion about the advantages and disadvantages of ECRs concerning their mechanical structures, actuation methods, stiffness variability, and control methods. It is a beneficial resource for researchers and engineers interested in ECRs, providing essential insights to guide future developments in this field. Based on the literature, existing ECRs exhibit an inherent trade-off between flexibility and structural strength due to the absence of systematic design methods. Additionally, there is a lack of intelligent and effective controllers for achieving complex control performance and autonomous stiffness variability.

The inverse dynamics model of an industrial robot can predict and control the robot’s motion and torque output, improving its motion accuracy, efficiency, and adaptability. However, the existing inverse rigid body dynamics models still have some unmodelled residuals, and their calculation results differ significantly from the actual industrial robot conditions. The bootstrap aggregating (bagging) algorithm is combined with a long short-term memory network, the linear layer is introduced as the network optimization layer, and a compensation method of hybrid inverse dynamics model for robots based on the BLL residual prediction algorithm is proposed to meet the above needs. The BLL residual prediction algorithm framework is presented. Based on the rigid body inverse dynamics of the Newton–Euler method, the BLL residual prediction network is used to perform error compensation on the inverse dynamics model of the Franka robot. The experimental results show that the hybrid inverse dynamics model based on the BLL residual prediction algorithm can reduce the average residuals of the robot joint torque from 0.5651 N·m to 0.1096 N·m, which improves the accuracy of the inverse dynamics model compared with those of the rigid body inverse dynamics model. This study lays the foundation for performing more accurate operation tasks using industrial robots.

The problem of how to effectively track and intercept small aircraft that break into the no-fly zones is now attracting increasing interest in robotics society. Vision-based control has been proved an effective solution to the target tracking problem for unmanned aerial vehicles (UAVs). Due to the limited field of view (FOV) of onboard vision sensors, existing works assume that the target is always detectable during tracking or limit the flight speed of the UAV in practice. In this paper, inspired by the broad FOV of camera network, we are the first to propose an eye-to-hand (i.e., fixed cameras) visual servoing scheme to track and intercept aerial targets by using UAVs and ground visual sensors. Specifically, utilizing rotation matrices, we first present a visual servoing equation to convert the UAV motion in image planes to the inertial frame. Then, an image-based visual servoing controller is designed directly based on image errors of camera nodes in the sensor network, and system stability is proved by means of Lyapunov analysis. Additionally, to achieve the desired translational velocity command, a low-level attitude controller is developed based on the UAV dynamics. Finally, a series of experiments in both simulated and real flight scenarios show the outstanding efficacy of our method.

With advancements in industrial robot technology and the ongoing enhancements in control system performance, the demand for precise robot motion is increasing. Generally, an increased number of interpolation points enhances the precision of robot movement, but excessive points can lead to jittering and out-of-step issues. This paper investigates the relationship between the number of motion interpolation points and the response times of the control system and the robot’s terminal velocity, based on the theoretical calculation and experimental analysis of the limit interpolation points for the control system of a self-developed 6-DOF (Six Degree of Freedom) robot. The method for calculating limit interpolation points is refined using the least squares method, and equations are derived for different control system response time and robot’s terminal velocity reaction times. The validity of the prediction curves is verified through experimental analysis.

A more intuitive appreciation of spatial compliant behavior can be obtained through analysis and description of the behavior in terms of its centers, specifically the center of stiffness, the center of compliance, and the center of elasticity. This paper investigates the properties of each of these centers. Necessary and sufficient conditions for the coincidence of these centers are identified. A physical appreciation of those compliant behaviors that have coincident centers is obtained in terms of restrictions on the geometry of topologically simple mechanisms that realize those behaviors. The results can be used in the design of compliant mechanisms for robotic manipulation, especially when the compliance is characterized by the location of its center.

This paper proposes a seat support mechanism to solve the problem of misalignment between the chest examination and probe scanning areas when the patient is bent in a seated-style echocardiography robot. To guide the patient to an appropriate body position where their chest is within the examination range of the chest examination unit while minimizing the physical load of the patient, the posture of the patient must satisfy the three following conditions. (i) The breech must be in contact with the seat surface, (ii) the legs must be vertical to the floor, and (iii) the chest and mechanism must be parallel while the probe scanning and chest examination ranges must match. The human body was modeled to derive a posture that satisfies the aforementioned conditions for the height of each individual, and a seat support mechanism with four degrees of freedom was installed to guide the user to the derived posture. By installing this mechanism, the body load of the left biceps brachii, right biceps brachii, left latissimus dorsi, and right latissimus dorsi was reduced to 64.7%, 52.7%, 86.4%, and 80.2%, respectively. The sharpness of the image contours was improved to 103.8%.

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.

Interfacial interactions, including adhesion and friction, directly affect the ability of the robot system to interact with the external environment, such as the realization of operation and motion functions. Bionics provides guidance for the active control of interface forces. Creatures such as geckos, tree frogs, octopuses, and beetles have developed delicate topological structures and smart control strategies during long-term evolution, facilitating their ability to adhere to, manipulate, capture, and traverse various surfaces across diverse environments. Inspired by the advantages of high strength, adaptability, controllability, durability, and no residue, biomimetic controllable adhesion structures, materials, and systems have been developed, showing a wide range of potential applications in reversible attachment, flexible locomotion, and dexterous grasping. In this paper, the mechanisms and theoretical models of various biological reversible adhesion systems in nature are summarized. Then the design criteria, optimization method, and preparation technology of the artificial adhesion structures based on van der Waals interaction, capillary force, negative pressure, and mechanical interlocking mechanisms are reviewed. In particular, the adhesion/load ratio and the switch ratio of adhesive materials and structures are highlighted to evaluate the adhesion ability and controllability of various designs. The applications of biomimetic controllable adhesion structures and systems in robotics manipulation and locomotion are presented. Finally, the conclusion and possible future direction are discussed.

An improved identification algorithm is adopted to calibrate the kinematic parameters of the serial-parallel robot, which improves the motion accuracy of the end-effector. Firstly, a kinematic model of the serial-parallel robot is constructed based on the closed-loop vector method. Secondly, a kinematic error model is established by combining geometric error analysis with the vector differential method. Then, with the effective separation of compensable and non-compensable error sources, an identification model of kinematic parameters is constructed. Finally, an improved pivot element weighted iterative algorithm is used to identify the geometric error parameters. Through actual pose measurement, MATLAB is used to simulate the calibration process. The simulation and experimental results show that after kinematic calibration, compared with the traditional least squares method, the improved identification algorithm can significantly reduce the end-effector pose error of the serial-parallel robot, thus effectively improving the motion accuracy of the end-effector.

Global path planning using roadmap (RM) path-planning methods including Voronoi diagram (VD), rapidly exploring random trees (RRT), and probabilistic roadmap (PRM) has gained popularity over the years in robotics. These global path-planning methods are usually combined with other path-planning techniques to achieve collision-free robot control to a specified destination. However, it is unclear which of these methods is the best choice to compute the efficient path in terms of path length, computation time, path safety, and consistency of path computation. This article reviewed and adopted a comparative research methodology to perform a comparative analysis to determine the efficiency of these methods in terms of path optimality, safety, consistency, and computation time. A hundred maps of different complexities with obstacle occupancy rates ranging from 50.95% to 78.42% were used to evaluate the performance of the RM path-planning methods. Each method demonstrated unique strengths and limitations. The study provides critical insights into their relative performance, highlighting application-specific recommendations for selecting the most suitable RM method. These findings contribute to advancing robot path-planning techniques by offering a detailed evaluation of widely adopted methods.

Soft robots have emerged as a transformative technology with widespread applications across diverse fields. Among various actuation mechanisms, fluid-based actuation remains predominant in soft robotics, where precise fluid regulation is fundamental to system performance. This review aims to provide a comprehensive reference for researchers interested in fluid regulation strategies in soft robots by outlining the current state of research in this field and discussing innovations in valve designs to inspire future advancements. The fluid regulation strategies discussed in this review are systematically categorized into three main approaches: valve-based, smart fluid-based, and pressure source-based strategies, with each type systematically classified and discussed in detail. Building upon this analysis, a Task-to-Fluidic Regulation System mapping framework is proposed, integrating the V-model principles from systems engineering to provide a structured, requirements-driven methodology that links task objectives to concrete regulation system configurations through sequential design and multi-level verification. Finally, the latest advancements in fluid regulation methods in soft robotics are summarized, along with emerging trends and directions for future development.

Robotic lower limb exoskeletons are wearable devices designed to augment human motor functions and enhance physical capabilities mostly adopted in healthcare and rehabilitation. The field is strongly dominated by rigid exoskeletons driven by electromagnetic actuators constituted by electrical motors, gearboxes, and cylinders. This review focuses on the design and specifications of the actuation systems of lower limb exoskeletons, with the ultimate goal of providing reporting guidelines to allow for full reproducibility. For each paper, we assessed the quality and completeness of technical characteristics with two ad hoc rating scales for motors and reducers; we extracted the main parameters of the actuation unit and a quantitative analysis of the mechanical characteristics of the individual components was carried out considering the exoskeleton application. Overall, we observed a lack of details in reporting on actuation systems equipped on exoskeletons. To overcome this limitation, herein we conclude by proposing a data form and a checklist to provide researchers with a common approach in reporting the mechanical characteristics of the actuation unit of their lower limb exoskeletons. We believe that the convergence of exoskeletons’ literature toward a clearer standardization of design and reporting will boost the development of this technology and its diffusion outside the laboratory.