The problem of learning-based control for robots has been extensively studied, whereas the security issue under malicious adversaries has not been paid much attention to. Malicious adversaries can invade intelligent devices and communication networks used in robots, causing incidents, achieving illegal objectives, and even injuring people. This article first investigates the problems of optimal false data injection attack scheduling and countermeasure design for car-like robots in the framework of deep reinforcement learning. Using a state-of-the-art deep reinforcement learning approach, an optimal false data injection attack scheme is proposed to deteriorate the tracking performance of a robot, guaranteeing the tradeoff between the attack efficiency and the limited attack energy. Then, an optimal tracking control strategy is learned to mitigate attacks and recover the tracking performance. More importantly, a theoretical stability guarantee of a robot using the learning-based secure control scheme is achieved. Both simulated and real-world experiments are conducted to show the effectiveness of the proposed schemes.

This paper presents a method based on frequency shift feedback for living cell manipulation and in situ nano-injection with a cantilevered micropipette probe (CMP). This method can detect tip-cell interactive forces at the piconewton level by measuring the frequency shift of the rigid CMP oscillated in the first bending eigenmode (amplitude: ~10 nm). Different interaction states throughout the process of manipulation and injection, including the contact, gripping, detaching, and cell (nuclear) membrane penetration, can be well detected and thereby controlled. In addition, the cell adhesion can be quantified by the integral of the frequency shift during the detachment process. Manipulation and nanoinjection are automatically performed using two types of CMPs with different apex aperture diameters (manipulation: Ø4 μm; nanoinjection: Ø200 nm), under the control of the dynamic force and microscope vision feedback. The proposed method can control the contact force of ~ 300 pn for nondestructive cell manipulation and can detect cell membrane (~250 pn) and nuclear membrane (~400 pn) penetration forces in nanoinjection. The versatility and robustness of the proposed method are further demonstrated by quantifying the cellsubstrate adhesion, the building of cell patterns, and automated cell nanoinjection.

High-precision positioning is an essential requirement for sample operation at a small scale. At the current stage, although nanometer-scale accuracy has been achieved for the linear positioning, the rotational positioning (attitude control) is still very challenging and rarely addressed. This paper presents a rotatable nanorobotic system with rotational degrees of freedom first. Then, the system error, i.e., nonaxisymmetrical eccentricity error of the mechanism, is investigated dynamically and its fault model is established. After that, a double-loop servo repetitive controller is accordingly designed based on the circle interpolation strategy. The theoretical analysis and experimental results verify that the rotational positioning accuracy can be controlled up to submicrometers stably, which improves at least one order of magnitude than the current static method. Finally, two application cases are given to highlight the significance of this approach, i.e., surface defect detection from 360° and in situ twisting characterization of 1-D micro/nanomaterial. This research paves a new avenue for the ultrahigh rotational positioning at microscopy environment, which is expected to generate a long-term impact on the micro/nanofields, such as microscopy imaging, material characterization, and so on.

To understand robotic grasping at the nanoscale, contact mechanics between nano grippers and nano samples was studied. Contact mechanics models were introduced to simulate elastic contacts between various profiles of flat surface, sphere and cylinder for different types of nano samples and nano grippers. Analyses and evaluation instances indicate that friction forces, commonly used in macro grasping to overcome the gravity, at nanoscale is often not enough to overcome relatively strong adhesion forces to pick up the nano sample deposited on a substrate due to tiny contact area of the grasping. Two-finger grippers are proposed for the stable nanoscale grasping and a nonparallel gripper with a 'V' configuration was demonstrated with better grasping capabilities than a parallel one. To achieve the robotic nanoscale grasping, a nano gripper constructed from two individually actuated and sensed tips is presented. Pick-and-place manipulation of silicon nanowires validate the theoretical analyses and capabilities of the proposed nano gripper.

In this paper, a fully teleoperated 3D micro assembly task with haptic feedback is presented. Microspheres (diameter: 4-6μm) are manipulated by pick-and-place. The setup is composed of a dual-tip gripper controlled through a haptic interface. To grasp the spheres, the tips must be correctly positioned with respect to the objects. The approach proposed to align the gripper is based on a user-driven exploration of the to-be-manipulated object. During this step, the haptic feedback is based on amplitude measurements from cantilevers in dynamic mode. Hence, the operator perceives the contact while freely exploring the manipulation area. A virtual guide is generated to pull the user to the optimum contact point, allowing correct positioning of dual tips. For the pick-and-place operation, the haptic feedback provides the user with information about the microscale interactions occurring during the operation. As experimental validation, a two-layer pyramid composed of four nylon microspheres is built in ambient conditions.

Applications of the conventional atomic force microscope (AFM) succeeded in manipulating nanoparticles, nanowires or nanotubes by widely used pushing or pulling operations on a single plane. However, pick-and-place nanomanipulation is still a challenge in the air. In this paper, a modified AFM, called three-dimensional (3D) manipulation force microscope (3DMFM) was developed, aiming to achieve the pick-and-place in the air. This system mainly consists of two microcantilevers and each is quipped with a nanopositioning device and an optical lever, constructing a nanotweezer with capabilities of picking and releasing nanoobjects with force sensing. Before the 3D manipulation, one of the cantilevers is employed to position nanoobjects and locate the tip of another cantilever by image scanning, then these two cantilevers fit together as a nanotweezer to grasp, transport and place the nanoobjects with real-time force sensing. In pick-and-place experiments, silicon nanowires (SiNMs) with different diameters were manipulated and 3D nanowire crosses were achieved. 3D nanomanipulation and nanoassembly in the air could become feasible through the newly developed 3DMFM.

Three-dimensional (3-D) automated micromanipulation at scale of several micrometers using a nanotip gripper is presented. The gripper is constructed from protrudent tips of two independently actuated atomic force microscope (AFM) cantilevers and each cantilever. A protocol allows these two cantilevers to form a gripper for grasping and releasing the microspheres to target positions without obstacle of adhesive forces in air. For grasping, amplitude feedback from the dithering cantilevers is employed to locate the grasping points by laterally scanning the side of the microspheres. Real time force sensing is used to monitor the whole process of the pick-and-place with steps of pickup, transport and release. For trajectory planning, an algorithm based on the shortest path solution is used to obtained 3-D micropatterns with high efficiencies. In experiments, microspheres with diameters from 3 ¿m to 4 ¿m were manipulated and 3-D micropyramids with two layers were achieved. 3-D micromanipulation and 3-D microassembly at the scale of several microns to submicron could become feasible through the newly developed nanotip gripper.

Both the extent and accuracy of force application in atomic force microscope (AFM) nanomanipulation are significantly limited by the nonlinearity of the commonly used optical lever with a nonlinear position-sensitive detector (PSD). In order to compensate the nonlinearity of the optical lever, a nonlinear calibration method is presented. This method applies the nonlinear curve fit to a full-range position-voltage response of the photodiode, obtaining a continuous function of its voltage-related sensitivity. Thus, Interaction forces can be defined as integrals of this sensitivity function between any two responses of photodiode voltage outputs, instead of rough transformation with a single conversion factor. The lateral position-voltage response of the photodiode, a universally acknowledged puzzle, was directly characterized by an accurately calibrated force sensor composed of a tippless piezoresistive force sensor, regardless of any knowledge of the cantilevers and laser measuring system. Experiments using a rectangular cantilever (normal force constant 0.24 N/m) demonstrated that the proposed nonlinear calibration method restrained the sensitivity error of normal position-voltage responses to 3.6% and extended the force application range.

Batch microassembly of MEMS sensors is limited by the manual manipulation required specially trained technicians. To reduce the production costs and simultaneously obtain high production quality, a flexible microassembly system for automated bonding of MEMS sensors is developed. The system consists of a set of autonomous modules that can adapt their structures and functions to various sizes of MEMS sensors, including positioning stages, a microscopy imaging system, a flexible micromanipulator, a heater, a fixture, a supply station and auxiliary systems. Optomechatronic design is essential to the development of integrated systems due to the basic importance of microscope optics to microassembly. In this paper, major methodology issues in optomechatronic design of this system are introduced. A wavelet-based microscopic focus measure and a control scheme with a modified Smith predicator to decrease the inherent time delay of vision system are presented. A smart force sensor with one dimension is employed to sense and control the interactive force. To perform manipulations automatically, a control system, including a task planning level and a real-time execution level, is developed. The productivity of the flexible microassembly system is validated by further experiments

Microscopy imaging can not achieve both high resolution and wide image space simultaneously. Image fusion and 3-D surface reconstruction is of fundamental importance to micromanipulation in providing high lever task understanding, task planning and real time control. Purpose of image fusion is to combine those images into one single image with an extended depth-of-field. Previously, real valued wavelet transforms based image fusion suffers from shift variance, poor directionality, and lack of phase information. One promising fusion method based on complex wavelet transforms to overcome these problems is proposed. Combined 2-D position data of "in focus" pixels with a height map obtained from the proposed image fusion method, a 3-D surface reconstruction algorithm of microparts is developed. Experimental results validate performances of the proposed image fusion and 3-D surface reconstruction methods.

Microscopy imaging can not achieve both high resolution and wide image space simultaneously. Autofocusing and 3-D surface reconstruction techniques are of fundamental importance to automated micromanipulation in providing high lever task understanding, task planning and real time control. In this paper, a new wavelet-based focus measure is developed, which provides significantly better depth resolution accuracy, and robustness than previous ones. A complex valued wavelet-based microscopic image fusion method and 3-D surface reconstruction scheme were proposed. Purpose of image fusion is to combine those multi-focus images into one single clear composite image with an extended depth-of-field. Combined 2-D position data of "in focus" pixels with a height map obtained from the proposed image fusion method, a 3-D surface reconstruction algorithm of microparts is developed. Experimental results validate the performances of the proposed image fusion and 3-D surface reconstruction methods

This paper presents a Task-reconfigurable system developed for efficient and reliable MEMS assembly. The system consists of a set of autonomous modules that can adapt their shape and function to various assembly tasks, including a distributed 6 DOF coarse positioning module, a distributed 5 DOF fine positioning module, grippers designed for different manipulation tasks with same mechanical interface, a multi-view imaging system, and control software. In order to achieve high precision and dexterity in microassembly, a hybrid vision-force control method is proposed. For easier reconfiguration, a semi-automated calibration method is applied to quick calibrating of the new reconfigurations. Analysis and 3 cases of microassembly are presented to demonstrate that this system has high reconfigurability and adaptability.

This paper presents the automatic microassembly of miniaturized gear system by hybrid vision-force control with the vision system and the 3-DOF force sensor feedback data. The assembly process consists of three phases, including visual positioning, searching meshing state, and inserting. Visual feedback is used to achieve the coarse positioning and guide the grasping and transporting of microparts during the visual positioning phase. During the searching phase, a fuzzy PID controller is used to control the contact force on z axis and fuzzy logic strategy is developed to search the meshing state by the force feedback on x and y axes. The tolerance compensations movement is used to support the inserting task to avoid the case of blocking and prevent the micro parts from damaging. The assembly experiment of three planetary gears validates the hybrid force control strategy.