4-Coumarate-CoA ligase (4-CL) enhances flavonoid accumulation, lignin synthesis, and fruiting body formation in Ganoderma lucidum.

It is now understood that 4-Coumarate-CoA ligases (4-CL) are pivotal in bridging the phenylpropanoid metabolic pathway and the lignin biosynthesis pathway in plants. However, limited information on 4-CL genes and their functions in fungi is available. In this study, we cloned the 4-CL gene (Gl21040) from Ganoderma lucidum, which spans 2178 bp and consists of 10 exons and 9 introns. We also developed RNA interference and overexpression vectors for Gl21040 to investigate its roles in G. lucidum. Our findings indicated that in the Gl21040 interference transformants, 4-CL enzyme activities decreased by 31 %-57 %, flavonoids contents decreased by 10 %-22 %, lignin contents decreased by 20 %-36 % compared to the wild-type (WT) strain. Conversely, in the Gl21040 overexpression transformants, 4-CL enzyme activity increased by 108 %-143 %, flavonoids contents increased by 8 %-37 %, lignin contents improved by 15 %-17 % compared to the WT strain. Furthermore, primordia formation was delayed by approximately 10 days in the Gl21040-interferenced transformants but occurred 3 days earlier in the Gl21040-overexpressed transformants compared to the WT strain. These results underscored the involvement of the Gl21040 gene in flavonoid synthesis, lignin synthesis, and fruiting body formation in G. lucidum.

Joint Calibration Method for Robot Measurement Systems.

Robot measurement systems with a binocular planar structured light camera (3D camera) installed on a robot end-effector are often used to measure workpieces' shapes and positions. However, the measurement accuracy is jointly influenced by the robot kinematics, camera-to-robot installation, and 3D camera measurement errors. Incomplete calibration of these errors can result in inaccurate measurements. This paper proposes a joint calibration method considering these three error types to achieve overall calibration. In this method, error models of the robot kinematics and camera-to-robot installation are formulated using Lie algebra. Then, a pillow error model is proposed for the 3D camera based on its error distribution and measurement principle. These error models are combined to construct a joint model based on homogeneous transformation. Finally, the calibration problem is transformed into a stepwise optimization problem that minimizes the sum of the relative position error between the calibrator and robot, and analytical solutions for the calibration parameters are derived. Simulation and experiment results demonstrate that the joint calibration method effectively improves the measurement accuracy, reducing the mean positioning error from over 2.5228 mm to 0.2629 mm and the mean distance error from over 0.1488 mm to 0.1232 mm.

A Hierarchical Motion Planning Method for Mobile Manipulator.

This paper focuses on motion planning for mobile manipulators, which includes planning for both the mobile base and the manipulator. A hierarchical motion planner is proposed that allows the manipulator to change its configuration autonomously in real time as needed. The planner has two levels: global planning for the mobile base in two dimensions and local planning for both the mobile base and the manipulator in three dimensions. The planner first generates a path for the mobile base using an optimized A* algorithm. As the mobile base moves along the path with the manipulator configuration unchanged, potential collisions between the manipulator and the environment are checked using the environment data obtained from the on-board sensors. If the current manipulator configuration is in a potential collision, a new manipulator configuration is searched. A sampling-based heuristic algorithm is used to effectively find a collision-free configuration for the manipulator. The experimental results in simulation environments proved that our heuristic sampling-based algorithm outperforms the conservative random sampling-based method in terms of computation time, percentage of successful attempts, and the quality of the generated configuration. Compared with traditional methods, our motion planning method could deal with 3D obstacles, avoid large memory requirements, and does not require a long time to generate a global plan.

Symmetrical Efficient Gait Planning Based on Constrained Direct Collocation.

Biped locomotion provides more mobility and effectiveness compared with other methods. Animals have evolved efficient walking patterns that are pursued by biped robot researchers. Current researchers have observed that symmetry is a critical criterion to achieve efficient natural walking and usually realize symmetrical gait patterns through morphological characteristics using simplified dynamic models or artificial priors of the center of mass (CoM). However, few considerations of symmetry and energy consumption are introduced at the joint level, resulting in inefficient leg motion. In this paper, we propose a full-order biped gait planner in which the symmetry requirement, energy efficiency, and trajectory smoothness can all be involved at the joint level, and CoM motion is automatically determined without any morphological prior. In order to achieve a symmetrical and efficient walking pattern, we first investigated the characteristic of a completely symmetrical gait, and a group of nearly linear slacked constraints was designed for three phases of planning. Then a Constrained Direct Collocation (DIRCON)-based full-order biped gait planner with a weighted cost function for energy consumption and trajectory smoothness is proposed. A dynamic simulation with our newly designed robot model was performed in CoppliaSim to test the planner. Physical comparison experiments on a real robot device finally validated the symmetry characteristic and energy efficiency of the generated gait. In addition, a detailed presentation of the real biped robot is also provided.

A Non-Flat Terrain Biped Gait Planner Based on DIRCON.

Various constraints exist in bipedal movement. Due to the natural ability of effectively handling constraints, trajectory optimization has become one of the mainstream methods in biped gait planning, especially when constraints become much more complex on non-flat terrain. In this paper, we propose a multi-modal biped gait planner based on DIRCON, which can generate different gaits for multiple, non-flat terrains. Firstly, a virtual knot is designed to model the state transitions when the swing foot contacts terrain and is inserted as the first knot of the target trajectory of the current support phase. Thus, a complete gait or multi-modal gaits sequence can be generated at one time. Then, slacked complementary constraints, which can avoid undesired trajectories, are elaborated to describe the coupling relationships between terrain information and bipedal motion for trajectory optimization based gait planning. The concrete form of the gait planner is also delivered. Finally, we verify the performance of the planner, as well as the structural design of our newly designed biped robot in CoppeliaSim through flat terrain walking, stairs terrain walking and quincuncial piles walking. The three experiments show that the gaits planned by the proposed planner can enable the robot to walk stably over non-flat terrains, even through simple PD control.

Transcriptome and metabolome analyses reveal transcription factors regulating ganoderic acid biosynthesis in Ganoderma lucidum development.

Ganoderma lucidum is an important medicinal fungus in Asian countries. Ganoderic acid (GA) is the major variety of bioactive and medicative components in G. lucidum. Biosynthesis of secondary metabolites is usually associated with cell differentiation and development. However, the mechanism underlying these phenomena remain unclear. Transcription factors play an essential regulatory role in the signal transduction pathway, owing to the fact that they represent the major link between signal transduction and expression of target genes. In the present study, we performed transcriptome and metabolome analyses to identify transcription factors involved in GA biosynthesis during development of G. lucidum. Transcriptome data revealed differentially expressed genes between mycelia and primordia, as well as between mycelia and the fruiting body. Results from gene ontology enrichment analysis and metabolome analyses suggested that GAs and flavonoids biosynthetic process significantly changed during fungal development. The analysis of predicted occurrences of DNA-binding domains revealed a set of 53 potential transcription factor families in G. lucidum. Notably, we found homeobox transcription factor and velvet family protein played important role in GA biosynthesis. Combined with previous studies, we provided a model diagram of transcription factors involved in GA biosynthesis during fruiting body formation. Collectively, these results are expected to enhance our understanding into the mechanisms underlying secondary metabolite biosynthesis and development in fungi.

Research on Theory and a Performance Analysis of an Innovative Rehabilitation Robot.

This paper presents an innovative application of a 6-DOF robot in the field of rehabilitation training. This robot operates in a parallel fashion for lower limb movement, which adopts a new structure that can help patients to carry out a variety of rehabilitation exercises. Traditional parallel robots, such as the Stewart robot, have the characteristics of strong bearing capacity. However, it is difficult to achieve high-speed, high-acceleration and long journey movement. This paper presents a new robot configuration that can address these problems. This paper also conducts an all-around characteristic analysis of this new parallel robot, including kinematics, dynamics and structure, to better study the robot and improve its performance. This paper optimizes an algorithm to make it more suitable for rehabilitation training. Finally, the performance improvements brought by optimization are verified by simulations.

A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations.

In manufacturing, traditional task pre-programming methods limit the efficiency of human-robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy.

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review.

As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object's features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

Analysis and control of biped robot with variable stiffness ankle joints.

BACKGROUND: Biped robot locomotion is an active topic of research, and the walking stability is one of the research objectives. OBJECTIVE: This paper discusses the variable stiffness ankle joint and the walking control of a biped robot. METHODS: A design is introduced that achieves the ankle joint variable stiffness by using a pneumatic unit. The actuation system of the biped robot is based on the hybrid use of electric and pneumatic. The locomotion control architecture has been proposed to exploit natural leg dynamics in order to improve the biped robot walking stability. We also present a dynamic simulation which matches the biped robot and experiments with the real biped robot. RESULTS: The simulation and experiments result that introducing the variable stiffness ankle joint and the controller achieve a significant improvement in foot-ground impact and walking stability of the biped robot. CONCLUSION: The biped robot with variable stiffness ankle joints has a better walking performance under the control method.

Robot complex motion learning based on unsupervised trajectory segmentation and movement primitives.

This paper presents a robot skill acquisition framework for learning and reproducing humanoid trajectories with complex forms. A new unsupervised segmentation method is proposed to detect motion units in the demonstrated kinematic data using the concept of key points. To find the consistent features of trajectories, a Hidden Semi-Markov Model (HSMM) is used to identify key points common to all the demonstrations. Generalizing the motion units is achieved via a Probability-based Movement Primitive (PbMP), which encapsulates multiple trajectories into one model. Such a framework can generate trajectories suitable for robot execution with arbitrary shape and complexity from a small number of demonstrations, which greatly expands the application scenarios of robot programming by demonstration. The automatic segmentation process does not rely on a priori knowledge or models for specific tasks, and the generalized trajectory retains more consistent features than those produced by other algorithms. We demonstrate the effectiveness of the proposed framework through simulations and experiments.

Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design.

As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input-multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F1 and F3, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.

Gait Symmetry Can Reduce Dependence on the Intact Limb during Walking with Constraint of Unilateral Metatarsophalangeal Joints.

Our previous research showed that healthy subjects exhibited asymmetrical walking gait when their unilateral metatarsophalangeal (MTP) joints were constrained. They relied more on their intact limb for the compensation of missing functions of their constrained limb, which may result in poor balance, increased metabolic cost, and higher risks of falls. We examined how subjects would respond if gait asymmetry in stance time was improved using a split-belt treadmill. Twelve healthy subjects were instructed to walk on a split-belt treadmill at comfortable and fast speeds with their unilateral MTP joints constrained. The walking trials were performed in two conditions, i.e., tied-belt walking (TBW, both belts moved at the same speed), and split-belt walking (SBW, the speed of the belt on the constrained leg was appropriately slower than that of the contralateral leg). The ground reaction forces (GRF) and electromyography (EMG) data during walking were collected. Results showed that the GRF and impulses on the constrained foot at fast speed SBW significantly increased, compared with TBW, and the activation of medial gastrocnemius and soleus of the intact lower limb decreased significantly, which suggest that improved symmetry in stance time may reduce dependence on the intact limb for humans with the constraint of unilateral MTP joints.

Design and Evaluation of a Wearable Powered Foot Orthosis with Metatarsophalangeal Joint.

The metatarsophalangeal (MTP) joints play critical roles in human locomotion. Functional restriction or loss of MTP joints will lead to lower walking speed, poorer walking balance, and more consumed metabolic energy cost compared with normal walking. However, existing foot orthoses are focused on maintaining the movement of the ankle joint, without assisting the MTP joints. In this paper, in order to improve the walking performance of people with lower limb impairments, a wearable powered foot orthosis (WPFO) which has actuated MTP joint is designed and constructed. Preliminary experiments on three nondisabled subjects demonstrated functionality and capabilities of the WPFO to provide correctly timed dorsiflexion and plantar flexion assistance at the MTP joint during walking. These results also suggest that the WPFO could offer promise in certain rehabilitation applications and clinical treatment.

A Force-Sensing System on Legs for Biomimetic Hexapod Robots Interacting with Unstructured Terrain.

The tiger beetle can maintain its stability by controlling the interaction force between its legs and an unstructured terrain while it runs. The biomimetic hexapod robot mimics a tiger beetle, and a comprehensive force sensing system combined with certain algorithms can provide force information that can help the robot understand the unstructured terrain that it interacts with. This study introduces a complicated leg force sensing system for a hexapod robot that is the same for all six legs. First, the layout and configuration of sensing system are designed according to the structure and sizes of legs. Second, the joint toque sensors, 3-DOF foot-end force sensor and force information processing module are designed, and the force sensor performance parameters are tested by simulations and experiments. Moreover, a force sensing system is implemented within the robot control architecture. Finally, the experimental evaluation of the leg force sensor system on the hexapod robot is discussed and the performance of the leg force sensor system is verified.

Position control of a single pneumatic artificial muscle with hysteresis compensation based on modified Prandtl-Ishlinskii model.

BACKGROUND: High-performance position control of pneumatic artificial muscles is limited by their inherent nonlinearity and hysteresis. OBJECTIVE: This study aims to model the length/pressure hysteresis of a single pneumatic artificial muscle and to realize its accurate position tracking control with forward hysteresis compensation. METHODS: The classical Prandtl-Ishlinskii model is widely used in hysteresis modelling and compensation. But it is only effective for symmetric hysteresis. Therefore, a modified Prandtl-Ishlinskii model is built to characterize the asymmetric length/pressure hysteresis of a single pneumatic artificial muscle, by replacing the classical play operators with two more flexible elementary operators to independently describe the ascending branch and descending branch of hysteresis loops. On the basis, a position tracking controller, which is composed of cascade forward hysteresis compensation and simple proportional pressure controller, is designed for the pneumatic artificial muscle. RESULTS: Experiment results show that the MPI model can reproduce the length/pressure hysteresis of the pneumatic artificial muscle, and the proposed controller for the pneumatic artificial muscle can track the reference position signals with high accuracy. CONCLUSION: By modelling the length/pressure hysteresis with the modified Prandtl-Ishlinskii model and using its inversion for compensation, precise position control of a single pneumatic artificial muscle is achieved.

Design and control of a pneumatic musculoskeletal biped robot.

BACKGROUND: Pneumatic artificial muscles are quite promising actuators for humanoid robots owing to their similar characteristics with human muscles. Moreover, biologically inspired musculoskeletal systems are particularly important for humanoid robots to perform versatile dynamic tasks. OBJECTIVE: This study aims to develop a pneumatic musculoskeletal biped robot, and its controller, to realize human-like walking. METHODS: According to the simplified musculoskeletal structure of human lower limbs, each leg of the biped robot is driven by nine muscles, including three pairs of monoarticular muscles which are arranged in the flexor-extensor form, as well as three biarticular muscles which span two joints. To lower cost, high-speed on/off solenoid valves rather than proportional valves are used to control the muscles. The joint trajectory tracking controller based on PID control method is designed to achieve the desired motion. Considering the complex characteristics of pneumatic artificial muscles, the control model is obtained through parameter identification experiments. RESULTS: Preliminary experimental results demonstrate that the biped robot is able to walk with this control strategy. CONCLUSION: The proposed musculoskeletal structure and control strategy are effective for the biped robot to achieve human-like walking.

Study of human walking patterns based on the parameter optimization of a passive dynamic walking robot.

BACKGROUND: The study of human walking patterns mainly focuses on how control affects walking because control schemes are considered to be dominant in human walking. OBJECTIVE: This study proposes that not only fine control schemes but also optimized body segment parameters are responsible for humans' low-energy walking. METHODS: A passive dynamic walker provides the possibility of analyzing the effect of parameters on walking efficiency because of its ability to walk without any control. Thus, a passive dynamic walking model with a relatively human-like structure was built, and a parameter optimization process based on the gait sensitivity norm was implemented to determine the optimal mechanical parameters by numerical simulation. RESULTS: The results were close to human body parameters, thus indicating that humans can walk under a passive pattern based on their body segment parameters. A quasi-passive walking prototype was built on the basis of the optimization results. Experiments showed that a passive robot with optimized parameters could walk on level ground with only a simple hip actuation. CONCLUSION: This result implies that humans can walk under a passive pattern based on their body segment parameters with only simple control strategy implying that humans can opt to walk instinctively under a passive pattern.

Design of a biped robot actuated by pneumatic artificial muscles.

High compliant legs are essential for the efficient versatile locomotion and shock absorbency of humans. This study proposes a biped robot actuated by pneumatic artificial muscles to mimic human locomotion. On the basis of the musculoskeletal architecture of human lower limbs, each leg of the biped robot is modeled as a system of three segments, namely, hip joint, knee joint, and ankle joint, and eleven muscles, including both monoarticular and biarticular muscles. Each rotational joint is driven by a pair of antagonistic muscles, enabling joint compliance to be tuned by operating the pressure inside the muscles. Biarticular muscles play an important role in transferring power between joints. Walking simulations verify that biarticular muscles contribute to joint compliance and can absorb impact energy when the robot makes an impact upon ground contact.