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Reasonable risk sharing is the key to the smooth implementation of infrastructure public-private partnership (PPP) projects and the optimization of benefit distribution among the participants. This study aims to explore the risk redistribution ratio between the government and the private sector under different degree of fairness concern.

Renegotiation is a mechanism to provide flexibility and make up for incompleteness of PPP contracts. However, the threshold value of risk redistribution ratio and negotiation cost are not explicitly considered in previous studies. In addition, these studies do not consider the influence of the fairness concern psychology on the negotiation process. To address these gaps, based on risk-income equilibrium analysis, this paper established the bargaining optimization model of PPP projects renegotiation considering the fairness concerns of the negotiating parties. Furthermore, this study analyzed the influence of fairness concern degree on negotiation thresholds, negotiation results, and negotiation incomes under three scenarios.

The results showed that excessive focus on the fairness of incomes may exclude the risk redistribution ratio that is most beneficial to project incomes from the negotiation threshold. Moreover, the increase in the fairness concerns of negotiating parties can reduce the negotiation success period, but the net income may not necessarily be improved.

The main contribution of this paper is to propose a new risk renegotiation methodology based on the risk-income equilibrium analysis, which is helpful to develop risk management strategies in the construction field. The research results can provide government with reference about renegotiation in decision making and provide theoretical support for the practice of PPP renegotiation.

This paper aims to present an underwater climbing robot for wiping off marine life from steel pipes (e.g. jackets of oil platforms). The self-adaption mechanism that consists of a passive roll joint and combined magnet adhesion units provides the robot with better mobility and stability.

Adhesion requirements are achieved by analyses of falling and slipping. The movement status on pipes is analyzed to design the passive roll joint. The optimized structure parameters of the combined magnet adhesion unit are achieved by simulations. An approximation method is established to simplify the simulations conditions, and the simulations are conducted in two steps to save time effectively.

The self-adaption mechanism has expected performance that the robot can travel on pipes in different directions with high mobility. Meanwhile, the robot can clean continuous region of underwater pipes’ surface of offshore platforms.

The proposed underwater robot is needed by offshore oil platforms as their jackets require to be cleaned periodically. Compared with traditional maintenance by divers, it is more efficient, economic and safety.

Due to the specific self-adaption mechanism, the robot has good mobility and stability in any directions on pipes with different diameters. The good performance of striping attachments from pipes makes the underwater robot be a novel solution to clean steel pipes.

Walking-aid exoskeletons can assist and protect effectively the group with lower limb muscle strength decline, workers, first responders and military personnel. However, there is almost no united control strategy that can effectively assist daily walking. This paper aims to propose a hybrid oscillators’ (HOs) model to adapt to irregular gait (IG) patterns (frequent alternation between walking and standing or rapid changing of walking speed, etc.) and generate compliant and no-delay assistive torque.

The proposed algorithm, HOs, combines adaptive oscillators (AOs) with phase oscillator through switching assistive mode depending on whether or not the AOs' predicting error of hip joint degree is exceeded our expectation. HOs can compensate for delay by predicting gait phase when in AOs mode. Several treadmill and free walking experiments are designed to test the adaptability and effectiveness of HOs model under IG.

The experimental results show that the assistive strategy based on the HOs is effective under IG patterns, and delay is compensated totally under quasiperiodic gait conditions where a smoother human–robot interaction (HRI) force and the reduction of HRI force peak are observed. Delay compensation is found very effective at improving the performance of the assistive exoskeleton.

A novel algorithm is proposed to improve the adaptability of a walking assist hip exoskeleton in daily walking as well as generate compliant, no-delay assistive torque when converging.

This paper aims to present a natural human–robot teleoperation system, which capitalizes on the latest advancements of monocular human pose estimation to simplify scenario requirements on heterogeneous robot arm teleoperation.

Several optimizations in the joint extraction process are carried on to better balance the performance of the pose estimation network. To bridge the gap between human joint pose in Cartesian space and heterogeneous robot joint angle pose in Radian space, a routinized mapping procedure is proposed.

The effectiveness of the developed methods on joint extraction is verified via qualitative and quantitative experiments. The teleoperation experiments on different robots validate the feasibility of the system controlling.

The proposed system provides an intuitive and efficient human–robot teleoperation method with low-cost devices. It also enhances the controllability and flexibility of robot arms by releasing human operator from motion constraints, paving a new way for effective robot teleoperation.

Most current lower extremity exoskeletons emphasize assistance for walking rather than stability. The purpose of this paper is to propose a rehabilitation gait based on the transfer of gravity center to improve the balance of exoskeleton rehabilitation training of the hemiplegic patients in the frontal plane, reducing the dependence on crutches/walking frames.

The real-time and predictable instability factors of human and exoskeleton system (HES) are analyzed. Inspired by the walking balance strategy of the blind, a rehabilitation gait based on the transfer of gravity center is proposed and studied by modeling and experimental test and is finally applied to the prototype – Zhejiang University lower extremity exoskeleton (ZJULEEX) – to verify its feasibility.

At least three real-time and predictable factors cause the instability of HES, and the factor of lateral tilt caused by gravity should be focused in the balance control of frontal plane. With the proposed gait, the hip height of stepping leg of HES does not reduce obviously even when the crutches do not work, which can improve the balance of HES.

However, the rehabilitation gait control needs to be more complete and intelligent to response to other types of perturbations to further improve the balance of HES. In addition, more clinical trials should be conducted to evaluate the effect of the proposed gait.

May bring happiness to the rehabilitation of patients with hemiplegia.

The rehabilitation gait based on the transfer of gravity center to improve the balance of HES is first proposed and applied to HES.

Lower extremity exoskeletons have drawn much attention recently due to their potential ability to help the stroke and spinal cord injury patients to regain the ability of walking. However, the balance of the human-exoskeleton system (HES) remains a big challenge. Usually, patients use crutches to keep balance when they wear exoskeleton. However, the balance depends greatly on the patient's balance ability and will be inevitably poor occasionally, which often causes the landing in advance of HES. The purpose of this paper is to propose a real-time stepping gait trajectory planning method based on the hip height variation of the swing leg to solve the problem.

The hip height of the swing leg was analyzed and measured. The simulation with MATLAB and the experimental test with the prototype of the proposed gait were conducted to verify its feasibility.

With the proposed method, HES can achieve successful step even when the balance kept by crutches is poor.

Instead of actively avoiding the poor balance due to the instability caused by gravity, the method just modifies the stepping gait passively to avoid the landing in advance when the poor balance appears. In addition, it may not work well when the balance is too poor. Moreover, the proposed gait is just used in the initial stage of rehabilitation training. Besides, the step length of the gait must be limited for comfort.

A real-time stepping gait trajectory planning method based on the hip height variation of the swing leg is first proposed and its feasibility to avoid the landing in advance when the balance kept by the crutches is poor has been preliminary verified.

Variable stiffness structure can significantly improve the interactive capabilities of grippers. Shape memory alloys have become a popular option for materials with variable stiffness structures. However, its variable stiffness range is limited by its stiffness in two phases. The purpose of this paper is to enhance the manipulation capabilities of tendon-driven flexible grippers by designing a wide-range variable stiffness structure.

Constitutive models of shape memory alloy and mechanical models are used to analyze the performance of the variable stiffness structure. A separated solution was used to combine the tendon-driven gripper and the variable stiffness structure. The feed-forward control algorithm is used to enhance the control stability of the variable stiffness structure.

The stiffness variable capability of the proposed variable stiffness structure is verified by experiments. The stability of the feedback control algorithm was verified by sinusoidal tracking experiments. The variable stiffness range of 8.41 times of the flexible gripper was tested experimentally. The interaction capability of the variable stiffness flexible gripper is verified by the object grasping experiments.

A new wide-range variable stiffness structure is proposed and validated. The new variable stiffness structure has a larger range of stiffness variation and better control stability. The new flexible structure can be applied to conventional grippers to help them gain stiffness variable capability and improve their interaction ability.

This paper aims to propose an intuitive shared control strategy to control a humanoid manipulator that can fully combine the advantages of humans and machines to produce a stronger intelligent form.

The working space of an operator’s arm and that of a manipulator are matched, and a genetic algorithm that limits the position of the manipulator’s elbow joint is used to find the optimal solution. Then, the mapping of the operator’s action to that of manipulators is realized. The controls of the human and robot are integrated. First, the current action of the operator is input. Second, the target object is predicted according to the maximum entropy hypothesis. Third, the joint angle of the manipulator is interpolated based on time. Finally, the confidence and weight of the current moment are calculated.

The modified weight adjustment method is the optimal way to adjust the weight during the task. In terms of time and accuracy, the experimental results of single target obstacle avoidance grabbing and multi-target predictive grabbing show that the shared control mode can provide full play to the advantages of humans and robots to accomplish the target task faster and more accurately than the control merely by a human or robot on its own.

A flexible and highly anthropomorphic human–robot action mapping method is proposed, which provides operator decisions in the shared control process. The shared control between human and the robot is realized, and it enhances the rapidity and intelligence, paving a new way for a novel human–robot collaboration.