Search Results (17)

Humanoid robots are typically designed for static environments, but real-world applications demand robust performance under dynamic, uncertain conditions. This paper introduces a perceptive motion planning and control algorithm that enables humanoid robots to navigate and operate effectively in environments with unpredictable kinematic and dynamic disturbances. The proposed algorithm ensures synchronized multi-limb motion while maintaining dynamic balance, utilizing real-time feedback from force, torque, and inertia sensors. Experimental results demonstrate the algorithm’s adaptability and robustness in handling complex tasks, including walking on uneven terrain and responding to external disturbances. These findings highlight the potential of perceptive motion planning in enhancing the versatility and resilience of humanoid robots in uncertain environments. The results have potential applications in search-and-rescue missions, healthcare robotics, and industrial automation, where robots operate in unpredictable or dynamic conditions. Full article

Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot’s state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot’s perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods. Full article

In robotics, the ability of quadruped robots to perform tasks in industrial, mining, and disaster environments has already been demonstrated. To ensure the safe execution of tasks by the robot, meticulous planning of its foot placements and precise leg control are crucial. Traditional motion planning and control methods for quadruped robots often rely on complex models of both the robot itself and its surrounding environment. Establishing these models can be challenging due to their nonlinear nature, often entailing significant computational resources. However, a more simplified approach exists that focuses on the kinematic model of the robot’s floating base for motion planning. This streamlined method is easier to implement but also adaptable to simpler hardware configurations. Moreover, integrating impedance control into the leg movements proves advantageous, particularly when traversing uneven terrain. This article presents a novel approach in which a quadruped robot employs impedance control for each leg. It utilizes sixth-degree Bézier curves to generate reference trajectories derived from leg velocities within a planar kinematic model for body control. This scheme effectively guides the robot along predefined paths. The proposed control strategy is implemented using the Robot Operating System (ROS) and is validated through simulations and physical experiments on the Go1 robot. The results of these tests demonstrate the effectiveness of the control strategy, enabling the robot to track reference trajectories while showing stable walking and trotting gaits. Full article

A rollator, one of the most widely used among walking assistance devices, can assist the elderly with stable walking in their daily lives. In this study, we investigated how the structural characteristics of two types of rollators affect the upper and lower extremity muscle activity and plantar pressure of the elderly in various walking environments. We quantified muscle activity (upper and lower limbs) and plantar pressure (mean force, peak pressure, and contact area) of 11 older adults walking in various environments (flat, obstacle, uneven, and sloped terrain) using two types of rollators. Upper extremity muscle activity was highest in the obstacle terrain and the uneven terrain, and a significant difference was found due to the structural differences of the rollator. Additionally, it was observed that lower extremity muscle activity and plantar pressure patterns appeared in accordance with the gait strategy to maintain stability in an unstable or inclined walking environment. In other words, it was confirmed that the weight of the rollator, the size of the wheel, grip type, and the auxiliary tools had a great effect on the upper and lower extremity muscle activity and plantar pressure of the elderly during walking. From the results of this study, it can be suggested that it is absolutely necessary to consider the biomechanical characteristics of the elderly and the structure of the rollator, which appear differently depending on the walking environment, in the development of walking aids. In the future, more clinical data will be collected, and based on this a rollator that can safely assist the elderly in various walking environments will be developed. Full article

Recent years have witnessed relevant advancements in the quality of life of persons with lower limb amputations thanks to the technological developments in prosthetics. However, prostheses that provide information about the foot–ground interaction, and in particular about terrain irregularities, are still missing on the market. The lack of tactile feedback from the foot sole might lead subjects to step on uneven terrains, causing an increase in the risk of falling. To address this issue, a biomimetic vibrotactile feedback system that conveys information about gait and terrain features sensed by a dedicated insole has been assessed with intact subjects. After having shortly experienced both even and uneven terrains, the recruited subjects discriminated them with an accuracy of 87.5%, solely relying on the replay of the vibrotactile feedback. With the objective of exploring the human decoding mechanism of the feedback startegy, a KNN classifier was trained to recognize the uneven terrains. The outcome suggested that the subjects achieved such performance with a temporal dynamics of 45 ms. This work is a leap forward to assist lower-limb amputees to appreciate the floor conditions while walking, adapt their gait and promote a more confident use of their artificial limb. Full article

Uneven terrain walking is hard to achieve for most child-size humanoid robots, as they are unable to accurately detect ground conditions. In order to reduce the demand for ground detection accuracy, a walking control framework based on centroidal momentum allocation is studied in this paper, enabling a child-size humanoid robot to walk on uneven terrain without using ground flatness information. The control framework consists of three controllers: momentum decreasing controller, posture controller, admittance controller. First, the momentum decreasing controller is used to quickly stabilize the robot after disturbance. Then, the posture controller restores the robot posture to adapt to the unknown terrain. Finally, the admittance controller aims to decrease contact impact and adapt the robot to the terrain. Note that the robot uses a mems-based inertial measurement unit (IMU) and joint position encoders to calculate centroidal momentum and use force-sensitive resistors (FSR) on the robot foot to perform admittance control. None of these is a high-cost component. Experiments are conducted to test the proposed framework, including standing posture balancing, structured non-flat ground walking, and soft uneven terrain walking, with a speed of 2.8 s per step, showing the effectiveness of the momentum allocation method. Full article

In this paper, a compliant control scheme based on the optimization of the contact force of the robot leg is proposed to improve the stability of the whole moving process of the robot. Firstly, according to the motion state of the robot, the change of its center of gravity is analyzed, then the stable gait of the robot is determined by the stability margin, and the smooth control of the robot’s foot trajectory is realized. Finally, the compliant control model of the robot leg is established. In the process of moving, the contact force between the legs and the ground is optimized in real-time, so that the composite robot can walk steadily on uneven terrain. The 3-D model of the scorpion composite robot was built with ADAMS software, and dynamics simulation was carried out according to the compliant control scheme. This paper takes the robot’s walking speed and torso angle as performance evaluation indexes and verifies the effectiveness of the compliant control scheme. The cooperative motion stability test is carried out on the actual uneven terrain. The test results show that the robot’s pitch angle and roll angle are between ±0.5°, which meets the motion stability requirements of the robot and verifies the correctness of the compliant control scheme and control model proposed in this paper. Full article

In order to perform various complex tasks in place of humans, humanoid robots should walk robustly in the presence of interference. In the paper, an improved model predictive control (MPC) method based on the divergent components of motion (DCM) is proposed. Firstly, the humanoid robot model is simplified to a finite-sized foot-pendulum model. Then, the gait of the humanoid robot in the single-support phase (SSP) and double-support phase (DSP) is planned based on DCM. The center of mass (CoM) of the robot will converge to the DCM, which simplifies the feedback control process. Finally, an MPC controller incorporating an extended Kalman filter (EKF) is proposed to realize the tracking of the desired DCM trajectory. By adjusting the step duration, the controller can compensate for CoM trajectory errors caused by disturbances. Simulation results show that—compared with the traditional method—the method we propose achieves improvements in both disturbed walking and uneven-terrain walking. Full article

Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson’s disease (PD) has been primarily limited to detection of steady-state/static tasks (e.g., sitting, standing, walking). To date, identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of body locations which could adversely affect user convenience and system performance. Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Comparing the F1 score of a given signal across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent training appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent training and/or biomechanical data from multiple body locations. The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson’s disease. Full article

A conventional blind walking algorithm has low walking stability on uneven terrain because a robot cannot rapidly respond to height changes of the ground due to limited information from foot force sensors. In order to cope with rough terrain, it is essential to obtain 3D ground information. Therefore, this paper proposes a vision-guided six-legged walking algorithm for stable walking on uneven terrain. We obtained noise-filtered 3D ground information by using a Kinect sensor and experimentally derived coordinate transformation information between the Kinect sensor and robot body. While generating landing positions of the six feet from the predefined walking parameters, the proposed algorithm modifies the landing positions in terms of reliability and safety using the obtained 3D ground information. For continuous walking, we also propose a ground merging algorithm and successfully validate the performance of the proposed algorithms through walking experiments on a treadmill with obstacles. Full article

A walker assists elderly people with age-related reduced walking ability and helps to improve stability and balance ability. However, if the general-type walker (GTW) is used on an uneven, obstacle, or sloped terrain, it may cause excessive muscle use and falls. Therefore, in this study, we developed a caterpillar-type walker (CTW) that elderly people can safely use in various terrains. Twelve elderly who were able to walk normally participated in the study. The activity of upper and lower extremity muscles, the number of obstacles overcome, and walking speed was compared and analyzed when using two types of walkers in uneven terrain, obstacle terrain, and sloped terrain. In addition, satisfaction with the use of these walkers was evaluated. When CTW was used, the activity of the muscles of the upper and lower extremities was significantly reduced compared to the use of GTW on all terrains. The walker developed in this study overcame obstacles of all heights, but the GTW failed to overcome obstacles starting from the 2 cm section. In terms of walking speed, when the CTW was used, the walking speed was higher than that of the GTW in uneven terrain and obstacle terrain. In satisfaction, there were significant differences in safety, durability, simplicity of use, comfort, and effectiveness. Through these results, it was confirmed that the CTW can efficiently and safely assist the elderly in walking on uneven terrain, obstacle terrain, and inclined terrain. Full article

Humans and other animals can quickly respond to unexpected terrains during walking, but little is known about the cortical dynamics in this process. To study the impact of unexpected terrains on brain activity, we allowed rats with blocked vision to walk on a treadmill in a bipedal posture and then walk on an uneven area at a random position on the treadmill belt. Whole brain EEG signals and hind limb kinematics of bipedal-walking rats were recorded. After encountering unexpected terrain, the θ band power of the bilateral M1, the γ band power of the left S1, and the θ to γ band power of the RSP significantly decreased compared with normal walking. Furthermore, when the rats left uneven terrain, the β band power of the bilateral M1 and the α band power of the right M1 decreased, while the γ band power of the left M1 significantly increased compared with normal walking. Compared with the flat terrain, the θ to low β (3–20 Hz) band power of the bilateral S1 increased after the rats contacted the uneven terrain and then decreased in the single- or double- support phase. These results support the hypothesis that unexpected terrains induced changes in cortical activity. Full article

Stability is a prerequisite for legged robots to execute tasks and traverse rough terrains. To guarantee the stability of quadruped locomotion and improve the terrain adaptability of quadruped robots, a stability-guaranteed and high terrain adaptability static gait for quadruped robots is addressed. Firstly, three chosen stability-guaranteed static gaits: intermittent gait 1&2 and coordinated gait are investigated. In addition, then the static gait: intermittent gait 1, which is with the biggest stability margin, is chosen to do a further research about quadruped robots walking on rough terrains. Secondly, a position/force based impedance control is employed to achieve a compliant behavior of quadruped robots on rough terrains. Thirdly, an exploratory gait planning method on uneven terrains with touch sensing and an attitude-position adjustment strategy with terrain estimation are proposed to improve the terrain adaptability of quadruped robots. Finally, the proposed methods are validated by simulations. Full article

Research on a terrain-blind walking control that can walk stably on unknown and uneven terrain is an important research field for humanoid robots to achieve human-level walking abilities, and it is still a field that needs much improvement. This paper describes the design, implementation, and experimental results of a robust balance-control framework for the stable walking of a humanoid robot on unknown and uneven terrain. For robust balance-control against disturbances caused by uneven terrain, we propose a framework that combines a capture-point controller that modifies the control reference, and a balance controller that follows its control references in a cascading structure. The capture-point controller adjusts a zero-moment point reference to stabilize the perturbed capture-point from the disturbance, and the adjusted zero-moment point reference is utilized as a control reference for the balance controller, comprised of zero-moment point, leg length, and foot orientation controllers. By adjusting the zero-moment point reference according to the disturbance, our zero-moment point controller guarantees robust zero-moment point control performance in uneven terrain, unlike previous zero-moment point controllers. In addition, for fast posture stabilization in uneven terrain, we applied a proportional-derivative admittance controller to the leg length and foot orientation controllers to rapidly adapt these parts of the robot to uneven terrain without vibration. Furthermore, to activate position or force control depending on the gait phase of a robot, we applied gain scheduling to the leg length and foot orientation controllers, which simplifies their implementation. The effectiveness of the proposed control framework was verified by stable walking performance on various uneven terrains, such as slopes, stone fields, and lawns. Full article

Roaming complexity in terrains and unexpected environments pose significant difficulties in robotic exploration of an area. In a broader sense, robots have to face two common tasks during exploration, namely, walking on the drylands and swimming through the water. This research aims to design and develop an amphibious robot, which incorporates a webbed duck feet design to walk on different terrains, swim in the water, and tackle obstructions on its way. The designed robot is compact, easy to use, and also has the abilities to work autonomously. Such a mechanism is implemented by designing a novel robotic webbed foot consisting of two hinged plates. Because of the design, the webbed feet are able to open and close with the help of water pressure. Klann linkages have been used to convert rotational motion to walking and swimming for the animal’s gait. Because of its amphibian nature, the designed robot can be used for exploring tight caves, closed spaces, and moving on uneven challenging terrains such as sand, mud, or water. It is envisaged that the proposed design will be appreciated in the industry to design amphibious robots in the near future. Full article