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In nature, gliding birds frequently execute intricate flight maneuvers such as aerial somersaults, perched landings, and swift descents, enabling them to navigate obstacles or hunt prey. It is evident that birds rely on different wing–tail configurations to accomplish a wide range of aerial maneuvers. For traditional fixed‐wing unmanned aerial vehicles (UAVs), pitch control primarily comes from the tail's elevators, while adjusting flight lift and drag involves deploying wing flaps. Although these designs ensure reliable flight, they compromise the drones’ maneuverability to maintain longitudinal stability. Therefore, the study introduces a biomimetic morphing wing UAV, and presents a pitch control strategy that simultaneously engages morphing wings, ailerons, and tail elevators. The pull‐up maneuver tests indicate that the proposed control method results in a pitch rate that is approximately 2.5 times greater than when using only the elevator control. A closed‐loop control system for the drone is also established. The closed‐loop flight experiment, which tracks a 45° pitch angle, demonstrates the effectiveness of the proposed coupled control method in adjusting the flight attitude. In addition, during cruising, the UAV employs three configurations, straight wing, forward‐swept wing, and back‐swept wing, to cater to different mission objectives and augment its flight capabilities.

Currently, cables are manually installed in aircraft manufacturing scenarios. The utilization of robots in cable assemblies can enhance efficiency and ensure quality, presenting great promise for the future. However, the assembly process must control the shape of the cables, which is a significant challenge for robots. On the one hand, cables have high degrees of freedom in space, making accurate modeling of cable dynamics for robotic arm manipulation difficult; on the other hand, cable deformation has uncertainty, and the applied forces cause simultaneous deformation and motion, which is difficult to control. To address these problems, this study proposes a cable‐shape control method based on graph neural networks and online visual shape‐servoing. The method first approximates cable dynamics with a graph neural network. Then, in practice, the learnt model is used alongside visual‐based shape‐servoing to generate optimal robotic arm movements, controlling the cable to attain the desired shape. In the experiments, precise control of three different cable types is realized, and an example of a completed cable assembly is shown.

Enabling a robot to learn skills from a human and adapt to different task scenarios will enable the use of robots in manufacturing to improve efficiency. Movement Primitives (MPs) are prominent tools for encoding skills. This paper investigates how to learn MPs from a small number of human demonstrations and adapt to different task constraints, including waypoints, joint limits, virtual walls, and obstacles. Probabilistic Movement Primitives (ProMPs) model movements with distributions, thus providing the robot with additional freedom for task execution. We provide the robot with three modes to move, with only one human demonstration required for each mode. We propose an improved via-point generalization method to generalize smooth trajectories with encoded ProMPs. In addition, we present an effective task-constrained optimization method that incorporates all task constraints analytically into a probabilistic framework. We separate ProMPs as Gaussians at each timestep and minimize Kullback–Leibler (KL) divergence, with a gradient ascent–descent algorithm performed to obtain optimized ProMPs. Given optimized ProMPs, we outline a unified robot movement adaptation method for extending from a single obstacle to multiple obstacles. We validated our approach with a 7-DOF Xarm robot using a series of movement adaptation experiments.

A mobile manipulator is capable of traversing a vast area while performing manipulation tasks in confined spaces. However, the high degree of freedom presents a challenge for path planning. In this paper, a hybrid sampling-based path planning method is proposed for mobile manipulators performing pick and place tasks in confined spaces. This method employs a random sampling approach, yet differs from the traditional RRT method. Firstly, a sampling-based configuration generation method for mobile manipulators is proposed, with the objective of generating a valid, collision-free configuration with the end-effector at the desired pose. A path for the end-effector corresponding to the goal configuration is then planned using the RRT method. Secondly, an area-restricted approach that samples in the vicinity of the previous configuration is introduced to generate the next valid configuration. Subsequently, a cost computation rule is devised to identify the optimal subsequent configuration utilizing the trajectory of the end-effector as a guiding principle. Finally, the obtained path is smoothed. Simulations demonstrate that the proposed hybrid sample-based method is an effective solution to the path planning problem for mobile manipulators performing pick and place tasks in narrow spaces.

This article introduces a novel perspective on designing a stepping controller for bipedal robots. Typically, designing a state-feedback controller to stabilize a bipedal robot to a periodic orbit of step-to-step (S2S) dynamics based on a reduced-order model (ROM) can achieve stable walking. However, the model discrepancies between the ROM and the full-order dynamic system are often ignored. We introduce the latest results from behavioral systems theory by directly constructing a robust stepping controller using input-state data collected during flat-ground walking with a nominal controller in the simulation. The model uncertainty discrepancies are equivalently represented as bounded noise and over-approximated by bounded energy ellipsoids. We conducted extensive walking experiments in a simulation on a 22-degrees-of-freedom small humanoid robot, verifying that it demonstrates superior robustness in handling uncertain loads, various sloped terrains, and push recovery compared to the nominal S2S controller.

Lyophyllum decastes is a commercially cultivated rare edible mushroom with high dietary and medicinal value. The mycelium physiological maturation was an important factor to the yield and quality of mushrooms obtained. However, it was impossible to obtain discriminative characteristics represented the maturity level of the mycelia from morphological features. In this article, we screened a candidate metabolite for evaluating the mycelium physiological maturation by metabolomic and transcriptomic analysis during mycelial vegetative growth stages of L. decastes. The results showed that mycelial vegetative growth 55 d had reached to physiological maturation according to the yield and quality of L. decastes, which the single bottle yield (307 g/bottle), the contents of flavonoids (2.94 mg/g dry weight), and 5 flavor amino acids (glutamate 177.90 μg/g, aspartate 95.74 μg/g, phenylalanine 31.68 μg/g, tyrosine 13.79 μg/g, and alanine 10.99 μg/g) were the highest. In addition, the metabolomic and transcriptomic analysis results showed that biosynthesis of secondary metabolites and amino acid had significant different during mycelial vegetative growth stages, and glutamine may be a candidate indicator to evaluate the mycelial physiological maturation. These findings contribute to improve our knowledge of mycelium physiological maturation and it will be used to increase mushroom yield and quality.

This paper presents a complete planner and controller scheme to achieve balance and walking for a biped robot, which does not need to distinguish the robot’s dynamic model parameters. The high-level planner utilizes model predictive control to optimize both the foothold location and step duration based on the Divergent Component of Motion (DCM) model to enhance the robustness of generated gaits. For low-level control, we use quadratic programming (QP) to optimize the contact force distribution under the contact constraints to achieve the virtual wrench exerted on the base of the robot. Then, the joint torques sent to the robot are derived from three parts: first, the torques mapped from the contact force; second, the swing leg tracking; and third, the stance foot stabilization. The simulation and experiment on BRUCE, a miniature bipedal robot from Westwood Robotics (Los Angeles, CA, USA), testify to the performance of the control scheme, including push recovery, Center of Mass (CoM) tracking, and omnidirectional walking.

In this study, the conventional virtual chain method is refined and a 4‐degrees‐of‐freedom (DOF) parallel bipedal mechanism (PBM) for walking robots is presented. The proposed design has the advantages of low inertia, high load‐to‐weight ratio, and impact resistance. First, to solve the redundant motion‐transmission problem, a refined virtual chain method is proposed for the type synthesis of the 4‐DOF parallel thigh mechanism. Second, the relationship equation for each actuated wrench component in a reasonable actuated joint arrangement is derived, and the number and location of the actuated joints are determined. Third, by optimizing the design of the hip joint as a 2‐DOF counter‐centered five‐link spherical mechanism with different rotation centers, passive knee and ankle joints are designed based on the bionic principle. This design is performed to make the PBM isotropic and minimize inertia. The proposed PBM can withstand heavy loads and impacts owing to its humanoid crossed four‐link knee joint mechanism and compliant calf mechanism. Finally, the prototype is processed, and the rationality of the design and excellent performance of the PBM design are verified by dynamic and static simulations and gait experiments.

Three dimensional (3D) robot measurement systems, with binocular planar structured light cameras (3D cameras) installed at the terminal flange of robots, are widely used to measure complete workpieces by stitching point clouds obtained from various sampled poses. To ensure accurate stitching, hand–eye calibration is necessary. However, 3D cameras often demonstrate low precision in measuring jumping edges and vertices, resulting in challenges when selecting appropriate calibrators and calibration methods for hand–eye calibration. We propose a simple and high‐precision hand–eye calibration method for 3D robot measurement systems. Initially, a single sphere is utilized as the calibrator, and its poses are observed using specific robotic motions. Subsequently, the hand–eye calibration problem is formulated as a well‐known equation, AX=XB, relying solely on the high‐precision relative pose of the robot. Finally, a novel simultaneous solution for AX=XB and the corresponding closed‐form initial solution are introduced. Simulations and experiments confirm that the proposed method exhibits high noise resistance and achieves high‐precision calibration, even with a limited calibration data quantity. Compared with traditional methods, the fitting error of the proposed method can be reduced from over 0.9 mm to less than 0.6 mm.

In the wild, gliding birds dodge obstacles or predators by folding and twisting their wings swiftly to perform a rapid roll. Accordingly, the authors strive to explore the feasibility of improving the roll rate of drones through this bird‐inspired morphing method, by using the asymmetric sweepback of wings to simulate the contraction of birds’ wings and the deflection of the aileron to imitate wing torsion. Moreover, the effects of wing morphing on the centroid, inertia matrix, and aerodynamic characteristics of the drone are explored herein, and a nonlinear dynamic model is established. Furthermore, a novel cooperative strategy that combines wing morphing with aileron deflection for roll control is introduced, and a flight controller based on this cooperative strategy is developed. Finally, the superiority of the cooperative strategy and the accuracy of the dynamic modeling have been validated in the outdoor flights of the morphing wing drone.

In the human–robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

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.

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.

The current morphing technologies are mostly regarded as auxiliary tools, providing additional control torques to enhance the flight maneuverability of unmanned aerial vehicles (UAVs), and they cannot exist independently of the traditional control surfaces. In this paper, we propose a tandem-wing micro aerial vehicle (MAV) with multiple variable-sweep wings, which can reduce the additional inertia forces and moments and weaken the dynamic coupling between longitudinal and lateral motion while the MAV morphs symmetrically for pitch control or asymmetrically for roll control, thereby flying without the traditional aileron and elevator. First, load experiments were conducted on the MAV to verify the structural strength of the multiple variable sweep wings, and the control moments caused by the morphing of the MAV were presented through numerical simulations. Then, the effects caused by symmetric and asymmetric morphing were investigated via dynamic response simulations based on the Kane dynamic model of the MAV, and the generated additional inertia forces and moments were also analyzed during morphing. Finally, dynamic response experiments and open-loop flight experiments were conducted. The experimental results demonstrated that the morphing mode in this study could weaken the coupling between the longitudinal and lateral dynamics and that it was feasible for attitude control without the traditional aileron and elevator while flying.

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.

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.

Turbojet engines have a slow response and require mechanical assistance to perform vertical take-offs and landings. In this paper, we propose a vertical take-off and landing (VTOL) aircraft that contains one-dimensional thrust vectoring nozzle modules to solve the problem of dependence on the response of the turbojet. The turbojet engine is placed horizontally in the module, the rotational speeds are fixed, and the force along the turbojet axis remains constant as the nozzle rotates from the horizontal to the vertical position. The aircraft is composed of four modules, whose position and attitude control are realized by using the thrust difference caused by the different angles of the four vector nozzles. The modules no longer rely on the response speed of the turbojet engine, and controlling the angles of four nozzles makes it easier to operate the aircraft. The horizontal placement of the turbojet can reduce the thickness of the aircraft fuselage and decrease air resistance. The process of moving the nozzle from a horizontal to a vertical position prevents the engine from blowing directly on the ground and, thus, reduces the ground effect.

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.

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.

This paper addresses the problem of learning from demonstration (LfD) and subsequent robot safety control in an unstructured dynamic environment different from the demonstrations. Generally, LfD has been successfully exploited for task programming, but the existing methods have not solved the problem of allowing the entire arm to avoid obstacles while satisfying the task motion constraints (e.g., the robotic arm approaching the target object while avoiding obstacles moving within the environment). To achieve this, we present an incremental LfD approach that combines a task-parameterized probabilistic model and the robot security domain to control a robot's behavior during task execution. Specifically, we propose a safety-oriented and task-oriented control strategy for redundant manipulators that makes full use of the motion redundancy of the manipulator and the space with no task restraints to satisfy the task constraints for human-robot coexistence. We then demonstrate the effectiveness of the proposed approach through a series of pick-and-pour experiments performed by a manipulator with 7 degree of freedom in a dynamic environment, where the robot must both avoid obstacles and satisfactorily complete the learned task with constraints.

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.

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.

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.

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.

Accurate position control of pneumatic artificial muscle actuated systems has always been difficult due to their inherent nonlinear hysteresis characteristics. This article designs a bio-inspired semi-active robotic joint that is synergistically driven by a pneumatic artificial muscle and an extension spring by imitating the mechanism of energy storage and return in animals and then studies its position tracking control with hysteresis compensation. To simplify the derivation of the inverse hysteresis model which functions as hysteresis compensator, a novel approach termed as direct inverse hysteresis modeling is adopted. With this method, the inversion of the asymmetric angle–pressure hysteresis of the semi-active joint is modeled directly from experimental measurements by a modified Prandtl–Ishlinskii model. On the basis, a closed-loop position tracking controller composed of forward hysteresis compensation and conventional proportional–integral–derivative control is designed for the joint. Experimental results indicate that the proposed controller can track the reference position signals with high accuracy.

Rescue robots that can penetrate into narrow gaps in the rubble, create and maintain spaces for rescuers after earthquakes are urgently needed in search and rescue missions. This paper proposes two novel rescue robots, including a cutting robot and a jack robot, which are used to cut obstacles and jack up heavy debris in hazardous locations and in narrow spaces, where rescuers cannot work or approach. Firstly, a multi-linked tracked, or snake-like rescue robot platform is designed, which is composed of modular crawler vehicles connected by active and passive joints with three degrees of freedom. This rescue robot platform has high mobility on irregular terrains and the ability to move into narrow collapsed structures. Then, portable rescue tools including an electric cutter and a novel electric screw jack are designed. In order to perform operations to the tools, manipulators with multi degrees of freedom are also developed. Consequently, the cutting robot and jack robot are realized by equipping the corresponding rescue tool and manipulator in the rescue robot platform. Finally, field tests of the two rescue robots operating on different terrains and performing rescue missions are carried out in the national training base for urban search and rescue. The results validated the mobility, cutting and lift-up capacity of the two rescue robots. The experiments show that they might play some role in rescue operations.

The HIT load-carrying exoskeleton is designed for enhancing the operator’s load-carrying capability. Human walking has the characteristics of diversity and randomness. It requires that the sensing system of exoskeleton could detect and identify the random motion of human body. In this article, a human–machine force interaction designing architecture is proposed for HIT load-carrying exoskeleton. The human–machine interactive forces at the kinematic terminals (the back and feet) are detected for the human movement identification. Four basic body modalities during the movement process are proposed, which could be automatically identified by the sensing system. The kinematic models for the swing leg and supporting leg are established, respectively. A positional deviation feedback control method based on the interaction force detection is designed. The experiments show that the human–machine interaction force detection at the back and feet and the identification of different body modalities and movement intention are feasible. The interactive forces on the back are far less than the payload, indicating that the exoskeleton has good power-assisted effect.

This article presents a summary of applications of chaos and fractals in robotics. Firstly, basic concepts of deterministic chaos and fractals are discussed. Then, fundamental tools of chaos theory used for identifying and quantifying chaotic dynamics will be shared. Principal applications of chaos and fractal structures in robotics research, such as chaotic mobile robots, chaotic behaviour exhibited by mobile robots interacting with the environment, chaotic optimization algorithms, chaotic dynamics in bipedal locomotion and fractal mechanisms in modular robots will be presented. A brief survey is reported and an analysis of the reviewed publications is also presented.

To achieve high walking stability for a passive dynamic walking robot is not easy. In this article, we aim to investigate whether the walking performance for a passive dynamic walking robot can be improved by just simply changing the swing ankle angle before impact. To validate this idea, a passive bipedal walking model with two straight legs, two flat feet, a hip joint, and two ankle joints was built in this study. The walking dynamics that contains double stance phase was derived. By numerical simulation of the walking in MATLAB, we found that the walking performance can be adjusted effectively by only simply changing the swing ankle angle before impact. A bigger swing ankle angle in a reasonable range will lead to a higher walking stability and a lower initial walking speed of the next step. A bigger swing ankle angle before impact leads to a bigger amount of energy lost during impact for the quasi-passive dynamic walking robot which will influence the walking stability of the next step.

The concept of “passive dynamic walking robot” refers to the robot that can walk down a shallow slope stably without any actuation and control which shows a limit cycle during walking. By adding actuation at some joints, the passive dynamic walking robot can walk stably on level ground and exhibit more versatile gaits than fully passive robot, namely, the “limit cycle walker.” In this article, we present the mechanical structures and control system design for a passive dynamic walking robot with series elastic actuators at hip joint and ankle joints. We built a walking model that consisted of an upper body, knee joints, and flat feet and derived its walking dynamics that involve double stance phases in a walking cycle based on virtual power principle. The instant just before impact was chosen as the start of one step to reduce the number of independent state variables. A numerical simulation was implemented by using MATLAB, in which the proposed passive dynamic walking model could walk stably down a shallow slope, which proves that the derived walking dynamics are correct. A physical passive robot prototype was built finally, and the experiment results show that by only simple control scheme the passive dynamic robot could walk stably on level ground.

Walking on rough terrains still remains a challenge that needs to be addressed for biped robots because the unevenness on the ground can easily disrupt the walking stability. This paper proposes a novel foot system with passively adjustable stiffness for biped robots which is adaptable to small-sized bumps on the ground. The robotic foot is developed by attaching eight pneumatic variable stiffness units to the sole separately and symmetrically. Each variable stiffness unit mainly consists of a pneumatic bladder and a mechanical reversing valve. When walking on rough ground, the pneumatic bladders in contact with bumps are compressed, and the corresponding reversing valves are triggered to expel out the air, enabling the pneumatic bladders to adapt to the bumps with low stiffness; while the other pneumatic bladders remain rigid and maintain stable contact with the ground, providing support to the biped robot. The performances of the proposed foot system, including the variable stiffness mechanism, the adaptability on the bumps of different heights, and the application on a biped robot prototype are demonstrated by various experiments.

The under-actuated foot rotation that the heel of the stance leg lifts off the ground and the body rotates around the stance toe is an important feature in human walking. However, it is absent in the realized walking gait for the majority of biped robots because of the difficulty and complexity in the control it brings about. In this paper, a hybrid control approach aiming to integrate the main characteristics of human walking into a simulated seven-link biped robot is presented and then verified with simulations. The bipedal robotic gait includes a fully actuated single support phase with the stance heel supporting the body, an under-actuated single support phase, with the stance toe supporting the body, and an instantaneous double support phase when the two legs exchange their roles. The walking controller combines virtual force control and foot placement control, which are applied to the stance leg and the swing leg, respectively. The virtual force control assumes that there is a virtual force which can generate the desired torso motion on the center of mass of the torso link, and then the virtual force is applied through the real torques on each actuated joint of the stance leg to create the same effect that the virtual force would have created. The foot placement control uses a path tracking controller to follow the predefined trajectory of the swing foot when walking forward. The trajectories of the torso and the swing foot are generated based on the cart-cable model. Co-simulations in Adams and MATLAB show that the desired gait is achieved with a biped robot under the action of the proposed method.