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Active power-assist lower limb exoskeleton robots aim to enhance wearer assistance while ensuring wearer comfort and simplifying the exoskeleton’s design and control. This study proposes an active assistance method known as Equivalent Force on Connection (EFOC). The EFOC method effectively addresses the limitations encountered in conventional Joint Torque Proportional Compensation (JTPC) approaches. These limitations include the necessity for exoskeleton robot configurations to align with human limb structures for parallel assistance at each lower limb joint, as well as the exoskeleton’s inability to contribute a greater proportion of assistance due to the excessive load on specific skeletal and muscular structures, resulting in wearer discomfort. Furthermore, the effectiveness of the EFOC method is evaluated and validated for assistance during both the stance and swing phases of single-leg movements. Finally, the proposed EFOC method is implemented on a hydraulic-driven lower limb exoskeleton robot to assist wearers in squatting, stepping, and jumping locomotion. The experimental results demonstrate that the proposed EFOC method can effectively achieve the desired assistance effect.

This paper proposes a method to realize the robot’s self-learning of environment by using an episode cognition model inspired by the hippocampus. The episode cognition map is suitable for robot navigation in an unknown environment, which solves the problem of robustness of the robot’s perception in complex and dynamic environments. The model called the hippocampus episode cognitive network (HEC), is based on the hippocampus CA1, CA3, and the dentate gyrus, and combined with the adaptive resonance theory (ART) network. It extract new events through the incremental generation of cognitive neurons, and encode the events into episodes through space-time connections. The episode nodes can be connected to generate an episode cognition map. This method can learn, store, and update information for autonomous mobile robots in an unknown environment. Based on the episode cognitive map, the path trajectory can be predicted through the playback of the episode neuron. The experimental results on the mobile robot show that this method can effectively improve the robot’s adaptability for location and mapping in a complex and dynamic environment.

The traditional Cubature Kalman Filter (CKF) and its derived algorithms cannot work without the two hypotheses of Kalman Filter (KF), one is that the system model is accurate and the other is the system is only influenced by independent white noise with known statistical characteristics. However, it is difficult to fully guarantee the above hypotheses in actual operation. The presence of multiplicative noise undermines the first hypothesis, while the additive noise correlation undermines the second hypothesis. Given invalid hypotheses, the estimated performances of CKF and its derivative algorithms are degraded drastically. To solve this issue, this article proposes Multiplicative Noises and Additive Correlated Noises Cubature Kalman Filter (MACNCKF), which solves state estimation problems involving multiplicative noise and additive noise while maintaining CKF advantages. Moreover, this algorithm has been testified to be correct and easy to be transplanted to CKF and its derivative algorithm after strict mathematical derivation. When the system lacks multiplicative noise and additive noise, the algorithm is degraded to corresponding algorithm in a general form, which helps extend the application environment of CKF and its derivative algorithms, thus improving robustness. Numerical simulation and experiments on quadruped robot system indicate that MACNCKF can effectively solve state estimation problem involving both multiplicative noise and additive noise. Given time consumption in MACNCKF basically comparable to that in CKF, MACNCKF has improved estimation accuracy, robustness and reliability.

The hippocampus and its accessory are the main areas for spatial cognition. It can integrate paths and form environmental cognition based on motion information and then realize positioning and navigation. Learning from the hippocampus mechanism is a crucial way forward for research in robot perception, so it is crucial to building a calculation method that conforms to the biological principle. In addition, it should be easy to implement on a robot. This paper proposes a bionic cognition model and method for mobile robots, which can realize precise path integration and cognition of space. Our research can provide the basis for the cognition of the environment and autonomous navigation for bionic robots.

Many algorithms in probabilistic sampling-based motion planning have been proposed to create a path for a robot in an environment with obstacles. Due to the randomness of sampling, they can efficiently compute the collision-free paths made of segments lying in the configuration space with probabilistic completeness. However, this property also makes the trajectories have some unnecessary redundant or jerky motions, which need to be optimized. For most robotics applications, the trajectories should be short, smooth and keep away from obstacles. This paper proposes a new trajectory optimization technique which transforms a polygon collision-free path into a smooth path, and can deal with trajectories which contain various task constraints. The technique removes redundant motions by quadratic programming in the parameter space of trajectory, and converts collision avoidance conditions to linear constraints to ensure absolute safety of trajectories. Furthermore, the technique uses a projection operator to realize the optimization of trajectories which are subject to some hard kinematic constraints, like keeping a glass of water upright or coordinating operation with dual robots. The experimental results proved the feasibility and effectiveness of the proposed method, when it is compared with other trajectory optimization methods.

Robots manipulating in domestic environments generally need to interact with articulated objects, such as doors, drawers, laptops and swivel chairs. The rigid bodies that make up these objects are connected by a revolute pair or a prismatic pair. Robots are expected to learn and understand the objects' articulated constraints with a simple interaction method. In this way, the autonomy of robot manipulation will be greatly improved in an environment with unstructured constraints. In this paper, a method is proposed to obtain the articulated objects' constraint model by learning from a one-shot continuous visual demonstration which contains multistep movements, and this enables human teacher to continuously demonstrate several tasks at once without manual segmentation. At the end of this paper, a six-degree-of-freedom robot uses the constraint model obtained by demonstration learning to achieve manipulation planning of various tasks based on the AG-CBiRRT algorithm.

This article presents a mechanical design structure for hydraulic actuators using the principle of series-elastic actuator, considering the restriction of mechanical structure, weight and size, as well as the requirement of high joint torques due to the large payload. An articulated leg was designed to incorporate with two of these elastic elements symmetrically for each joint. Further, for the hydraulic system with a position servo loop, a force control algorithm was particularly proposed and developed for joint torque tracking and Cartesian impedance control. Finally, a number of experiments were carried out on the leg platform, and the experimental results validated the effectiveness of the force control algorithm and the performance of the overall system.

Leg stiffness plays a critical role in legged robots’ speed regulation. However, the analytic solutions to the differential equations of the stance phase do not exist, of course not for the exact analytical solution of stiffness. In view of the challenge in dealing with every circumstance by numerical methods, which have been adopted to tabulate approximate answers, the “harmonic motion model” was used as approximation of the stance phase. However, the wide range leg sweep angles and small fluctuations of the “center of mass” in fast movement were overlooked. In this article, we raise a “triangle motion model” with uniform forward speed, symmetric movement, and straight-line center of mass trajectory. The characters are then shifted to a quadratic equation by Taylor expansion and obtain an approximate analytical solution. Both the numerical simulation and ADAMS-Matlab co-simulation of the control system show the accuracy of the triangle motion model method in predicting leg stiffness even in the ultra-high-speed case, and it is also adaptable to low-speed cases. The study illuminates the relationship between leg stiffness and speed, and the approximation model of the planar spring–mass system may serve as an analytical tool for leg stiffness estimation in high-speed locomotion.

Model-based force control for motion and force tracking faces significant challenges on real quadruped platforms due to the apparent model inaccuracies. In this paper, we present a multi-objective optimal torque control for hydraulic quadruped robots under significant model errors, such as non-modelable hydraulic components, linearization, disturbances, etc. More specifically, the centroidal dynamics are first modeled to project the dynamics of the floating-based whole-body behaviors to the centroidal frame. Model error compensation mechanisms are subsequently developed to track the reference motion of the CoM, torso, and foot-end trajectories, which are mapped into the joint space. Furthermore, a multi-objective optimal torque control scheme is formulated using quadratic programming (QP) to coordinate follow the reference motion and ground reaction forces simultaneously while satisfying all constraints. Finally, we present a series of simulations as well as experiments on a real hydraulic quadruped platform, EHbot. The results demonstrate that the proposed torque control scheme is robust to large model inaccuracies and improves the performance of the overall system.

The spatial topological relations are the foundation of robot operation planning under unstructured and cluttered scenes. Defining complex relations and dealing with incomplete point clouds from the surface of objects are the most difficult challenge in the spatial topological relation analysis. In this paper, we presented the classification of spatial topological relations by dividing the intersection space into six parts. In order to improve accuracy and reduce computing time, convex hulls are utilized to represent the boundary of objects and the spatial topological relations can be determined by the category of points in point clouds. We verified our method on the datasets. The result demonstrated that we have great improvement comparing with the previous method.

This paper proposes a simple attitude trajectory optimization method to enhance the walking balance of a large-size hexapod robot. To achieve balance motion control of a large-size hexapod robot on different outdoor terrains, we planned the balance attitude trajectories of the robot during walking and introduced how leg trajectories are generated based on the planned attitude trajectories. While planning the attitude trajectories, high order polynomial interpolation was employed with attitude fluctuation counteraction considered. Constraints that the planned attitude trajectories must satisfy during walking were well-considered. The trajectory of the swing leg was well designed with the terrain attitude considered to improve the environmental adaptability of the robot during the attitude adjustment process, and the trajectory of the support leg was automatically generated to satisfy the demand of the balance attitude trajectories planned. Comparative experiments of the real large-size hexapod robot walking on different terrains were carried out to validate the effectiveness and applicability of the attitude trajectory optimization method proposed, which demonstrated that, compared with the currently developed balance motion controllers, the attitude trajectory optimization method proposed can simplify the control system design and improve the walking balance of a hexapod robot.

Compared with traditional control methods, the advantage of CPG (Central Pattern Generator) network control is that it can significantly reduce the size of the control variable without losing the complexity of its motion mode output. Therefore, it has been widely used in the motion control of robots. To date, the research into CPG network has been polarized: one direction has focused on the function of CPG control rather than biological rationality, which leads to the poor functional adaptability of the control network and means that the control network can only be used under fixed conditions and cannot adapt to new control requirements. This is because, when there are new control requirements, it is difficult to develop a control network with poor biological rationality into a new, qualified network based on previous research; instead, it must be explored again from the basic link. The other direction has focused on the rationality of biology instead of the function of CPG control, which means that the form of the control network is only similar to a real neural network, without practical use. In this paper, we propose some physical characteristics (including axon resistance, capacitance, length and diameter, etc.) that can determine the corresponding parameters of the control model to combine the growth process and the function of the CPG control network. Universal gravitation is used to achieve the targeted guidance of axon growth, Brownian random motion is used to simulate the random turning of axon self-growth, and the signal of a single neuron is established by the Rall Cable Model that simplifies the axon membrane potential distribution. The transfer model, which makes the key parameters of the CPG control network—the delay time constant and the connection weight between the synapses—correspond to the axon length and axon diameter in the growth model and the growth and development of the neuron processes and control functions are combined. By coordinating the growth and development process and control function of neurons, we aim to realize the control function of the CPG network as much as possible under the conditions of biological reality. In this way, the complexity of the control model we develop will be close to that of a biological neural network, and the control network will have more control functions. Finally, the effectiveness of the established CPG self-growth control network is verified through the experiments of the simulation prototype and experimental prototype.

Three-dimensional reconstruction and semantic understandings have attracted extensive attention in recent years. However, current reconstruction techniques mainly target large-scale scenes, such as an indoor environment or automatic self-driving cars. There are few studies on small-scale and high-precision scene reconstruction for manipulator operation, which plays an essential role in the decision-making and intelligent control system. In this paper, a group of images captured from an eye-in-hand vision system carried on a robotic manipulator are segmented by deep learning and geometric features and create a semantic 3D reconstruction using a map stitching method. The results demonstrate that the quality of segmented images and the precision of semantic 3D reconstruction are effectively improved by our method.

A growing algorithm of neural network based on axon growth is developed in this article. At the beginning, the Newtonian gravitation and the Brownian motion are employed to establish an axon growth model, and the constraint function of the neural axon growth is derived according to the gravitational coefficient and the Brown coefficient. And then, the growing algorithm of neural network is obtained on the basis of the output function of neural network. Simulation experiments show that the developed algorithm can simulate the process that different neurons connect each other by their axon growth to establish neural network. And at the end, the developed algorithm is applied to the rhythmic motion control of a quadruped robot to verify the effectiveness in walk gait and in trot gait, respectively.

To secure the data accuracy while reducing development cost of robot, in this article, we conducted fusion estimation of the information measured by strapdown inertial navigation system and the information solved by forward kinematics using extended Kalman filter and regarding the motion parameter error was proposed as state variable. On this basis, state equation and detecting equation can be established. In addition, this article made innovative attempt to compensate the optimal estimation of motion parameter error using feedback correction method, and finally obtained stable and accurate velocity estimation of robot. Being unrestricted by the number of robot’s leg, this method is suitable for both static gait and dynamic gait, and can overcome the impact from leg slipping. As for the verification of this method, in this article, a quadruped robot was employed for motion simulation and experimental verification, and results verified the correctness and feasibility of the proposed method.

Accurate acoustic source localization at a low sampling rate (less than 10 kHz) is still a challenging problem for small portable systems, especially for a multitasking micro-embedded system. A modification of the generalized cross-correlation (GCC) method with the up-sampling (US) theory is proposed and defined as the US-GCC method, which can improve the accuracy of the time delay of arrival (TDOA) and source location at a low sampling rate. In this work, through the US operation, an input signal with a certain sampling rate can be converted into another signal with a higher frequency. Furthermore, the optimal interpolation factor for the US operation is derived according to localization computation time and the standard deviation (SD) of target location estimations. On the one hand, simulation results show that absolute errors of the source locations based on the US-GCC method with an interpolation factor of 15 are approximately from 1/15- to 1/12-times those based on the GCC method, when the initial same sampling rates of both methods are 8 kHz. On the other hand, a simple and small portable passive acoustic source localization platform composed of a five-element cross microphone array has been designed and set up in this paper. The experiments on the established platform, which accurately locates a three-dimensional (3D) near-field target at a low sampling rate demonstrate that the proposed method is workable.

Active power-assist exoskeletons are becoming more prospective than follow-up types, especially for elderly and handicapped motion auxiliary. The exoskeleton is required not only to withstand load but also to actively share the weight of a human body. Active power-assist lower limb is designed to meet this expectation. The definition of “active power-assist” was suggested in this article. A unique man–machine motion mapping was derived based on the configuration matching, wherein the exoskeleton obtains the wearer’s motion data and parse out the corresponding intention. Man–machine coupling mechanisms were exquisitely configured, which rationalize the degrees of freedom of the man–machine system and facilitate force transmission for active assistant. The dynamic knees and hip joints with the integrated force servo unit were designed, which is the key to realize soft contact and cooperative movement. The prototype was developed, and three basic functions (human movement perception, force transmission, and movement cooperation) were preliminarily verified in a single leg swinging experiment. The effect of follow-up mode and active power-assist mode were quantitatively analyzed in marches-on-the-spot experiment. A 24.6% proportional reduction of the wearer foot force and smooth man–machine coordination in field experiments has demonstrated the feasibility of this structure design of active power-assist lower limb.

In biped walking, dynamic balance ability is an important evaluation index. Zero-moment-point-based trajectory control is a common method for biped dynamic walking, but it requires complex control mechanisms that limit its applications. With the help of passive dynamics, the biped walking based on the inverted pendulum model can achieve dynamic walking in a simple way; however, it has no stopping ability, which is necessary for practical use. To solve this problem, this article proposes a footed inverted pendulum model and develops a simple three-part decomposition control algorithm for controlling biped dynamic walking based on the model. In the control algorithm, the biped walking is decomposed into three separate control parts: body posture, body height, and body velocity. Body posture is controlled by the stance hip, body height is controlled by the stance knee, and body velocity is controlled by the stance ankle and swing foot placement simultaneously. A simulation is presented to analyze the foot’s effect on the inverted pendulum model. Two hardware experiments exploring velocity control and balance are described, with the results showing that the biped can achieve dynamic walking with stopping ability by using the simple control algorithm based on the footed inverted pendulum model.

Exoskeleton robots demonstrate promise in their application in assisting or enhancing human physical capacity. Joint muscular torques (JMT) reflect human effort, which can be applied on an exoskeleton robot to realize an active power-assist function. The estimation of human JMT with a wearable exoskeleton is challenging. This paper proposed a novel human lower limb JMT estimation method based on the inverse dynamics of the human body. The method has two main parts: the inverse dynamic approach (IDA) and the sensing system. We solve the inverse dynamics of each human leg separately to shorten the serial chain and reduce computational complexity, and divide the JMT into the mass-induced one and the foot-contact-force (FCF)-induced one to avoid switching the dynamic equation due to different contact states of the feet. An exoskeleton embedded sensing system is designed to obtain the user’s motion data and FCF required by the IDA by mapping motion information from the exoskeleton to the human body. Compared with the popular electromyography (EMG) and wearable sensor based solutions, electrodes, sensors, and complex wiring on the human body are eliminated to improve wearing convenience. A comparison experiment shows that this method produces close output to a motion analysis system with different subjects in different motion.

High-speed legged locomotion pushes the limits of the most challenging problems of design and development of the mechanism, also the control and the perception method. The cheetah is an existence proof of concept of what we imitate for high-speed running, and provides us lots of inspiration on design. In this paper, a new model of a cheetah-like robot is developed using anatomical analysis and design. Inspired by a biological neural mechanism, we propose a novel control method for controlling the muscles' flexion and extension, and simulations demonstrate good biological properties and leg's trajectory. Next, a cheetah robot prototype is designed and assembled with pneumatic muscles, a musculoskeletal structure, an antagonistic muscle arrangement and a J-type cushioning foot. Finally, experiments of the robot legs swing and kick ground tests demonstrate its natural manner and validate the design of the robot. In the future, we will test the bounding behaviour of a real legged system.