Lower limb exoskeletons and exosuits have shown promise in augmenting human physical capabilities, with applications ranging from rehabilitation to performance enhancement. Accurate evaluation of their impact on metabolic energy expenditure is crucial for optimizing design and control strategies. While experimental measurement of metabolic cost via indirect calorimetry provides direct assessment, it is often impractical outside laboratory settings. Computational models offer an alternative, but their effectiveness in predicting metabolic cost changes induced by assistive devices remains underexplored. This study investigates the impact of incorporating different levels of complexity and sensory information, as well as various metabolic cost models, on estimating muscle metabolic cost during walking with a passive biarticular thigh exosuit. We compare three modeling approaches: joint-space dynamics, musculoskeletal simulation with effort minimization, and EMG-informed musculoskeletal simulation, each employing several metabolic models. Results show that EMG-informed musculoskeletal simulation, particularly using the Uchida (2016) metabolic model, provides the highest accuracy in predicting metabolic cost changes. Musculoskeletal simulation with effort minimization also shows promise, offering a viable alternative without the need for EMG data. These findings highlight the potential of computational models in evaluating and optimizing assistive devices.

The virtual pivot point (VPP), a theoretical convergence point of ground reaction forces during gait, has gained attention for its potential to uncover underlying locomotor control strategies. Here, we present the first investigation of VPP in individuals with unilateral above-knee amputation, using a publicly available dataset of 18 participants. Subjects were categorized into K2 (walking speeds 0.4–0.8m/s) and K3 (0.6–1.4m/s) functional levels. Our findings show that both groups demonstrate high sagittal-plane VPP quality, comparable to that of healthy individuals, with R2> 95%, indicating a strong relationship between VPP formation and sagittal plane dynamics. Conversely, in the frontal plane, VPP analysis reveals greater variability and lower quality, indicating the absence of a well-defined pivot during gait. Notably, frontal-plane VPP quality deteriorates with increasing walking speed, particularly in K3 ambulators. While this speed-dependency is observed in healthy individuals as well, the rate of decline is significantly steeper in amputees. Additionally, spatial analysis of VPP positions reveals a consistent elevation of the amputated leg’s VPP compared to the intact leg. These findings emphasize the importance of frontal plane dynamics in amputee gait and suggest improvements in prosthetic design to enhance control and promote more symmetrical, natural gait.

Human locomotion exhibits extraordinary adaptability and robustness, yet the mechanisms by which lower limbs adjust to sudden environmental disruptions remain poorly understood. To address this, we employed the bioinspired human-sized EPA-Hopper II robot to examine how lower-limb joints recover from an abrupt drop in ground height, mimicking unexpected perturbations encountered in natural settings. Our study investigates the roles of the monoarticular soleus (SOL) and biarticular gastrocnemius (GAS) muscle configurations, focusing on how their compliance influences the robot’s hopping stability. Experiments reveal that a coordinated interplay between SOL and GAS markedly improves recovery from disturbances, enhancing energy distribution and joint synchronization. Detailed kinematic and power analyses show that GAS facilitates energy transfer across joints, while SOL’s spring-like properties support rapid recovery. These results highlight how bioinspired muscle arrangements enable robust locomotion through intrinsic mechanical interactions. By leveraging a robotic platform to probe these dynamics, this work deepens our understanding of biological locomotion and informs the design of bioinspired bipedal robots and prosthetics capable of thriving in unpredictable environments.

The fear of falling due to changes in gait leads to a decrease in quality of life in Parkinson's patients. Also, Parkinson's patients require medical treatment due to falling each year. However, the reasons for the changed walking style still remain unknown. The goal of this study is the evaluation of possible reasons for the changed walking pattern in Parkinson's disease. A pilot study is conducted, which includes patient experiments, data analysis, and biomechanical modeling. Differences between Parkinsonian and healthy gait are detected and replicated by the model. The model represents simplified body dynamics and is optimized for healthy and Parkinsonian gait, respectively. Comparison measures are ground reaction forces, joint torques, and the virtual pivot point (VPP) location. The VPP is the intersection point of all forces throughout the gait cycle and is closely correlated to human balancing and stability. Parkinsonian gait showed different force and torque curves compared to healthy walking and a VPP below the center of mass location (negative VPP). The model represents healthy walking well. Specific Parkinsonian behavior can be explained by the changed modulation of the model. However, a negative VPP location turns the model unstable as, after two steps, the trunk tilts more than 90 degrees forward. Our modeling with negative VPP shows instability effects observed in Parkinson's patients walking who struggle with frequent stumbles and falls. Such modeling approaches could be used for developing new rehabilitation techniques and gait assistance devices.

The lower limb morphology of biological locomotors is abundant in muscle-tendon units. Yet, not much is known about how these actuation units contribute to the output performance and energy economy of movements. In this work, we investigate the functionality of four of the important lower limb muscles - Vastus, Popliteus, Soleus, and Gastrocnemius - in a hopping task at different frequencies (1.5-3.5 Hz). These muscles are implemented as pneumatic artificial muscles (PAMs) on the EPA-Hopper-II robot, which is a human-sized 3-segmented leg co-actuated by electrical motors and PAMs. A bioinspired reflex-based Force Modulated Control (FMC) is also implemented on the robot to achieve hopping at different frequencies. The results show that the Vastus contributes the most to energy-efficient hopping at low to mid frequencies. The biarticular Gastrocnemius also helps increase efficiency at low frequencies. Further, it is found that the Gastrocnemius synchronizes the knee-ankle motion and mitigates lateral knee motion. The outcomes of this work add further evidence to hypotheses regarding human lower-limb actuation and proper recruitment of muscles for building more efficient robots.

There is a long history demonstrating humans' tendency to create artificial copies of living creatures. For moving machines called robots, actuators play a key role in developing human-like movements. Among different types of actuation, PAMs (pneumatic artificial muscles) are known as the most similar ones to biological muscles. In addition to similarities in force generation mechanism (tension based), the well-accepted argumentation from Klute et al., states that the PAM force-length (fl) behavior is close to biological muscles, while the force-velocity (fv) pattern is different. Using the multiplicative formulation of the pressure (as an activation term), fland fvbeside an additive passive parallel elastic element, we present a new model of PAM. This muscle-based model can predict PAM dynamic behaviors with high precision. With a second experiment on a two-segmented leg, the proposed model is verified to predict the generated forces of PAMs in an antagonistic arrangement. Such a dynamic muscle-like model of artificial muscles can be used for the design and control of legged robots to generate robust, efficient and versatile gaits.

Balancing or postural stability is one of important locomotor subfunctions in bipedal gaits. The inverted pendulum and virtual pivot point (VPP) are common modeling approaches to analyze balance control in human and robot walking. In this paper, we employ the VPP concept to investigate posture control in the frontal plane. The outcomes demonstrate that unlike posture control in the sagittal plane, the VPP in the frontal plane is place below center of mass. This finding explains a novel hybrid strategy for lateral stability in human walking. The here proposed model shows that switching between unstable inverted virtual pendulums generate stable posture control in the frontal plane. This outcome is consistent within a group of seven human subjects walking at normal and slow speeds.

Several modeling studies, which address neuromuscular control in impaired unperturbed gaits, were performed to predict human strategies to cope with lateral asymmetries in the body. Experimental studies show different step length and stance time relations between limbs in walking of stroke patients. By extension of a bipedal SLIP (spring-loaded inverted pendulum) based model and the corresponding controllers to 3D space, we focus on different features of the pathologic gaits. The introduced model is based on an extension of the FMCH (force modulated compliant hip) and VBLA (velocity based leg adjustment) model to 3D space. With the proposed model, asymmetric leg and control parameters can result in similar gait patterns as observed in experiments. These parameters comprise hip stiffness and rest angles in FMCH models and the tuning parameter of VBLA for foot placement. It is shown that asymmetries in muscle properties (e.g. stiffness) and leg adjustment can play an important role in generating pathologic gaits.

Walking with 5-link model has been achieved by HZD (Hybrid Zero Dynamics) controller based on virtual constraints. These holonomic constraints are obtained by optimizing a set of virtual relations (e.g., Beziér polynomial) between system states which mostly do not have physical interpretations. In this paper, the virtual constraints are designed using human walking experiment data. Inspiring from human locomotion, different polynomials are extracted to mimic human joint angles patterns during walking. The virtual leg angle is the increasing variable which synchronize the joints angles and defines the virtual constraints. Simulation results show that stable locomotion with leg and upper-body behavior similar to human experiment data is achieved for a wide range of speeds and body configuration parameters. VPP (Virtual Pivot Point) concept, a significant balancing feature found in human/animal locomotion, is investigated for different gait speeds as a performance index to compare the kinetic behavior of the simulated and human walking. Hence, we present human-like posture control as an outcome of motion control achieved by HZD with human inspired virtual constraints.

Spring loaded inverted pendulum (SLIP) model used simple spring mass mechanism to explain leg function and ground reaction force in legged locomotion. Balancing the upper body can be addressed by addition of a rigid trunk to this template model. The resulting model is not conservative and needs hip torque to keep the trunk upright during locomotion, like humans. Leg force modulated compliant hip (FMCH) is our new model for postural control in walking which employs the leg force feedback to adjust the hip compliance. Such an application of positive force feedback presents a new template for neuromuscular model. This method provides stable and robust walking in simulations and also mimics human-like kinetic behavior. Analyzing human walking experiment shows that FMCH can explain the hip torque-angle relation for different walking speeds. Finally, this approach may physically implement the virtual pendulum (VP) concept, observed in human/animal locomotion.

There are three challenges in finding robot location and environment map which are accuracy, robustness and computational cost. Some simultaneous localisation and mapping (SLAM) methods work perfectly with ideal environment without uncertainties, noise and disturbances; however, in the real world, the performance considerably decreases. Designing a practical method of finding the robot position during motion, particularly when it is fully autonomous, should not be time consuming. This point has been forgotten in many SLAM approaches. Finding a comprehensive algorithm for solving all the problems is very complex. In this paper, with a new geometrical viewpoint, the objective is to solve them simultaneously as much as possible. However, with the proposed technique, sub-optimal solutions of different targets will be found. This method, named ICE matching (Intersection, Corner and End of Wall features), is capable of being combined with different mapping algorithms to solve the SLAM problem. Defining new informative features and novel matching and hierarchical optimisation mechanisms, congregated in this method, create a robust practical technique in terms of accuracy and convergence rate. To evaluate the accuracy, qualities of different popular SLAM algorithms are compared with the present approach in some similar data sets. In addition, evaluating ICE matching on a real robot with manual and autonomous driving shows its considerably high performance and robustness against noises and shortage of data in online applications, in comparison with other approaches.

Swing leg adjustment, repulsive leg function and balance are key elements in the control of bipedal locomotion. In simple gait models like spring-loaded inverted pendulum (SLIP), swing leg control can be applied to achieve stable running. The aim of this study is to investigate the ability of pendulum like swing leg motion for stabilizing running and reproducing a desired (human like) gait pattern. The employed running model consists of two sub-models: SLIP model for the stance phase and a pendulum based control for the swing phase. It is shown that with changing the pendulum length at each step, stable running gaits with widely different performances are achieved. The body vertical speed at take off is utilized as feedback information to tune the pendulum length as the control parameter. In particular, the effect of the pendulum length adjustment on the motion characteristics like horizontal speed, apex height and the stabilized system energy will be investigated. With this method key features of the human like swing leg motion e.g. leg retraction can be reproduced. Higher speeds correspond larger angular motion of each leg which is in agreement with experimental results in previous studies. The presented model also explains the swing-leg to stance-leg interaction mechanism which was not addressed in the underlying SLIP model. This conceptual model can be considered as a functional mechanical template for legged locomotion and can be used to build more complex models, e.g. having segmented legs or an upper body.

Bipedal locomotion can be divided into primitive tasks, namely repulsive leg behavior (bouncing against gravity), leg swing (protraction and retraction) and body alignment (balancing against gravity). In the bipedal spring-mass model for walking and running, the repulsive leg function is described by a linear prismatic spring. This paper adopts two strategies for swinging and bouncing control from conceptual models for the human-inspired musculoskeletal BioBiped robot. The control approach consists of two layers, velocity based leg adjustment (VBLA) and virtual model control to represent a virtual springy leg between toe and hip. Additionally, the rest length and stiffness of the virtual springy leg are tuned based on events to compensate energy losses due to damping. In order to mimic human locomotion, the trunk is held upright by physical constraints. The controller is implemented on the validated detailed simulation model of BioBiped. In-place as well as forward hopping and switching between these two gaits are easily achieved by tuning the parameters for the leg adjustment, virtual leg stiffness and injected energy. Furthermore, it is shown that the achieved motion performance of in-place hopping agrees well with that of human subjects.

Bouncing, balancing and swinging the leg forward can be considered as three basic control tasks for bipedal locomotion. Defining the trunk by an unstable inverted pendulum, balancing as being translated to trunk stabilization is the main focus of this paper. The control strategy is to generate a hip torque to have upright trunk to achieve robust hopping and running. It relies on the Virtual Pendulum (VP) concept which is recently proposed for trunk stabilization, based on human/animal locomotion analysis. Based on this concept, a control approach, named Virtual Pendulum Posture control (VPPC) is presented, in which the trunk is stabilized by redirecting the ground reaction force to a virtual support point. The required torques patterns generated by the controller, could partially be exerted by elastic structures like hip springs. Hybrid Zero Dynamics (HZD) control approach is also applied as an exact method of keeping the trunk upright. Stability of the motion which is investigated by Poincaré map analysis could be achieved by hip springs, VPPC and HZD. The results show that hip springs, revealing muscle properties, could facilitate trunk stabilization. Compliance in hip produces acceptable performance and robustness compared with VPPC and HZD, while it is a passive structure.

This paper presents a new control approach to achieve robust hopping with upright trunk in the sagittal plane. It relies on an innovative concept for trunk stabilization, called Virtual Pendulum concept, recently proposed, based on experimental finding in animal locomotion. With this concept, the trunk is stabilized by redirecting the ground reaction force to a virtual support point, named Virtual Pivot Point (VPP). This concept is combined with a new leg adjustment scheme to induce stable hopping when an extended trunk is added to SLIP model. The stability is investigated by Poincaré map analysis. With fixed VPP position, stability, disturbance rejection and moderate robustness are achieved, but with low convergence speed. To improve the performances and attain higher robustness, event based control of VPP position is introduced, using feedback of the system state at apex. Dead beat control and Discrete LQR are alternatively considered to adjust the feedback gains. In both cases, considerable enhancements with respect to stability, convergence speed and robustness against perturbations are achieved.

Application of the compliance to increase the robustness of the bipedal walker locomotion is the main target of this paper. The control of bipedal robots with point feet is one of the most challenging problems in the domain of hybrid dynamical systems. In order to make an exponentially stable periodic walking for an Acrobot as a planar biped with only one degree of freedom, a controller is designed based on the hybrid zero dynamics analysis. Then, through appropriate alterations in robot structure, the controller complexities are reduced which result in fewer parameters for tuning. Improvement of the robot structure via compliance insertion not only compensates the lower degree of freedom of the control design process, but also expands the domain of stability of the closed-loop system. In this regard, a nonlinear damper is inserted between two legs of Acrobot to enable it to walk on a wider range of slopes. The main controller is designed for walking on a flat terrain and the compliance of the damper empowers it to make stable walking on slopes up to 17°. The simulation results confirmed the efficiency of the proposed approach.

Scan matching as a basic part of SLAM has a key role in localization and even mapping of mobile robots. Our innovative method named ICE matching presents a fast and accurate method to solve the challenges of this problem. Novelty in defining new features, matching mechanism and new state estimation approach congregated in this method creates a robust practical technique. Comparison with some high quality scan matching methods from different viewpoints illustrates the performance of ICE matching. It was applied besides Grid based mapping to generate fine tuned maps to compare with slam methods.

An intelligent controller is applied to control the motion of an omni-directional robot in this paper. The controller is based on the brain-emotional-learning algorithm, which is inspired from a computational model of limbic system in the mammalian brain. The brain-emotional-learning-based intelligent controller (BELBIC) with quadratic reward, comparable to linear-quadratic-regulator (LQR) control, is used in an omni-directional robot. The contribution of BELBIC in improving the control system performance is shown via simulation and experimental implementation. The results show that this method can be used in more practical problems efficiently. Optimization of the control effort or other objectives like energy minimization, as additional concerns constitute other advantages of the proposed method.