Search Results (200)

To enhance the flexibility and adaptability of underwater robots in complex environments, this paper designs an octopus-inspired soft underwater robot capable of both bipedal walking and multi-arm swimming. The robot features a rigid–flexible coupling structure consisting of a head module and eight rope-driven soft tentacles and integrates buoyancy adjustment and center-of-gravity balancing systems to achieve stable posture control in both motion modes. Based on the octopus’s bipedal walking and multi-arm swimming mechanisms, this study formulates gait generation strategies for each mode. In walking mode, the robot achieves underwater linear movement, turning, and in-place rotation through coordinated tentacle actuation; in swimming mode, flexible three-dimensional propulsion is realized via synchronous undulatory gaits. Experimental results demonstrate the robot’s peak thrust of 14.1 N, average swimming speed of 8.6 cm/s, and maximum speed of 15.1 cm/s, validating the effectiveness of the proposed structure and motion control strategies. This research platform offers a promising solution for adaptive movement and exploration in unstructured underwater environments. Full article

With the rapid development of mobile robotics, wheeled bipedal robots, which combine the terrain adaptability of legged robots with the high mobility of wheeled systems, have attracted increasing research attention. To address the balance control problem during both standing and locomotion while reducing the influence of noise on control performance, this paper proposes a balance control framework based on a Linear Quadratic Regulator integrated with an Extended Kalman Filter (KLQR). Specifically, a baseline LQR controller is designed using the robot’s dynamic model, where the control input is generated in the form of wheel-hub motor torques. To mitigate measurement noise and suppress oscillatory behavior, an Extended Kalman Filter is applied to smooth the LQR torque output, which is then used as the final control command. Filtering experiments demonstrate that, compared with median filtering and other baseline methods, the proposed EKF-based approach significantly reduces high-frequency torque fluctuations. In particular, the peak-to-peak torque variation is reduced by more than 60%, and large-amplitude torque spikes observed in the baseline LQR controller are effectively eliminated, resulting in continuous and smooth torque output. Static balance experiments show that the proposed KLQR algorithm reduces the pitch-angle oscillation amplitude from approximately ±0.03 rad to ±0.01 rad, corresponding to an oscillation reduction of about threefold. The estimated RMS value of the pitch angle is reduced from approximately 0.010 rad to 0.003 rad, indicating improved convergence and steady-state stability. Furthermore, experiments involving constant-speed straight-line locomotion and turning indicate that the KLQR algorithm maintains stable motion with velocity fluctuations limited to within ±0.05 m/s. The lateral displacement deviation during locomotion remains below 0.02 m, and no abrupt acceleration or deceleration is observed throughout the experiments. Overall, the results demonstrate that applying Extended Kalman filtering to smooth the control torque effectively improves the smoothness and stability of LQR-based balance control for wheeled bipedal robots. Full article

The presence of sensor noise, missing states and inadequate future prediction capabilities imposes significant limitations on the locomotion performance of bipedal robots operating in unstructured terrain. Conventional methods generally depend on long-term history observations to reconstruct single-frame privileged information. However, these methods fail to acknowledge the pivotal function of short-term history in rapid state responses and the significance of future state prediction in anticipating potential risks. The proposed framework is a Long–Short World Model (LSWM), which integrates state reconstruction and future state prediction to enhance the locomotion capabilities of bipedal robots in complex environments. The LSWM framework comprises two modules: a state reconstruction module (SRM) and a future state prediction module (SPM). The state reconstruction module employs long-term history observations to reconstruct privileged information in the current short-term history, thereby effectively improving the system’s robustness to sensor noise and enhancing state observability. The future state prediction module enhances the robot’s adaptability to complex environments and unpredictable scenarios by predicting the robot’s future short-term privileged information. We conducted extensive comparative experiments in simulation as well as in a variety of real-world indoor and outdoor environments. In the indoor stair-climbing task, LSWM achieved a 94% success rate, outperforming the current state-of-the-art baseline methods by at least 34%, thereby demonstrating its substantial performance advantages in complex and dynamic environments. Full article

In intensive breeding systems, goats reared for meat production are often housed in group pens at high stocking densities. This study aimed to investigate the correlation between expressed behaviours and stocking density, and to compare the time budget of these confined goats with that of pasture-based goats. A detailed ethogram of 19 mutually exclusive behavioural activities was developed. Behavioural observations were conducted continuously over 72 h on group pens selected for their variation in stocking density and homogeneity in breed, age, body condition and acclimation period since arrival. Using the scan-sampling method (96 scans per goat daily), data were collected from 42 goats. The time budget, expressed as the mean frequency (%) ± standard deviation for each behavioural activity, was calculated. The associations between time budget and stocking density were assessed via bivariate analysis, with the strength and direction of relationships quantified using Pearson’s correlation coefficient (r). Results indicated that self-grooming and Bipedal stance/Climbing were positively correlated with increased space allowance (i.e., lower stocking density), suggesting their potential utility as positive welfare indicators for housed fattening goats in group pens. Furthermore, the time budget differed notably from pasture-based patterns, primarily characterized by resting (53.09% ± 2.72%), eating (16.05% ± 2.88%), and moving (2.30% ± 0.75%). Full article

This research develops and implements a novel reinforcement learning (RL) architecture to address the trajectory-tracking problem in bipedal robotic systems under articulated-joint constraints. The proposed RL framework extends previously designed adaptive controllers characterized by state-dependent gain structures. The learning mechanism comprises two hierarchical adaptation layers: the first employs an adaptive dynamic programming (ADP) formulation to approximate the Bellman value function using a class of continuous-time dynamic neural networks. In contrast, the second uses an iterative optimization scheme based on the deep deterministic policy gradient (DDPG) algorithm. The resulting control strategy minimizes a robust performance index defined over the tracking trajectories of a system with uncertain and nonlinear dynamics representative of bipedal locomotion. The dynamic programming formulation ensures robustness to bounded parametric uncertainties and external perturbations. By approximating the Hamilton–Jacobi–Bellman (HJB) value function using neural network structures, a closed-loop controller design is systematically established. Numerical simulations demonstrate the convergence of the tracking error to a region centered at the origin with a size that depends on the approximation quality of the selected neural network. To assess the effectiveness of the proposed approach, a conventional state-feedback control design is adopted as a benchmark, revealing that the suggested method produces a lower cumulative tracking error norm (0.023 vs. 0.037 rad·s) in the trajectory-tracking control problem for all robotic joints while simultaneously reducing the control effort required to complete motion tasks. Full article

Humanoid bipedal walking remains challenging due to unstable, high-dimensional dynamics and the labor-intensive, platform-specific tuning typically required to obtain workable gaits. We present a hybrid framework that couples a compact screw-theory kinematic model with a multi-objective genetic algorithm (GA) to tune humanoid gait parameters automatically. The method parameterizes the foot’s half-elliptical swing (horizontal and vertical speeds) and the torso pitch angle, and optimizes stride length while limiting lateral deviation through a single, weighted objective. Relying only on kinematic models—without explicit dynamic equations—the framework integrates inverse kinematics and Jacobian computation to evaluate candidate solutions efficiently. We validate the approach in simulation and on a 14-degrees-of-freedom (DoF) humanoid platform. This work contributes a compact modeling and optimization strategy that enables sim-to-real transfer, establishing a foundation for future extensions incorporating stability criteria, sensor feedback, and adaptive weighting. Full article

Biped robots based on parallel mechanisms hold great potential for applications in complex terrains. Based on a 4-UPU parallel mechanism, this paper proposes a novel biped robot that achieves alternating bipedal locomotion and turning with only six actuators by employing fixed/moving platform switching and following an “upper platform + lower foot” continuous gait strategy. Using the influence coefficient method, the first order and second order kinematic influence coefficient matrices of the biped robot were derived. Based on the principle of virtual work, a dynamic model of the robot was formulated, and its validity was verified through numerical simulations. The dynamic performance of the robot was further evaluated using the Dynamic Manipulability Ellipsoid (DME) index, while its stability during step-climbing and turning was analyzed using the Zero-Moment Point (ZMP) method. The results demonstrate that the dual-platform biped robot features a rational structure and exhibits robust stability during step-climbing and turning. Full article

Background: Adolescence is characterized by rapid physical growth and neuromuscular reorganization, which may influence the development of postural control. Gender-specific differences in pubertal timing suggest that girls may achieve postural stability earlier than boys, but evidence remains inconsistent. This cross-sectional pilot study aimed to examine gender differences in static postural control among adolescents. Material and methods: A total of 59 students (28 females, 31 males; mean age 13.49 ± 0.97 years) from two schools in Bari, Italy, participated. Postural stability was assessed during bipedal and single-leg stance tasks under eyes-open and eyes-closed conditions using an inertial sensor placed at the lumbosacral region. The primary outcomes were sway path length and oscillation ellipse area. Results: Females demonstrated significantly shorter path length in eyes-open bipedal stance (p = 0.027, d = −0.51), as well as reduced ellipse area (p = 0.047, d = −0.44) and path length (p = 0.010, d = −0.62) in eyes-closed bipedal stance. No significant gender differences were observed in single-leg stance. Conclusions: These findings support the hypothesis that adolescent girls exhibit superior postural stability compared to boys, particularly under challenging sensory conditions. Such differences may reflect earlier maturational processes and suggest possible implications for motor development, injury prevention, and sports training. Full article

Incomplete spinal cord injury (iSCI) results in impaired postural control and walking ability. Visual over-reliance may occur in iSCI individuals to maintain postural control. This can challenge their postural stability in various contexts of daily life activities. The present study assessed the feasibility, acceptability, and preliminary outcomes of balance training with intermittent visual deprivation using stroboscopic glasses on postural control and visual reliance during quiet standing in iSCI individuals. Training impact on walking performance was also evaluated. Seven chronic iSCI individuals participated in a 6-week home-based balance training program, three times weekly, using stroboscopic glasses. Postural and walking abilities were assessed pre- and post-training using a bipedal stance test (BST) and 10 m walking test (10 MWT). BST was performed, with open-eyes (OE) and closed-eyes (CE), on a force plate for three 30 s trials. The center of pressure (CoP) variables included were CoP area (A-CoP) and CoP mean velocity (MV-CoP). Romberg ratios (CE/OE) for two CoP variables were calculated. Duration and speed were measured in 10 MWT. Intervention feasibility was assessed using the feasibility and acceptability questionnaire. Data from able-bodied individuals were recorded and used as references of physiological performance. iSCI individuals were significantly less stable and showed visual over-reliance for postural steadiness compared to controls. Also, their walking ability was impaired. All iSCI individuals completed the training (adherence rate: 84%) and rated it highly feasible. A-CoP and MV-CoP significantly reduced after training in CE condition (p = 0.018, respectively) but not in OE condition (p > 0.05). The Romberg ratio of A-CoP was significantly lower (p = 0.018), but the Romberg ratio of MV-CoP was not (p > 0.05). A significant reduction in duration and increase in speed (p = 0.018, respectively) in performing the 10 MWT were observed. Preliminary findings from this explorative study indicated that 6-week home-based balance training with intermittent visual deprivation was feasible, acceptable, and had promising potential benefits in improving postural control with a reduction in visual over-reliance in iSCI individuals. The training enhanced also their walking performance. Full article

In disturbance scenarios, wheeled bipedal robots (WBRs) require effective control algorithms to restore balance. To address the trade-off between computational burden and control precision, and to enhance anti-disturbance capability, this paper proposes a soft-constrained Model Predictive Control (MPC) algorithm with optimized horizon parameters tailored to the hardware of the WBR. A cost function is designed, and the Dung Beetle Optimizer (DBO) is employed to optimize the MPC’s prediction and control horizons. An experimental platform is built, and impact and load disturbance experiments are conducted. The experimental results show that, under impact disturbances, the pitch angle and displacement overshoot with optimized MPC are reduced by 58.57% and 42.20%, respectively, compared to unoptimized LQR. Under load disturbances, the pitch angle and displacement overshoot are reduced by 17.09% and 15.53%, respectively, with both disturbances converging to the equilibrium position. Full article

Background/Objectives: This study investigated postural stability in male inmates of a semi-open correctional facility, with a specific focus on comparing individuals with and without a history of substance dependence. The aim was to identify how addiction-related neurophysiological changes impact postural control under varying sensory and biomechanical demands. Methods: A total of 47 adult male prisoners (mean age: 24.3 years) participated in this study. Nineteen inmates had a documented history of alcohol or drug dependence (addicted group), while twenty-eight had no such history (non-addicted group). All participants were physically able and free of neurological disorders. Postural control was assessed using a stabilometric platform and wireless IMU across six 30 s standing tasks of varying difficulty (bipedal/unipedal stance and eyes open/closed). Linear (center of pressure path and ellipse area) and nonlinear (sample entropy, fractal dimension, and the Lyapunov exponent) sway metrics were analyzed, along with trunk kinematics from IMU data. This study received institutional ethical approval; trial registration was not required. Results: The addicted group showed greater instability, especially in the eyes-closed and single-leg tasks, with increased sway and irregularity in the anterior–posterior direction. IMU data indicated altered trunk motion, suggesting impaired neuromuscular control. In contrast, non-addicted individuals demonstrated more efficient, targeted postural strategies, while addicted participants relied on broader, less selective movements, possibly reflecting compensatory or neuroadaptive changes from substance use. Conclusions: Substance dependence is associated with compromised postural stability in incarcerated men. Balance assessments may be valuable for detecting functional impairments and guiding rehabilitation within prison healthcare systems. Full article

Wheeled bipedal robots (WBRS) combine the terrain adaptability potential of legged robots with the motion efficiency of wheeled robots, but the terrain adaptability is affected by the control system. Aiming at the defect that the traditional modeling ignores the dynamic changes in head angle and center of mass height, this paper proposes a method of integrated dynamic modeling and hierarchical control. The posture balance optimizes the system performance index through the linear quadratic regulator (LQR) to control the in-wheel motor, and the state feedback matrix is designed to suppress the tipping caused by external interference. At the same time, the changes in head angle and center of mass height are included in the balance control variables. The center of mass height changes are fed back through the proportional differential (PD) control and virtual model control (VMC) algorithm to control the joint motor. Simulation experiments are carried out on multiple platforms to verify that the proposed method effectively improves the control robustness of the traditional wheeled bipedal robot through geometric-dynamic coupling modeling and LQR-PD hybrid control, providing a new method of complex terrain adaptive control. Full article

In the study of biomechanical models, balance represents a complex problem due to the issue of indeterminate forces while standing. In order to solve this problem, it is essential to measure the ground reaction forces (GRFs) applied to each foot independently. The present work proposes a methodology for determining the independent GRF applied to each foot while standing when only one forceplate is available. For this purpose, an analytical method is proposed to determine the distribution of vertical GRFs and the position of the independent center of pressure (CoP) in each foot. Concurrently, several neural network (NN) models are trained to improve the results obtained. This hypothesis is experimentally validated by a self-developed device that allows one to simultaneously obtain the vertical GRF and CoP location of each foot at the same time that the GRF and the global CoP location are obtained from a single forceplate. The results obtained achieve a CoP position error of less than 8% and a vertical force error of 2%. The analytical hypothesis is demonstrated to offer a satisfactory level of precision, while the NN is shown to result in considerable improvement in some cases. Full article

In high-dynamic bipedal locomotion control, robotic systems are often constrained by motor torque limitations, particularly during explosive tasks such as jumping. One of the key challenges in reinforcement learning lies in bridging the sim-to-real gap, which mainly stems from both inaccuracies in simulation models and the limitations of motor torque output, ultimately leading to the failure of deploying learned policies in real-world systems. Traditional RL methods usually focus on peak torque limits but ignore that motor torque changes with speed. By only limiting peak torque, they prevent the torque from adjusting dynamically based on velocity, which can reduce the system’s efficiency and performance in high-speed tasks. To address these issues, this paper proposes a reinforcement learning jump-control framework tailored for tendon-driven bipedal robots, which integrates dynamic torque boundary constraints and torque error-compensation modeling. First, we developed a torque transmission coefficient model based on the tendon-driven mechanism, taking into account tendon elasticity and motor-control errors, which significantly improves the modeling accuracy. Building on this, we derived a dynamic joint torque limit that adapts to joint velocity, and designed a torque-aware reward function within the reinforcement learning environment, aimed at encouraging the policy to implicitly learn and comply with physical constraints during training, effectively bridging the gap between simulation and real-world performance. Hardware experimental results demonstrate that the proposed method effectively satisfies actuator safety limits while achieving more efficient and stable jumping behavior. This work provides a general and scalable modeling and control framework for learning high-dynamic bipedal motion under complex physical constraints. Full article

Achieving energy-efficient, human-like gait remains a major challenge in bipedal humanoid robotics, as traditional serial actuation architectures often lead to high instantaneous power peaks and uneven load distribution. This study addresses the lack of research on how mechanical symmetry, achieved through parallel actuation, can improve power management in lower-limb joints. We developed a 14-degree-of-freedom (DOF) hip-sized bipedal robot model and conducted simulations comparing a conventional serial configuration—using single-DOF rotary actuators—with a novel parallel configuration that employs paired linear actuators at the hip pitch, hip roll, ankle pitch, and ankle roll joints. Simulation results over a standardized walking cycle show that the parallel configuration reduces peak hip-pitch power by 80.4% and peak ankle-pitch power by 53.5%. These findings demonstrate that incorporating actuator symmetry through parallel joint design significantly reduces actuator stress, improves load sharing, and enhances overall energy efficiency in bipedal locomotion. Full article