The grasping of transparent objects is challenging but of significance to robots. In this article, a visual–tactile fusion framework for transparent object grasping in complex backgrounds is proposed, which synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. First, we propose a multiscene synthetic grasping dataset named SimTrans12 K together with a Gaussian-mask annotation method. Next, based on the TaTa gripper, we propose a grasping network named transparent object-grasping convolutional neural network for grasping position detection, which shows good performance in both synthetic and real scenes. Inspired by human grasping, a tactile calibration method and a visual–tactile fusion classification method are designed, which improve the grasping success rate by 36.7% compared with direct grasping and the classification accuracy by 39.1%. Furthermore, the tactile height sensing module and the tactile position exploration module are added to solve the problem of grasping transparent objects in irregular and visually undetectable scenes. The experimental results demonstrate the validity of the framework.

Output redefinition based dynamic inversion (ORDI) control is proposed for a nonminimum phase hypersonic vehicle. When velocity and altitude are selected as control outputs, a hypersonic vehicle exhibits nonminimum phase behavior, preventing the application of standard dynamic inversion due to the unstable zero dynamics. This problem is solved by the ORDI control architecture, where output redefinition is utilized at first to render the modified zero dynamics stable, and then dynamic inversion is used to stabilize the new external dynamics. Three kinds of ORDI controllers with different choices of new control output are investigated. The first takes the internal variable as the control output, which exhibits good robustness but with restricted performance. The second utilizes a synthetic output, which is a linear combination of the system output and internal variable, making the zero dynamics adjustable, and thus improves the tracking performance. The third adds an integral item to the synthetic output, and thus ensures zero steady-state error even with model uncertainties. A systematic way is proposed to determine the combination coefficient to achieve zero dynamics assignment by using the root locus method. The efficiency of the method is illustrated by numerical simulations.

The attitude control of an underactuated reusable launch vehicle (RLV) in the reentry phase involving nonminimum phase problem and control input constraints is investigated in this article. To address the nonminimum phase problem, an approach combining output redefinition and robust backstepping is proposed, where a synthetic output is constructed using the combination of the original output and the internal states to obtain stable zero dynamics, and then robust backstepping is performed on the new output. Besides, the ideal internal dynamics are obtained by using optimal bounded inversion, which are incorporated into the controller as the reference trajectories for the internal states to improve the output tracking accuracy. To cope with the control input constraints, a simple and useful anti-windup strategy is proposed by using feedback error clipping, which is shown to be very effective in mitigating control input saturation. Numerical simulations are given to validate the effectiveness of the proposed method.

To realize the exquisite interaction and precise manipulation for the robot, in this article, we propose a multispectral multimodal visuotactile sensor named M $^{3}$ Tac, which combines visible, near-infrared, and mid-infrared imaging technologies for the first time and can exceed the sensing ability of human skin in terms of resolution (719 pixels/cm $^{2}$ ), temperature sensing range (-20 $\sim$ 130 $^\text{o}$ C), etc. The M $^{3}$ Tac can not only realize high-quality sensing of deformation, texture, force, stickiness, and temperature comparable to human skin but also can realize proximity sensing that is lacking for human skin. To achieve this, we not only design a multispectral imaging system with an elastic film whose light penetrability can be regulated by the brightness of the light, but also develop corresponding algorithms, including the pixel-level force sensing with finite element method (accuracy: $\pm$ 0.023 N), the proximity perception (accuracy: $\pm$ 3.8 mm), the 3D reconstruction (accuracy: 0.33 mm), the super-resolution temperature sensing (accuracy: $\pm$ 0.3 $^\text{o}$ C), the multimodal fusion classification (accuracy: 98%), and the stickiness recognition (accuracy: 98%). Finally, we conduct experiments to verify the effectiveness and application potential of our research. This paper has supplementary material available at https://sites.google.com/view/MTac-sensor .

The precision output tracking problem of multi-input-multi-output (MIMO) uncertain nonlinear nonminimum phase systems is investigated in this paper. The challenge lies in the difficulty in solving the ideal internal dynamics (IID), which is a bounded solution for the uncertain zero dynamics. First, the experience replay technique is applied to identify the uncertain parameters in the zero dynamics. It uses the recorded past data concurrently with current data which can greatly speed up the identification convergence. With the identified parameters, a novel approach called optimal bounded inversion is proposed to obtain the IID by solving a trajectory optimization problem using GPOPS-II. The boundedness of the IID is guaranteed by setting state constraints and the feasibility is achieved by minimizing the initial condition mismatch. Moreover, a piecewise IID updating scheme is adopted to reduce the computational burden. The benchmark nonminimum phase system, a vertical take-off and landing (VTOL) aircraft is used to validate the effectiveness of the proposed method.