Search Results (262)

Planetary exploration has increasingly relied on mobile robots known as rovers to support space development. Among various locomotion systems, legged mechanisms have attracted attention as a promising approach for achieving high mobility on rough terrain. However, the surfaces of extraterrestrial bodies such as the Moon and Mars are covered with loose regolith that easily deforms under external forces. As a result, legged rovers tend to disturb the ground surface and experience slippage due to leg-induced loading. To address this issue, a previous study proposed a novel walking method in which the rover’s leg applies vibration to the soil before stepping to compact it. Experiments confirmed that this vibration increases the soil’s bearing capacity, defined as its resistance to vertical loading. This increase is attributed to improvements in soil density and particle interconnectivity, which enhance soil shear strength. In this study, the relationship between the bearing capacity of vibration-compacted soil and its shear strength is investigated through experiments. The results reveal a clear correlation between these parameters, indicating that the bearing capacity of vibration-compacted soil can be estimated from shear strength measurements. Full article

Background/Objectives: Locomotive syndrome (LS) is associated with impaired balance and functional decline in older adults. Although locomotive training (LT) improves mobility, whether central neuromodulation enhances short-term balance adaptation remains unclear. This pilot randomized controlled trial examined the additive effect of anodal transcranial direct current stimulation (tDCS) with LT on balance. Methods: Sixteen community-dwelling adults aged ≥ 65 years with LS were randomized (1:1:1) to anodal tDCS + LT (n = 6), sham tDCS + LT (LT group, n = 6), or anodal tDCS alone (n = 4). Participants underwent five consecutive days of intervention. The primary outcome was eyes-open single-leg stance time, assessed before stimulation and at 10, 20, 50, and 80 min during and after stimulation on days 1–5. Group × time interactions were evaluated using linear mixed-effects models adjusted for baseline and age. Long-term outcomes were assessed on days 1, 5, and 12. Results: In the primary analysis, a significant group × time interaction for right-sided single-leg stance time was observed between the anodal tDCS + LT and the LT groups (F(1,58) = 6.08, p = 0.017; β = 0.966), indicating greater within-day improvement with combined therapy, but not in sensitivity analyses treating time as a categorical variable. No significant interactions were observed on the left side. Secondary outcomes showed time-dependent improvements without consistent group-specific effects or significant group × day interactions over the long term. No serious adverse events occurred. Conclusions: Anodal tDCS with LT improved short-term balance in the primary analysis; however, these effects were model-sensitive and not sustained over 12 days. These findings should be considered preliminary and hypothesis-generating. Larger trials are needed to determine optimal stimulation dosing and long-term efficacy. Full article

As a typical biomimetic robotic system, quadruped robots replicate the flexible locomotion of quadruped mammals, outperforming wheeled robots in human-centered daily scenarios. To improve the social navigation adaptability of biomimetic quadruped robots in human–robot shared environments, this paper proposes a collision-aware orthogonal steering social force model (COSFM), an enhanced social force model that integrates collision prediction and social norms, inspired by human-like collision avoidance behaviors and social interaction rules. The model addresses key limitations of conventional social force models: delayed responses to dynamic pedestrians and inadequate consideration of pedestrians’ comfort zones. It introduces a time-to-collision prediction mechanism to mimic human predictive decision-making in dynamic social interactions, enhancing the robot’s anticipation of pedestrian motion intentions, and designs an orthogonal steering-based avoidance strategy for four typical human–robot interaction scenarios (head-on encounters, intersecting paths, active overtaking, passive yielding). This strategy replicates humans’ natural priority of lateral steering over abrupt deceleration or retreat, generating socially compliant trajectories aligned with human behavioral expectations. The proposed method is validated via simulation and real-world experiments on a Unitree Aliengo quadruped robot. Results show that the COSFM algorithm achieves a higher navigation success rate and better performance in path length, navigation time, and minimum human-robot distance than existing approaches, while its human-like lateral avoidance priority effectively preserves pedestrians’ psychological comfort zones, demonstrating robust social adaptability and great application potential for biomimetic legged robots. Full article

This paper studies a MuJoCo-based locomotion framework that couples an adaptive-frequency central pattern generator (AFCO-CPG) with single rigid-body dynamics model predictive control (MPC) for the RENS Q1 quadruped with elastic parallel knee joints. AFCO-CPG combines multi-scale phase coordination, saturated phase correction, and load-gated feedback, while MPC supplies feasible ground-reaction forces and returns load cues to the timing layer. In MuJoCo, the controller achieves stable diagonal-trot speed tracking from 0.4 to 1.2 m/s and recovers from short external pushes. A matched elastic-versus-rigid timing sweep shows a favorable flat-ground parameter band around $\omega =1.8$ Hz, with a best-case cost-of-transport reduction of 12.83% for the elastic model under identical controller gains. A flat-to-slope ascent case further verifies that AFCO timing is modulated when load conditions change. Ablation across nine controller variants shows that multi-scale coordination is the dominant component, causing a 135% increase in phase error and a 536% increase in recovery time when removed. A reduced-order early/late-contact benchmark further confirms faster re-locking than diagonal-only and minimal variants. The results support the value of combining neural timing, predictive force optimization, and compliant-leg feedback in high-fidelity simulation, while hardware validation remains future work. 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

Background/Objectives: Foot impairments are common in older adults, but the independent associations of specific foot indices with locomotive syndrome (LS) severity remain unclear. We examined hallux valgus angle (HV), navicular height (NH), and passive ankle dorsiflexion (ADF). Methods: This cross-sectional study included 119 community-dwelling older adults classified into LS stages 0–3. Bilateral measures were summarized as maximum HV and minimum NH/ADF, reflecting the worst-affected side. Proportional-odds ordinal logistic regression modeled LS stage (0–3) with foot indices and covariates (age, sex, body mass index [BMI]). Extended models additionally adjusted for Timed Up and Go (TUG), gait speed, or single-leg stance (SLS). Sensitivity analysis used binary logistic regression (LS ≥ 2 vs. <2). Results: Greater ADF was independently associated with lower LS severity (OR per 1°, 0.91; 95% CI, 0.85–0.98; p < 0.01), whereas higher BMI was associated with greater LS severity (OR per 1 kg/m², 1.15; 95% CI, 1.01–1.30; p < 0.05). HV and NH were not significant. After adjustment for TUG, gait speed, or SLS, ADF remained inversely associated with LS severity (ORs, 0.92–0.93; p < 0.05), while the BMI association was attenuated. In binary logistic regression, greater ADF was associated with lower odds of LS ≥ 2 (OR per 1°, 0.85; 95% CI, 0.76–0.94; p < 0.005). Conclusions: Reduced passive ankle dorsiflexion is independently associated with greater LS severity, robust after accounting for key mobility and balance measures. Interventions targeting ankle mobility may represent a potentially modifiable factor and warrants confirmation in longitudinal and interventional studies. 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

Bipedal wheeled robots combine the advantages of wheeled mobility and legged agility, enabling high-speed locomotion and obstacle negotiation in complex environments. However, their dynamic behavior is inherently unstable and highly coupled, making robust control particularly challenging in the presence of task conflicts, external disturbances, and modeling uncertainties. This paper proposes an RBF–L1–WBC framework that integrates L1 adaptive control to compensate for model inaccuracies and disturbances, radial basis function (RBF) neural networks to approximate nonlinear variations in linear quadratic regulator (LQR) gains, and whole-body control (WBC) to coordinate multiple tasks while mitigating control conflicts. Experimental findings confirm that the proposed methodology yields statistically significant improvements in both attitude regulation precision and velocity tracking accuracy, surpassing the performance of benchmark controllers including classical LQR, adaptive LQR, and classical Virtual Model Control (VMC). Full article

Exploring the lunar complex and extreme terrain presents formidable challenges for conventional lunar rovers. To address these limitations, this study proposes a novel hexapod jumping hybrid robot that incorporates a “figure-of-eight” (butterfly-shaped) six-branched wheel-legged mechanism and a jumping system that stores elastic energy via deformation of its elastic body. Inspired by the multimodal locomotion of grasshoppers, the robot dynamically switches between two operational modes: high-efficiency wheeled locomotion on relatively flat surfaces and agile jumping to traverse steep slopes and surmount large obstacles. A bio-inspired gait, inspired by the crawling patterns of a hexapod insect, is implemented using a Central Pattern Generator (CPG)-based controller to produce coordinated, rhythmic limb movements. Dynamic simulations of the jumping mechanism were conducted to optimize the critical parameters of the elastic structure and its associated control strategy. Experiments on a physical prototype were conducted to validate the robot’s wheeled mobility and jumping performance. The results demonstrate that the robot exhibits excellent adaptability to rugged terrains and obstacle-dense environments. The integration of multimodal locomotion and adaptive gait control significantly enhances the robot’s operational robustness and survivability in the harsh lunar environment, opening new possibilities for future lunar exploration missions. 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

Legged robots inspired by animal locomotion require actuators with high power density, fast response, and robust force control, yet traditional valve-controlled hydraulic systems suffer from substantial energy losses and weak regeneration performance. Motivated by role allocation across gait phases in animal legs, where in-air positioning requires far less actuation effort than ground contact support and force modulation, this work proposes a novel gas–oil hybrid servo actuator, denoted GOhsa, for quadruped knee joints. GOhsa utilizes pre-charged high-pressure gas to pressurize hydraulic oil, converting the conventional dual-chamber pressure servo control into a single-chamber configuration while preserving the original piston stroke. This architecture enables bidirectional position–force control, enhances energy regeneration applicability, and improves operational efficiency. Theoretical modeling is conducted to analyze hydraulic stiffness and frequency-response characteristics, and a linearization-based force controller with dynamic compensation is developed to handle system nonlinearities. Experimental validation on a single-leg platform demonstrates significant energy-saving performance: under no-load conditions (simulating the swing phase), GOhsa achieves a maximum power reduction of 79.1%, with average reductions of 15.2% and 11.5% at inflation pressures of 3 MPa and 4 MPa, respectively. Under loaded conditions (simulating the stance phase), the maximum reduction reaches 28.0%, with average savings of 10.0% and 9.8%. Tracking accuracy is comparable to traditional actuators, with reduced maximum errors (13.7 mm/16.5 mm at 3 MPa; 15.0 mm/17.8 mm at 4 MPa) relative to the 16.6 mm and 18.1 mm errors of the conventional system, confirming improved motion stability under load. These results verify that GOhsa provides high control performance with markedly enhanced energy efficiency. Full article

Walking on compliant terrain presents a substantial challenge for individuals with lower-limb amputation, further elevating their already high risk of falling. While powered ankle–foot prostheses have demonstrated adaptability across speeds and rigid terrains, control strategies optimized for soft or compliant surfaces remain underexplored. This work experimentally validates an admittance-based control strategy that dynamically adjusts the quasi-stiffness of powered prostheses to enhance gait stability on compliant ground. Human subject experiments were conducted with three healthy individuals walking on two bilaterally compliant surfaces with ground stiffness values of 63 and $25{\displaystyle \frac{\mathrm{kN}}{\mathrm{m}}}$, representative of real-world soft environments. Controller performance was quantified using phase portraits and two walking stability metrics, offering a direct assessment of fall risk. Compared to a standard phase-variable controller developed for rigid terrain, the proposed admittance controller reduced short-term maximum Lyapunov exponents by an average of 7%, indicating improved local dynamic stability. These results support the potential of adaptive prostheses control to enhance gait stability on compliant surfaces, contributing to the development of more robust human–prosthesis interaction. Full article

Modular reconfigurable robots exhibit significant potential in adapting to complex terrains through cooperative multi-robot formations. However, current control systems often struggle to maintain consistent performance when the number of modules varies due to a lack of unified and adaptive control frameworks. Existing Virtual Model Control (VMC) methods, while effective for fixed-configuration legged robots, are limited in their ability to dynamically adjust control parameters in reconfigurable multi-legged systems. To address this gap, this study proposes a parallel multi-legged control system that integrates a Backpropagation Neural Network (BPNN) with a decoupled VMC framework. The BPNN enables adaptive tuning of motion parameters under varying modular configurations, while the decoupled VMC ensures stable gait control under force feedback. Simulation and physical experiments demonstrate that the proposed system achieves a unified control architecture across quadrupedal and multi-legged configurations, with improved tracking accuracy, stability, and adaptability compared to traditional VMC methods. Full article

Exoskeletons are increasingly used in industrial settings, yet most are designed for structured, repetitive tasks, limiting adaptability to dynamic movements. In construction, frequent locomotion tasks demand continuous lower-limb engagement, and ladder climbing places substantial loads on coordination and flexibility. This study aimed to identify key muscles involved in climbing to support the development of adaptive exoskeletons. Ten healthy male participants (33.8 ± 3.4 years; 178.7 ± 5.0 cm; 87.4 ± 16.1 kg) performed vertical and A-frame ladder ascents in a controlled laboratory setting. Surface electromyography was recorded from eight right-leg muscles and processed using band-pass filtering, rectification, and root mean square smoothing. Two normalization strategies were applied: walking normalization, expressing climbing activity relative to level walking, and maximum voluntary contraction normalization, with amplitudes expressed as a percentage of maximum voluntary contraction. Our results showed that all muscles were more active in climbing than walking, with quadriceps (vastus medialis, vastus lateralis, rectus femoris) exhibiting the greatest increases. Gastrocnemius also approached or exceeded 100%MVC, tibialis anterior averaged 70–80%MVC, and hamstrings contributed 20–40%MVC mainly for stabilization. Vertical and A-frame ladders followed similar patterns with subtle posture-related variations. These findings highlight knee extensors as primary targets for adaptive exoskeleton assistance during ladder climbing tasks commonly performed on construction sites. Full article