Search Results (642)

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle dynamics and pelvic translation. To address these limitations, this paper establishes a sagittal-plane dynamic model comprising nine generalised coordinates, treating the human lower limb and exoskeleton as an integrated coupled system. A seven-segment kinematic model encompassing the trunk, bilateral thighs, shanks, and feet is constructed via a modified Denavit–Hartenberg parameter method, and dynamic equations are derived using Lagrangian formulation. Three control strategies—PD control, PD with gravity compensation, and the computed torque method—are designed and evaluated through simulations using gait data from five subjects (two self-collected, three from a public dataset) acquired via Vicon motion capture. Results demonstrate that the computed torque method achieves a joint angle tracking root mean square error (RMSE) of 0.59°, representing an 86.3% improvement over conventional PD control, while maintaining a low control torque RMS of 4.44 N·m. The controller exhibits stable tracking performance across walking speeds of 0.4–1.45 m/s, validating the effectiveness of the proposed model and control strategies. Full article

Gait analysis study comparing unicompartmental vs. total knee arthroplasty, differences in knee kinematics: a retrospective cohort study. Background and Objectives: Total knee arthroplasty (TKA) is an effective treatment for advanced knee osteoarthritis, although functional outcomes may remain suboptimal in many patients. Unicompartmental knee arthroplasty (UKA) often provides better functional recovery but shows lower long-term implant survival. Recently, personalized TKA approaches have been developed to improve kinematic restoration and patient satisfaction. This study aimed to compare knee kinematics among patients who underwent personalized TKA, medial UKA, and healthy controls. Materials and Methods: This retrospective cohort study included 9 patients treated with robotic-assisted personalized TKA, 9 patients treated with medial UKA, and 9 healthy controls matched for age, sex, and BMI. Inclusion criteria were age 60–80 years, Kellgren–Lawrence grade III–IV, a minimum follow-up of 12 months, deviation from neutral HKA < 15°, healthy contralateral knee, and high postoperative functional scores. Exclusion criteria included valgus knees (HKA > 180°), postoperative complications, and neuromotor disorders. In the TKA group, a Medial Congruent implant was implanted with ROSA robotic assistance using a restricted kinematic alignment (±5° HKA) and asymmetric intercompartmental balancing. In the UKA group, a fixed-bearing medial implant (Physica ZUK) was used. Gait analysis was performed on a markerless instrumented treadmill (WalkerView™; Dalmine, Italy). Differences between groups were analyzed using one-way ANOVA and Tukey’s post-hoc test (p < 0.05). Results: UKA patients walked with a stiffer knee during stance. Knee range of motion during stance increased from UKA (6.3° ± 7.2°) to TKA (13.6° ± 8.8°, p = 0.045) and to controls (16.6° ± 4.5°, p = 0.02). During loading response, UKA patients showed lower flexion (10.2° ± 6.1°) than TKA (19.4° ± 7.9°, p = 0.049) and controls (19.6° ± 2.8°, p = 0.004). Knee flexion during swing was comparable between UKA and TKA. Conclusions: UKA patients demonstrated reduced knee flexion during early stance compared with robotic-assisted TKA and healthy controls. The observed differences may reflect multiple factors, including surgical technique, implant design, and patient-related characteristics. Because preoperative functional data were not available, potential selection bias cannot be excluded. These findings should be interpreted cautiously and warrant confirmation in larger prospective studies. Full article

In recent years, we have seen a constant increase in the capabilities of walking robots, leading to early cases of their practical use, and a much broader application is expected in the near future. However, creating a robust control design (in the presence of disturbances and model uncertainties) for walking robots still remains a challenge. One challenging source of uncertainty is the combination of the contact constraints and the lack of full state information, which can potentially lead to an offset (a steady-state error) in the robot’s position, interfering with tasks requiring high accuracy and deteriorating the overall performance of the robot. This is further exacerbated by the presence of multiplicative model uncertainties, common to mobile robots. In this work, we introduce an $H_{\infty }$ control formulation designed to attenuate this type of disturbance. The proposed method can handle norm-bounded multiplicative uncertainties in the state, control, and disturbance matrices using a full-state static feedback control. The resulting control design procedure is a single semidefinite program which provides a large computational advantage over the alternative dynamic feedback controller methods. We demonstrate the effectiveness of the method in comparison with the alternative formulations in simulation. We demonstrate that the method can be effectively tuned using a regularization term in the cost function. We show that the upper bounds on the $H_{\infty }$ gain of the closed-loop system can be effectively tightened post control design. Full article

This paper presents the mechanical design and experimental evaluation of the biped unit of LARMbot V.3—a compact low-cost humanoid robot for educational and research purposes. The biped unit features a modular architecture with a parallel leg mechanism for bipedal locomotion. The mechanical configuration of the unit is introduced, highlighting improvements on previous versions in terms of compactness and operating efficiency. A functional prototype is developed and described with detailed specifications of its actuation and transmission systems. To evaluate the performance of the proposed design, experimental tests were conducted both in-air and on-ground, demonstrating the robot’s ability to perform repeatable walking cycles. The results confirm the feasibility of the design and its potential as a platform for further developments in humanoid locomotion. Full article

With the increasing demands for autonomy and coverage efficiency in tasks such as security patrol and post-disaster exploration using mobile robots, achieving random, efficient, and complete coverage path planning has become a critical challenge. Traditional chaotic path planning methods, while capable of generating unpredictable trajectories, still have limitations in terms of randomness strength, traversal uniformity, and convergence coverage. To address this, this study proposes a complete-coverage random path planning method based on a novel four-dimensional fractal-fractional multi-scroll chaotic system. The main contributions of this research are as follows: First, by introducing additional state variables and fractal-fractional operators into the classical Chen system, a fractal-fractional chaotic system with a multi-scroll attractor structure is constructed. The output of this system is then mapped into robot angular velocity commands to achieve area coverage in unknown environments. Key findings include: the novel chaotic system possesses two positive Lyapunov exponents; Spectral Entropy (SE) and Complexity (CO) analyses indicate that when parameter B is fixed and the fractional order α increases, the dynamic complexity of the system significantly rises; in a 50 × 50 grid environment, the robot driven by this system achieved a coverage rate of 98.88% within 10,000 iterations, outperforming methods based on Lorenz, Chua systems, and random walks; ablation experiments further demonstrate that the combined effects of the fractal order β, fractional order α, and multi-scroll nonlinear terms are key to enhancing system complexity and coverage performance. The significance of this study lies in that it not only provides new ideas for constructing complex chaotic systems but also offers a reliable theoretical foundation and practical solution for mobile robots to perform efficient, random, and high-coverage autonomous inspection tasks in unknown regions. Full article

Background and Objectives: Post-traumatic lower limb injuries are frequently associated with gait impairment, reduced functional independence, and delayed recovery due to weight-bearing restrictions. Dynamic body weight support (DBWS) refers to rehabilitation technologies that provide real-time, adaptive unloading of body weight during functional tasks such as walking, enabling safer and more effective gait training. Although these robotic systems have been extensively investigated in neurological pathologies, there is a lack of evidence regarding their use in post-traumatic lower limb injuries. Therefore, this systematic review aimed to evaluate the clinical effectiveness of robotic systems incorporating DBWS in the rehabilitation of post-traumatic lower limb pathologies. Materials and Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the protocol was registered in PROSPERO (CRD420261294273). Seven major databases (PubMed, Scopus, ScienceDirect, Cochrane, Web of Science, Springer, and Wiley) were searched from inception to October 2025. Studies that were considered included patients with recent post-traumatic pathologies in the lower limbs. The methodological quality and risk of bias of the included studies were evaluated using the PEDro scale and the RoB 2 tool. Results: Seven studies involving 265 participants with recent post-traumatic lower limb injuries were included. The rehabilitation systems reviewed enabled early, intensive gait and balance training by reducing lower limb loading and facilitating safe performance of functional walking tasks. However, substantial heterogeneity in intervention protocols and outcome measures limited direct comparisons across studies. Conclusions: The findings of this systematic review suggest that DBWS interventions may enhance gait and balance recovery in individuals with post-traumatic lower limb injuries. Despite the small number of participants included, the available evidence indicates that these technologies can facilitate functional improvements during the early stages of rehabilitation and may represent a valuable adjunct to conventional therapeutic approaches. Nevertheless, further well-designed studies with larger sample sizes, standardized intervention protocols, and long-term follow-up are required to establish optimal clinical implementation strategies and to confirm the durability of treatment effects. Full article

While walking robots possess significantpotential for various real-world applications, the reliance on high-performance sensors and complex control architectures for precise gait control remains a significant barrier to commercialization and lightweight design. To overcome these engineering limitations and lay the groundwork for a sensing paradigm adaptable to complex terrains, this study proposes an AI-based sensorless feedback control framework that incorporates the biological principles of proprioception. To this end, a walking robot leveraging the morphological intelligence of the Klann linkage was developed. We constructed a time-series dataset by defining motor current signals as ‘interoceptive sensing’ information—analogous to biological muscle feedback—and synchronizing them with absolute angular data. This dataset was used to train an Attention-LSTM (A-LSTM) model, which predicts future motor states in real-time by decoding nonlinear physical information embedded within internal current data, independent of external environmental sensors. By integrating the proposed model into a PI controller, a stable biomimetic walking loop was successfully implemented without the need for additional position sensors. Full article

Biarticular actuators can enhance efficiency and stability in legged locomotion by transferring energy between joints. Their effectiveness depends strongly on the lever arm ratio—the ratio of the actuator’s moment arm at one joint to its moment arm at another—which governs how torque is distributed across joints during movement. Inspired by biomechanics, early robotic studies implemented biarticular actuators to improve energy efficiency, joint coordination, and positional control, primarily in planar or single-joint systems, leaving a gap in fully 3D robotic legs. Here, we present a geometry optimization framework for a robotic leg incorporating both biarticular and monoarticular actuators. Using human motion capture and joint torque data, we optimized the linkage mechanisms so that the system can maintain the required joint torques while keeping biarticular actuator moment arm ratios near their optimal values during walking and running. The optimized leg achieved a minimum achievable cost of transport of approximately $0.41 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for walking and $0.62 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for running. Full article

In recent years, many countries have increased their investment in the field of humanoid robots, promoting significant technological development. This study aims to enable humanoid robots to better adapt to various complex environments, enhancing the robustness of their motion systems and the generalization ability of their motion strategies. Using reinforcement learning algorithms, training on varied terrain is a critical factor for developing adaptable humanoid robots. This paper takes the humanoid robot G1 as the research platform. First, it completes the training, transfer verification, and real-machine deployment of a flat-ground walking model. Then, using fuzzy logic control and a phased training strategy, walking models for ascending/descending stairs and traversing slopes are trained. By systematically varying the stair height and slope gradient, the convergence of the reward function and the task completion success rate are analyzed. Furthermore, the dynamic stability of the robot on complex terrains is validated through qualitative kinematic analysis. The research concludes that as the single-step height and slope gradient increase, the reward value initially rises with more iterations but converges more slowly and at a lower final value. Statistical analysis shows that the success rates of phased training for stair and slope terrains are higher than 86% and 92%, respectively. Full article

Background and Objectives: This study was conducted to investigate the effects of robot-assisted gait rehabilitation approaches using commonly used end-effector, fixed exoskeleton, and wearable exoskeleton on gait and balance abilities in patients with early post-stroke (≤3 months). Materials and Methods: Sixty patients admitted to a rehabilitation center with confirmed stroke by a medicine specialist were assigned to three groups such as the end-effector group (EG 1), the fixed exoskeleton group (EG 2), and the wearable exoskeleton group (EG 3). The primary endpoint was pre-specified as the change in timed up-and-go gait test (TUG) from baseline to week 6, and all other outcomes were treated as secondary. The functional gait category (FAC), 10-m walk test (10MWT), six-minute walk test (6MWT), timed up-and-go gait test (TUG), dynamic gait index (DGI), and Berg Balance Scale (BBS) were measured at four time points (baseline, 2 weeks, 4 weeks, and 6 weeks). Results: A significant main effect of time was observed for all outcome variables, but neither the main effect of group nor the interaction between group and time was significant for any outcome variable. Within-group analyses revealed that FAC, 6MWT, DGI, and BBS increased over time in all groups, whereas 10MWT and TUG decreased. Conclusions: All three robot-assisted gait rehabilitation approaches in patients with early post-stroke were associated with significant improvements in gait and balance abilities over 6 weeks. However, statistically significant differential trajectories were not detected across robot types in this sample. Full article

This paper proposes a learning-based contact force controller using deep neural networks (DNN) and a PI controller. Stable contact force control between the foot and the ground is essential for humanoid robots to maintain balance during bipedal walking. While admittance controllers have been extensively employed for contact force control in humanoid robots, their performance is limited by the high nonlinearity inherent in robot systems. To overcome these limitations, we propose a deep neural network (DNN)–based inverse model, which leverages input–output data that inherently capture system nonlinearities. The proposed learning-based contact force controller computes the target foot height based on the target force, measured force, and measured foot height, without relying on a dynamic model of the articulated robotic leg. Furthermore, a PI controller is integrated to mitigate steady-state errors. Experimental comparisons between the proposed controller and an admittance controller were conducted using an articulated robotic leg. Compared with an admittance controller, the proposed method reduced overshoot by 96% and settling time by 61% on average in step responses and decreased force-tracking RMSE by 66.3% on average across both step and sinusoidal experiments. Full article

Background: Cerebral palsy (CP) is the most common cause of disability in developmental age, affecting motor and postural skills. With growth, lower-limb orthopedic surgery often becomes necessary. Post-surgical walking rehabilitation programs generally involve conventional therapy with only limited evidence on the use of robot-assisted gait training (RAGT). The aim of the present pilot study is to assess the feasibility and the preliminary functional outcomes of an intensive 3-week rehabilitation of 15 sessions with Lokomat combined with 15 sessions of conventional physiotherapy. Methods: In total, 27 patients with diplegic cerebral palsy who underwent orthopedic surgery were recruited. Outcomes collected: the 6 min walking test (primary outcome), the Gross Motor Function Measure-88, the Gillette Functional Assessment Questionnaire, 3D gait analysis, and spasticity and force metrics of the lower limbs. Paired statistical tests were used to assess pre–post changes. Results: A pre–post statistically significant improvement was observed in gait endurance in the 6MWT (Δ = 28.56 ± 34.28 m; p < 0.001) and in gross motor functional skills. Gait parameters showed some functional and structural improvements, and joint stiffness was reduced in some measures. Conclusions: This combined rehabilitative approach seems to be promising in postoperative patients with CP. Future studies, involving a control group and larger sample size, are needed to generalize our results. Full article

Walking is one of many basic human motor functions, yet replicating it in robotic systems remains a complex problem. Historically, the design of walking mechanisms has relied on human intuition and iterative refining. Some well-known mechanisms, like Theo Jansen, have been invented by artists rather than engineers. In this paper, we present a novel, automated pipeline that includes dataset generation, filtering, and an optimization procedure for synthesizing 1-DOF geometrically feasible walking mechanisms. Four million mechanisms were simulated and evaluated for 25 mechanism types, for a total of 100 million mechanisms. Quantitative design criteria for walking motion were identified and applied to retain a total of 23,250 valid, stable walking mechanisms. We then apply a custom optimization process to adjust near-walking mechanisms whose joints run into the ground. A custom function is used to minimize the error related to ground intersection and step uniformity. The computational generation and optimization of walking linkages demonstrated in this work aims to systematically generate a large number of design concepts for walking mechanisms. While the focus of this work is on the synthesis of mechanisms for walking robots, the same methodology could be generalized to identify mechanisms for a wide range of applications beyond walking robots. Full article

Ensuring user safety while enabling independent mobility is crucial to autonomous healthcare and rehabilitation robots, such as active lower-limb orthoses and exoskeletons. A key requirement for these devices is to provide full assistance without supervision; however, existing designs do not simultaneously satisfy autonomous operation and inherent safety. To address this gap, a novel safety principle, Safety by Design, and a corresponding system architecture for a fully assistive active knee orthosis are introduced. The proposed architecture is based on a comprehensive risk analysis for the use of active orthoses and exoskeletons and integrates redundancies for all safety-critical components while minimizing additional weight. This redundancy enables the orthosis to remain operational at reduced power in the event of component failure, improving both user safety and system reliability. The design supports safe, unsupervised operation by ambulatory users, enhancing independent patient mobility and the performance of the gait activities of level walking, stair climbing and sitting down/standing up. The proposed architecture is scalable and adaptable to a wide range of robotic devices. By improving robustness, efficiency, and safety, this work contributes to the advancement of autonomous biomedical robotic systems and wearable assistive devices. Full article

Hexapod walking robots are a subject of intense research in the existing literature. To move effectively in natural terrain, these robots must be able to adapt to surface irregularities. While most existing designs employ sophisticated technical solutions for the leg mechanisms, none of these projects allow for combined roll and pitch movements of the body segments. This paper addresses this gap, presenting the concept of a hexapod robot with a body formed of three segments connected by two active universal joints. This unique architecture allows the robot to locomote on both sides and autonomously recover from a rollover event. The robot’s legs are underactuated, utilizing a passive spring element to simplify the mechanical design and control system while maintaining effective terrain adaptation capabilities. Experimental results are presented and discussed, validating the theoretical model and demonstrating the effectiveness of the proposed solution on varied terrains. Full article