Search

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.

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.

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.

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.

Broccoli is becoming increasingly popular among consumers owing to its nutritional value and rich bioactive compounds, such glucosinolates (GSLs) and hydrolysis products, which are secondary metabolites for plant defense, cancer prevention, and higher antioxidant activity for humans. In this study, 40 μmol/L methyl jasmonate (MeJA) was sprayed onto broccoli from budding until harvest. The harvested broccoli florets, stem, and leaves were used to measure the contents of GSLs, sulforaphane, total phenolics, and flavonoids, as well as myrosinase activity, antioxidant activity, and gene expression involved in GSL biosynthesis. The overall results revealed that GSL biosynthesis and sulforaphane accumulation were most likely induced by exogenous MeJA treatment by upregulating the expression of CYP83A1, SUR1, UGT74B1, and SOT18 genes. Exogenous MeJA treatment more remarkably contributed to the increased GSL biosynthesis in broccoli cultivars with low-level GSL content (Yanxiu) than that with high-level GSLs (Xianglv No.3). Moreover, MeJA treatment had a more remarkable increasing effect in broccoli florets than stem and leaves. Interestingly, total flavonoid content substantially increased in broccoli florets after MeJA treatment, but total phenolics did not. Similarly, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging capacity, trolox-equivalent antioxidant capacity (ABTS), and ferric-reducing antioxidant power (FRAP) were higher in broccoli floret after MeJA treatment. In conclusion, MeJA mediated bioactive compound metabolism, had positive effects on GSL biosynthesis, sulforaphane, and flavonoids accumulation, and showed positive correlation on inducing higher antioxidant activities in broccoli floret. Hence, preharvest supplementation with 40 μM MeJA could be a good way to improve the nutritional value of broccoli florets.

The effects of storage conditions and cooking methods on chlorophyll, glucosinolate (GSL), and sulforaphane content in broccoli florets were investigated in this study. For the storage experiment, fresh broccoli florets were stored for 2, 4, and 6 days at 10, 4, and 0 °C with or without 1-methylcyclopropene (1-MCP) treatment. For the cooking experiment, fresh broccoli florets were cooked for 1, 3, 5, 7, and 9 min under three cooking methods, namely, steaming, microwaving, and boiling. Results showed that the contents of chlorophyll, aliphatic GSL, indole GSL, and sulforaphane in broccoli florets of two cultivars decreased with prolonged storage time. The retained contents of chlorophyll, GSLs, and sulforaphane under 0 °C storage condition were significantly higher than those under 10 °C storage condition after 6 days of storage. The sulforaphane content was increased by 1-MCP treatment but differed among varieties. The cooking experiment showed that aliphatic GSL content decreased with increased cooking time under three cooking methods, and indole GSL and sulforaphane contents had a fluctuating trend with increasing cooking time after steaming and microwaving. Sulforaphane content increased by 17.15–50.16% relative to that in fresh broccoli florets and was considerably affected by cooking time. The highest level of sulforaphane content was retained for 7 min during steaming or 5 min during microwaving. Therefore, the combination of 1-MCP treatment and 0 °C storage condition had the best performance in preserving chlorophyll, GSLs, and sulforaphane. Moreover, steaming for 7 min or microwaving for 5 min is a more effective method for preserving the quality and increasing the sulforaphane content of broccoli florets than boiling.