This article proposes a unified task trajectory planner (UTTP) that integrates motion observation, trajectory planning and obstacle avoidance function, which is a key technology for dynamic target operations. The high-order motion observer (HOMO) for capturing target motion information, and the virtual impedance model for noise reduction and obstacle avoidance are two cores of UTTP. First, in HOMO, an exploratory model and optimal policy iteration method are established based on dynamics principles. Guided by the optimal policy, the exploratory model simulates the target's dynamic behavior and outputs pose, velocity, and acceleration in real-time. Second, a controller is designed based on the high-order motion information observed by HOMO and the positions of obstacles, driving the virtual impedance model to produce safe and smooth task trajectories. The introduction of UTTP can effectively enhance the robot's autonomous decision-making capabilities in dynamic environments. Finally, simulation analyses and experiments on a hyperredundant humanoid upper-limb robot support the effectiveness and superiority of the proposed algorithm in terms of accuracy and smoothness.

This article applies high-order differential estimation to the motion planning of humanoid robots for the first time. A multiobjective optimization model and the corresponding optimal policy are designed from the perspective of solving time-varying linear equations. This method can avoid the calculation of the Jacobian matrix pseudo-inverse and its derivative, reduce energy consumption, and achieve smooth human-like robot motions. High-order differential estimation is realized by cascading multiple integration-enhanced differentiators, which estimate the first derivative based on hybrid error and quasi-sliding mode techniques. The merits of the differentiator include high accuracy in estimating high-order derivatives and the elimination of chattering. Theoretical analyses verify that the proposed differentiator and the differentiator-based solver have asymptotic convergence. Simulations prove that the integration-enhanced differentiator and the differentiator-based method have excellent performance. Experiments illustrate that the designed solver for the motion planning of a humanoid upper-body robot can track desired trajectories and perform carrying tasks.

This paper presents a new variable stiffness actuator based on a second-order lever mechanism which has wide stiffness regulation range. By employing a novel symmetric structure design and improving the load capacity of the stiffness regulation module, the proposed actuator also shows well performance in load capacity, stiffness regulation response, and elastic hysteresis. On this basis, a variable stiffness actuated manipulator is developed. The experimental results demonstrate that the presented manipulator possesses abilities in fast stiffness tracking, shock-absorbing and explosive movement. It is also verified that the manipulator can withstand accidental impact, which illustrates the structure stability of the proposed design.

This paper presents a real-time kinematic control strategy to realize fast tracking of redundant robot manipulators in a time-varying environment. An obstacle avoidance method based on the law of conservation of energy is proposed to adjust the motion states of robot manipulators in real time. This method defines that the total energy for the end effector consists of an energy toward object (ETO) and an energy around obstacle (EAO), and that the total energy for each critical point on manipulator composes a relative kinematic energy (RKE) and an energy memory (EM). The total energies remain constant at each sampling period, and the conversions between the ETO and the EAO or between the RKE and the EM are recognized to obey a distance-related S-function. Such considerations ensure the smooth movement of the manipulator and avoid collisions with obstacles. In real-time planning, an unsupervised single neuron PID model is raised to adaptively increase the convergence ratio of moving object tracking via the online learning of the principal component analysis. Then, combined with the dynamic obstacle avoidance method based on conservation of energy, the kinematic control strategy is established for redundant manipulators to track a moving object rapidly in the presence of multiple dynamic obstacles. Theory analysis and various contrast experimental results show that the proposed kinematic control strategy is feasible and has fast convergence.

This article presents an approach and a systematic design methodology to path following control based on motion decoupling for high-performance ducted fan unmanned aerial vehicles (UAVs). The decoupling is performed according to the principle of regarding the attitude motion as a virtual input of the lateral longitudinal flight dynamics. This allows the attitude and flight controllers to be designed individually without mutual interference. Considering that dynamics of the ducted fan UAV is uncertain, an estimation method of system function based on the smooth saturation function and the state variable integral is proposed. This method has the advantage of less computation while keeping the high estimation performance. The stability analysis and the simulation results showing the practical feasibility of the proposed control scheme to ducted fan UAVs are given.

This paper presented a novel compliant actuator used for lower limb exoskeletons. The compliant joint consists of a series elastic actuator (SEA) and parallel elastic (PE) unit. SEA has various advantages as the actuator of assistive exoskeletons, such as low output impedance, impact absorption, precise force control and high stability. We designed and fabricated a novel SEA as the primary joint actuator which is compact, adjustable and low-cost. Meanwhile an additional elastic unit is installed in parallel with the SEA to improve energy utilization by storing and releasing energy during motion cycles. An adaptive stable controller is designed to realize the joint following motion to a virtual limb. The algorithm can identify and compensate the undetermined contact stiffness between the joint output and the virtual limb. Finally, the performance of the actuator is evaluated through motion tracking and energy-conservation experiments. Preliminary results indicate the validity of the design and imply its potential usage in lower limb exoskeletons.

This article presents the nonlinear dynamics and the posture stabilization control scheme for the single-wheeled pendulum robot (SWPR). Considering the maneuverability of SWPR, the steering is realized through the control for the inertia pendulum (IP) installed horizontally on the middle part of robot body. The feature of the control system modeling consists in a technique for which the posture stabilization control design is based on the parameterization of the dynamic interactions (DIT) between the lateral dynamics, the longitudinal dynamics, and the rotational dynamics. Simulation results showed the feasibility of the SWPR model and the control algorithm.